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Risk management | 12 Months Ended
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Dec. 31, 2023
Risk management [abstract] |
Risk management |
Basis of disclosures (*) The risk management section contains information relating to the nature and extent of the risks of financial instruments as required by International Financial Reporting Standards (IFRS) 7 'Financial Instruments: Disclosures'. These disclosures are an integral part of ING Group Consolidated financial statements and are indicated by the symbol (*). This is applicable for the chapters, paragraphs, graphs or tables within the risk management section that are indicated with this symbol in the respective headings or table header. This risk management section includes additional disclosures beyond those required by IFRS standards, such as certain legal and regulatory disclosures. Not all information in this section can be reconciled back to the primary financial statements and corresponding notes, as it has been prepared using risk data that differs to the accounting basis of measurement. Disclosures in accordance with Part Eight of the CRR2 and CRD V, and as required by the supervisory authority, are published in our ‘Additional Pillar III Report’, which can be found on our corporate website ing.com.
Governance (*) ING’s credit risk strategy is to maintain an internationally diversified loan and bond portfolio, while avoiding large risk concentrations. The emphasis is on managing business developments within the defined credit and concentration risk appetite. The aim is to support relationship banking activities, while maintaining internal risk/reward guidelines and controls. The Credit Risk department is responsible for setting the credit risk strategy for ING and aims to ensure credit risk and credit restructuring are managed from an overarching point of view rather than per business line. Furthermore, the Credit Risk Control Unit is responsible for the oversight and control of rating systems. While the Credit Risk department has oversight of the Group credit risk strategy and risk appetite across Retail Banking risk and Wholesale Banking risk, the head of Retail/Rest of World (RoW) Risk and head of Wholesale Banking Risk aim to ensure the management of the risk within these business lines. Also refer to the Risk governance and organisational structure in the introductory section of 'Risk management'. The Credit Risk function encompasses the following activities: •Providing consistent credit risk policies, systems and tools to manage the credit lifecycle of all activities; •Measuring, monitoring and managing credit risks in the bank’s portfolio, including evaluation of scenario and stress test results; •Challenging and approving new and modified transactions and borrower reviews, including involvement in the process of assigning risk ratings to indicate a clients's creditworthiness; •Managing the levels of provisioning and risk costs, and advising on impairments. Credit risk categories (*) In the following table the different types of credit risk categories are described and a reconciliation with the notes in the financial statements is also included:
Reconciliation between credit risk categories and financial position (*)
Credit risk categories | Notes in the financial statements
Lending risk: is the risk that the client (counterparty, corporate or individual) does not pay the principal interest or fees on a loan when they are due, or on demand for letters of credit (LCs) and guarantees provided by ING. | Note | 2 | Cash and balances with central banks
Note | 3 | Loans and advances to banks
Note | 4 | Financial assets at fair value through profit or loss
Note | 5 | Financial assets at fair value through other comprehensive income
Note | 7 | Loans and advances to customers
Note | 41 | Contingent liabilities and commitments
Investment risk: is the credit default and risk rating migration risk that is associated with ING’s investments in bonds, commercial paper, equities, securitisations and other similar publicly traded securities. This can be viewed as the potential loss that ING may incur from holding a position in underlying securities whose issuer's credit quality deteriorates or defaults. | Note | 4 | Financial assets at fair value through profit or loss
Note | 5 | Financial assets at fair value through other comprehensive income
Note | 6 | Debt securities
Money market risk: arises when ING places short-term deposits with a counterparty in order to manage excess liquidity. In the event of a counterparty default, ING may lose the deposit placed. | Note | 2 | Cash and balances with central banks
Note | 3 | Loans and advances to banks
Note | 7 | Loans and advances to customers
Pre-settlement risk: arises when a client defaults on a transaction before settlement and ING must replace the contract by a trade with another counterparty at the then prevailing (possibly unfavourable) market price. This credit risk category is associated with derivatives transactions (exchange-traded derivatives, over- the-counter (OTC) derivatives and securities financing transactions). | Note | 4 | Financial assets at fair value through profit or loss
Note | 14 | Financial liabilities at fair value through profit or loss
Note | 40 | Offsetting financial assets and liabilities
Settlement risk: arises when there is an exchange of value (funds or instruments) and receipt from its counterparty is not verified or expected until after ING has given irrevocable instructions to pay or has paid or delivered its side of the trade. The risk is that ING delivers but does not receive delivery from its counterparty. | Note | 4 | Financial assets at fair value through profit or loss
Note | 11 | Other assets
Note | 14 | Financial liabilities at fair value through profit or loss
Note | 16 | Other liabilities
Credit risk appetite and concentration risk framework (*) The credit risk appetite and concentration risk framework is designed to prevent undesired high levels of credit risk and credit concentrations within various levels of the ING portfolio. It is derived from the concepts of boundaries and instruments as described in the ING Risk Appetite Framework (RAF). Credit risk appetite is the maximum level of credit risk ING is willing to accept for growth and value creation. The credit risk appetite is linked to the overall bank-wide RAF. The credit risk appetite is expressed in quantitative and qualitative measures. Having a credit risk appetite provides: •Clarity about the credit risks that ING is prepared to assume, target setting and prudent risk management. Credit risk appetite is used as an input for Lending guidances which are made by sector and/or location; •Consistent communication to different stakeholders; •Guidelines on how to align reporting and monitoring tools with the organisational structure and strategy; •Alignment of business strategies and key performance indicators of business units with ING’s credit risk appetite through dynamic planning. The credit risk appetite is set at different levels and dimensions within ING. The credit risk appetite framework specifies the scope and focus of the credit risk which ING takes and the composition of the credit portfolio, including its concentration and diversification objectives in relation to business lines, locations, sectors and products. The credit risk appetite framework has also been extended to embed climate risk elements. The first steps towards introducing climate risk elements in the credit risk appetite framework were taken in 2022 and these have further evolved and matured in 2023. The climate risk elements within the credit risk appetite framework allow for more efficient steering of sector concentrations from a climate risk perspective. The credit concentration risk framework is composed of: •Country risk concentration: Country risk is the risk that arises due to events in a specific country (or group of countries). To manage the maximum country loss ING is willing to accept, boundaries are approved by the SB. The estimated level is correlated to the risk rating assigned to a given country. Actual country limits are set by means of country instruments, which are monitored monthly and updated, when needed. For countries with elevated levels of geopolitical or severe economic cycle risk, monitoring is performed on a more frequent basis with strict pipeline and exposure management. •Single name and secondary risk concentration: ING has an established credit concentration risk framework to identify, measure and monitor single name concentration including secondary risk. The same concept of boundaries and instruments is applicable. •Sector and product concentration risk are managed via the credit risk appetite framework. Credit risk models (*) Within ING, internal CRR-compliant models are used to determine probability of default (PD), exposure at default (EAD) and loss given default (LGD) for regulatory and economic capital purposes. These models also form the basis of ING’s IFRS 9 loan loss provisioning (see ‘IFRS 9 models’ below). Bank-wide, ING has implemented approximately 100 credit risk models, for regulatory capital, economic capital and loan loss provisioning purposes. There are two main types of PD, EAD and LGD models used throughout the bank: •Statistical models are created where a large set of default or detailed loss data is available. They are characterised by sufficient data points to facilitate meaningful statistical estimation of the model parameters. The model parameters are estimated with statistical techniques based on the data set available. •Hybrid models are statistical models supplemented with knowledge and experience of experts from risk management and front-office staff, literature from rating agencies, supervisors and academics. These models are only used for ‘low default portfolios’, where limited historical defaults exist. Credit risk rating process (*) The majority of risk ratings are based on a risk rating (PD) model that complies with the minimum requirements detailed in CRR/CRDIV, ECB Supervisory Rules and European Banking Authority (EBA) guidelines. This concerns all borrower types and segments. ING’s PD rating models are based on a 1-22 internal risk rating scale (1 = highest rating; 22 = lowest rating) referred to as the ‘master scale’, which roughly corresponds to the rating grades that are assigned by external rating agencies, such as Standard & Poor’s, Moody’s and Fitch. For example, an ING rating of 1 corresponds to an S&P/Fitch rating of AAA and a Moody’s rating of Aaa; an ING rating of 2 corresponds to an S&P/Fitch rating of AA+ and a Moody’s rating of Aa1, and so on. The 22 internal risk rating grades are composed of the following categories: •Investment grade (risk rating 1-10); •Non-investment grade (risk rating 11-17); •Performing Restructuring (risk rating 18-19); •Non-performing (risk rating 20-22). The first three categories (1-19) are risk ratings for performing loans. Ratings are calculated in IT systems with internally developed models, based on manually or automatically fed data, or for part of the non- performing loans set by the global or regional credit restructuring department. Under certain conditions, the outcome of a manually fed model can be challenged through a rating appeal process. For securitisation portfolios, the external ratings of the tranche in which ING has invested are leading indicators. Risk ratings assigned to clients are reviewed at least annually, with the performance of the underlying models monitored regularly. Some of these models are global in nature, such as those for large corporates, commercial banks, insurance companies, central governments, funds, fund managers, project finance and leveraged companies. Other models are more regional or country-specific: there are PD models for small and medium enterprises (SMEs) in the Netherlands, Belgium, Poland as well as residential mortgage and consumer loan models in the various retail markets. Rating models for Retail clients are predominantly statistically driven and automated, such that ratings can be updated on a monthly basis. Rating models for large corporates, institutions and banks include both statistical characteristics and manual input, with the ratings being manually updated at least annually. More frequent reviews (e.g. quarterly) are performed where considered necessary, for example portfolios and clients most at risk of being impacted by the Russian invasion of Ukraine and expected spillover effects. In line with evolving regulatory expectations on models and emerging industry practices ING has embarked a multi-year redevelopment process of its credit risk models. This is also in line with ING’s model governance to ensure continuous improvement of models. Credit risk portfolio (*) ING’s credit exposure is mainly related to lending to individuals (also referred to as consumer lending, all Retail) and businesses (referred to as business lending, both in Retail and Wholesale) followed by investments in bonds and securitised assets, and money market (Wholesale). Loans to individuals are mainly mortgage loans secured by residential property. Loans (including guarantees issued) to businesses are often collateralised, but may be unsecured based on the internal analysis of the borrower’s creditworthiness. Bonds in the investment portfolio are generally unsecured, but predominantly consist of bonds issued by central governments and EU and/or OECD-based financial institutions. Secured bonds, such as mortgage-backed securities and asset-backed securities are secured by the underlying diversified pool of assets (commercial or residential mortgages, car loans and/or other assets) held by the securities issuer. For money market, exposure is mainly deposits to central banks. The last major credit risk source involves pre- settlement exposures which arise from trading activities, including derivatives, repurchase transactions and securities lending/borrowing transactions. This is also commonly referred to as counterparty credit risk. Portfolio analysis per business line (*)
Outstandings per line of business (*)1, 2, 3
in € million | Wholesale Banking | Retail Benelux | Retail Challengers & Growth Markets | Corporate Line | Total
Rating class | 2023 | 2022 | 2023 | 2022 | 2023 | 2022 | 2023 | 2022 | 2023 | 2022
Investment grade | 1 (AAA) | 52,665 | 89,686 | 310 | 324 | 34,373 | 32,492 | 2,284 | 2,529 | 89,631 | 125,032
2-4 (AA) | 67,034 | 49,320 | 7,089 | 7,871 | 52,566 | 40,498 | 6 | 12 | 126,694 | 97,701
5-7 (A) | 95,320 | 79,292 | 44,026 | 45,471 | 88,051 | 61,422 | 147 | 320 | 227,543 | 186,504
8-10 (BBB) | 123,081 | 129,709 | 118,340 | 117,172 | 27,955 | 56,046 | 2,357 | 2,833 | 271,733 | 305,760
Non-Investment grade | 11-13 (BB) | 57,348 | 56,409 | 57,652 | 55,945 | 36,756 | 46,657 |  | 4 | 151,756 | 159,016
14-16 (B) | 12,234 | 13,693 | 16,872 | 14,224 | 12,459 | 11,662 |  |  | 41,565 | 39,579
17 (CCC) | 1,122 | 1,858 | 2,129 | 2,021 | 985 | 1,014 | 392 | 299 | 4,628 | 5,192
Performing Restructuring loans | 18 (CC) | 2,523 | 3,564 | 1,363 | 1,304 | 594 | 519 |  |  | 4,481 | 5,386
19 (C) | 535 | 731 | 876 | 962 | 437 | 490 |  |  | 1,848 | 2,183
Non-performing loans | 20-22 (D) | 4,051 | 4,354 | 4,586 | 4,762 | 3,036 | 2,592 |  |  | 11,673 | 11,708
Total | 415,914 | 428,616 | 253,241 | 250,056 | 257,211 | 253,391 | 5,186 | 5,997 | 931,552 | 938,061
Industry |  |  |  |  |  |  |  |  |  |  |
Private Individuals |  | 2,330 | 32 | 165,447 | 163,243 | 193,610 | 191,556 |  |  | 361,387 | 354,831
Central Banks |  | 70,139 | 80,006 |  |  | 21,740 | 23,541 | 2,269 | 1,495 | 94,147 | 105,043
Natural Resources |  | 40,511 | 44,695 | 1,259 | 1,160 | 624 | 694 |  |  | 42,394 | 46,549
Real Estate |  | 24,904 | 26,426 | 23,675 | 22,648 | 2,936 | 3,439 |  |  | 51,515 | 52,513
Commercial Banks |  | 37,342 | 42,036 | 177 | 194 | 6,006 | 5,721 | 2,515 | 2,911 | 46,040 | 50,862
Non-Bank Financial Institutions |  | 55,313 | 54,274 | 1,400 | 1,379 | 890 | 504 | 286 | 438 | 57,889 | 56,594
Central Governments |  | 45,316 | 41,622 | 2,124 | 2,880 | 5,180 | 3,838 | 1 | 1,016 | 52,621 | 49,356
Transportation & Logistics |  | 27,106 | 25,474 | 4,105 | 4,038 | 1,679 | 1,471 |  |  | 32,890 | 30,982
Utilities |  | 23,324 | 22,683 | 2,024 | 1,865 | 160 | 150 |  |  | 25,509 | 24,698
Food, Beverages & Personal Care |  | 13,503 | 13,681 | 7,307 | 7,356 | 2,576 | 2,585 |  |  | 23,386 | 23,623
Services |  | 9,128 | 9,926 | 11,596 | 11,606 | 1,276 | 981 | 24 | 33 | 22,023 | 22,546
General Industries |  | 12,039 | 11,731 | 5,680 | 5,753 | 3,406 | 3,381 |  |  | 21,126 | 20,865
Lower Public Administration |  | 6,211 | 6,020 | 6,885 | 5,921 | 10,608 | 9,725 |  |  | 23,704 | 21,666
Other | 48,748 | 50,009 | 21,563 | 22,014 | 6,519 | 5,805 | 92 | 104 | 76,922 | 77,932
Total | 415,914 | 428,616 | 253,241 | 250,056 | 257,211 | 253,391 | 5,186 | 5,997 | 931,552 | 938,061
Outstandings per line of business (*) - continued 1, 2, 3
in € million | Wholesale Banking | Retail Benelux | Retail Challengers & Growth Markets | Corporate Line | Total
Region | 2023 | 2022 | 2023 | 2022 | 2023 | 2022 | 2023 | 2022 | 2023 | 2022
Europe | Netherlands | 54,938 | 61,143 | 155,792 | 154,253 | 390 | 254 | 2,366 | 2,898 | 213,486 | 218,548
Belgium | 24,171 | 27,144 | 90,450 | 88,767 | 1,294 | 669 | 7 |  | 115,921 | 116,580
Germany | 26,152 | 24,441 | 478 | 463 | 128,407 | 127,764 | 31 | 63 | 155,067 | 152,730
Poland | 20,346 | 16,350 | 9 | 49 | 28,962 | 26,831 |  |  | 49,317 | 43,229
Spain | 11,047 | 10,491 | 109 | 71 | 27,049 | 25,649 | 35 | 25 | 38,240 | 36,237
United Kingdom | 28,587 | 27,735 | 150 | 152 | 125 | 185 | 112 | 107 | 28,974 | 28,179
Luxembourg | 23,805 | 26,113 | 4,880 | 4,953 | 677 | 639 |  | 15 | 29,363 | 31,720
France | 21,528 | 18,484 | 698 | 643 | 2,410 | 4,448 | 14 | 1 | 24,650 | 23,576
Rest of Europe | 65,157 | 77,814 | 367 | 400 | 20,001 | 18,750 | 32 | 24 | 85,558 | 96,989
America | 78,851 | 80,444 | 201 | 190 | 1,841 | 1,795 | 222 | 358 | 81,114 | 82,786
Asia | 49,851 | 46,291 | 69 | 73 | 90 | 121 | 2,365 | 2,504 | 52,374 | 48,989
Australia | 9,409 | 9,817 | 14 | 16 | 45,963 | 46,281 | 2 | 2 | 55,389 | 56,116
Africa | 2,071 | 2,348 | 24 | 28 | 2 | 5 |  |  | 2,098 | 2,381
Total | 415,914 | 428,616 | 253,241 | 250,056 | 257,211 | 253,391 | 5,186 | 5,997 | 931,552 | 938,061
1 Based on credit risk measurement contained in lending, pre-settlement, money market and investment activities.
2 Based on the total amount of credit risk in the respective column using ING’s internal credit risk measurement methodologies. Economic sectors (industry) below 2% are not shown separately but grouped in Other.
3 Geographical areas are based on country of residence, except for private individuals for which the geographical areas are based on the primary country of risk.
Overall portfolio (*) During 2023, ING’s portfolio size decreased by €6.5 billion (0.69%) to €931.6 billion outstanding. Foreign exchange rate changes had a negative impact on the portfolio growth, mainly in WB, decreasing total outstanding by €4.6 billion, driven by the depreciation of the US dollar (-3.8%) against the euro, Australian dollar (-3.4%) and Turkish lira (-42.1%), partly compensated by the Polish zloty (+6.8%). Rating distribution (*) Overall, the rating class distribution remained stable in 2023. The share of investment grade rating classes increased from 76.2% to 76.8%, while the share of non-investment grade decreased from 21.7% to 21.2%. Performing restructuring outstandings decreased from 0.8% to 0.7% of the total portfolio, whereas non- performing loans increased from 1.2% to 1.3%. The decrease in AAA and the increase in AA was mainly due to lower Central banks/Central Governments exposure in AAA-rated countries, partly driven by the downgrade of the USA to AA rating. With respect to the rating distribution within the business lines, in WB, investment grade remained at 81.2%, where non-investment grade exposures increased to 17.1% (from 16.8%) compared to 2022. Performing Restructuring assets decreased from 1.0% to 0.7% of total Wholesale Banking assets. The share of non- performing loans for WB remained stable at 1.0%. The non-investment grade portfolio of Retail Benelux increased from 28.9% to 30.3% of the portfolio, which is explained primarily by rating migration in mortgages in Belgium. Performing restructuring remained flat at 0.9% whereas NPL improved to 1.8% (from 1.9%) in 2023. In Retail Challengers & Growth Markets, the distribution across rating classes remained stable in 2023. Overall share of investment grade increased from 75.2% to 78.9%. NPL increased to 1.2% (from 1.0%). Industry (*) The industry breakdown is presented in accordance with the NAICS definition. Total volume decreased in 2023 from €938.1 billion to €931.6 billion (-0.7%), mainly witnessed in Netherlands (-2.3%) and in Luxembourg (-7.4%) due to a decrease in outstanding at Central Banks. The largest part of our book in terms of outstandings is in private individuals with 38.8% (2022: 37.8%). Private individuals accounted for 67% outstanding in Germany, 67% in Spain, 66% Australia and 55% in the Netherlands. The increase in WB Private individuals is following the discontinuation of Retail Banking France which led to the transfer of the Retail individuals portfolio from Retail Banking to Wholesale Banking. Looking at sectors in the Business Lending portfolio, the most notable decrease in outstanding, next to Central Banks, was in Natural Resources (-€4.2 billion).
Outstandings by economic sectors and geographical area (*) 1
in € million | Region |  |  |  |  |  |  |  |  |  |  |  |  | Total
Industry | Netherlands | Belgium | Germany | Poland | Spain | United Kingdom | Luxembourg | France | Rest of Europe | America | Asia | Australia | Africa | 2023
Private Individuals | 116,530 | 44,637 | 103,151 | 14,860 | 25,452 | 128 | 3,347 | 2,472 | 14,179 | 149 | 121 | 36,340 | 20 | 361,387
Central Banks | 31,017 | 9,756 | 18,945 | 2,530 | 489 | 4,335 | 4,853 |  | 6,166 |  | 13,668 | 2,379 | 9 | 94,147
Natural Resources | 2,623 | 1,346 | 1,017 | 685 | 129 | 3,789 | 2,511 | 429 | 10,608 | 8,237 | 9,785 | 941 | 295 | 42,394
Real Estate | 16,907 | 10,986 | 1,111 | 2,184 | 1,551 | 420 | 3,563 | 2,901 | 3,492 | 3,323 | 1,367 | 3,709 |  | 51,515
Commercial Banks | 1,217 | 404 | 4,050 | 601 | 353 | 4,488 | 5,070 | 4,155 | 6,757 | 9,833 | 8,182 | 719 | 210 | 46,040
Non-Bank Financial Institutions | 2,573 | 1,457 | 5,710 | 2,532 | 652 | 6,837 | 4,631 | 4,274 | 4,269 | 20,118 | 3,884 | 950 |  | 57,889
Central Governments | 1,620 | 9,046 | 699 | 8,614 | 5,491 | 41 | 79 | 2,255 | 9,384 | 13,752 | 520 | 526 | 593 | 52,621
Transportation & Logistics | 3,860 | 2,198 | 1,277 | 1,598 | 658 | 2,113 | 596 | 784 | 8,177 | 3,511 | 7,044 | 456 | 618 | 32,890
Utilities | 2,419 | 1,634 | 3,516 | 792 | 912 | 2,723 | 480 | 619 | 4,469 | 4,424 | 1,306 | 2,041 | 173 | 25,509
Food, Beverages & Personal Care | 7,138 | 3,127 | 550 | 2,242 | 490 | 540 | 1,505 | 1,250 | 2,455 | 2,652 | 1,140 | 281 | 18 | 23,386
Services | 5,073 | 8,463 | 1,725 | 1,325 | 71 | 745 | 502 | 380 | 1,052 | 1,576 | 469 | 642 |  | 22,023
General Industries | 5,746 | 2,604 | 1,193 | 2,827 | 333 | 199 | 649 | 287 | 3,661 | 2,848 | 761 | 18 |  | 21,126
Lower Public Administration | 253 | 6,607 | 5,349 | 669 | 350 |  | 249 | 3,488 | 356 | 1,550 | 7 | 4,826 |  | 23,704
Other | 16,510 | 13,657 | 6,774 | 7,858 | 1,309 | 2,615 | 1,326 | 1,356 | 10,532 | 9,141 | 4,120 | 1,562 | 163 | 76,922
Total | 213,486 | 115,921 | 155,067 | 49,317 | 38,240 | 28,974 | 29,363 | 24,650 | 85,558 | 81,114 | 52,374 | 55,389 | 2,098 | 931,552
Rating class |  |  |  |  |  |  |  |  |  |  |  |  |  |
Investment grade | 170,067 | 71,730 | 136,675 | 31,772 | 29,583 | 24,299 | 24,083 | 18,692 | 56,404 | 63,652 | 44,481 | 44,139 | 24 | 715,602
Non-Investment grade | 40,399 | 40,236 | 16,929 | 15,785 | 8,134 | 4,508 | 5,013 | 5,713 | 25,967 | 16,003 | 6,770 | 10,715 | 1,776 | 197,949
Performing restructuring | 1,433 | 799 | 349 | 830 | 230 | 2 | 105 | 122 | 1,983 | 245 | 72 | 132 | 26 | 6,327
NPL grade | 1,587 | 3,156 | 1,114 | 929 | 293 | 165 | 162 | 124 | 1,205 | 1,213 | 1,051 | 403 | 272 | 11,673
Total | 213,486 | 115,921 | 155,067 | 49,317 | 38,240 | 28,974 | 29,363 | 24,650 | 85,558 | 81,114 | 52,374 | 55,389 | 2,098 | 931,552
1 Geographical areas are based on country of residence, except for private individuals for which the geographical areas are based on the primary country of risk.
Outstandings by economic sectors and geographical area (*) 1
in € million | Region |  |  |  |  |  |  |  |  |  |  |  |  | Total
Industry | Netherlands | Belgium | Germany | Poland | Spain | United Kingdom | Luxembourg | France | Rest of Europe | America | Asia | Australia | Africa | 2022
Private Individuals | 114,625 | 44,193 | 101,529 | 13,767 | 24,865 | 138 | 3,486 | 2,731 | 13,654 | 161 | 131 | 35,528 | 24 | 354,831
Central Banks | 35,202 | 14,338 | 21,041 | 1,060 | 347 | 427 | 6,820 | 35 | 18,092 |  | 4,962 | 2,695 | 24 | 105,043
Natural Resources | 3,084 | 1,356 | 809 | 746 | 169 | 4,442 | 2,764 | 470 | 12,154 | 8,771 | 10,398 | 1,028 | 361 | 46,549
Real Estate | 17,586 | 10,112 | 1,388 | 2,374 | 1,391 | 413 | 3,797 | 3,155 | 3,378 | 3,474 | 1,180 | 4,263 | 2 | 52,513
Commercial Banks | 1,358 | 265 | 3,974 | 551 | 402 | 4,933 | 4,480 | 4,371 | 6,368 | 9,945 | 12,041 | 1,765 | 409 | 50,862
Non-Bank Financial Institutions | 2,710 | 1,041 | 5,054 | 2,299 | 99 | 8,229 | 4,489 | 3,220 | 4,495 | 19,971 | 4,091 | 896 |  | 56,594
Central Governments | 3,342 | 7,716 | 1,179 | 6,578 | 4,578 | 46 | 175 | 1,797 | 8,444 | 13,979 | 333 | 636 | 551 | 49,356
Transportation & Logistics | 3,967 | 2,183 | 608 | 1,300 | 690 | 1,787 | 583 | 733 | 7,808 | 3,378 | 6,806 | 531 | 608 | 30,982
Utilities | 1,551 | 1,630 | 2,814 | 679 | 1,227 | 2,953 | 572 | 980 | 4,302 | 4,347 | 1,545 | 1,897 | 200 | 24,698
Food, Beverages & Personal Care | 7,249 | 3,002 | 573 | 2,334 | 475 | 739 | 1,667 | 469 | 2,668 | 3,245 | 942 | 248 | 13 | 23,623
Services | 4,819 | 8,816 | 1,254 | 1,101 | 67 | 685 | 808 | 1,066 | 1,120 | 1,821 | 357 | 632 |  | 22,546
General Industries | 5,430 | 2,689 | 1,007 | 2,849 | 311 | 330 | 604 | 245 | 3,152 | 2,926 | 1,311 | 9 |  | 20,865
Lower Public Administration | 272 | 5,638 | 5,197 | 644 | 200 |  | 313 | 3,126 | 402 | 1,310 |  | 4,564 |  | 21,666
Other | 17,353 | 13,602 | 6,302 | 6,946 | 1,416 | 3,058 | 1,164 | 1,179 | 10,952 | 9,457 | 4,891 | 1,424 | 188 | 77,932
Total | 218,548 | 116,580 | 152,730 | 43,229 | 36,237 | 28,179 | 31,720 | 23,576 | 96,989 | 82,786 | 48,989 | 56,116 | 2,381 | 938,061
Rating class |  |  |  |  |  |  |  |  |  |  |  |  |  |
Investment grade | 174,971 | 76,244 | 130,285 | 27,501 | 28,556 | 23,160 | 26,053 | 17,545 | 64,884 | 64,206 | 39,903 | 41,476 | 213 | 714,997
Non-Investment grade | 40,325 | 36,036 | 20,967 | 14,596 | 7,330 | 4,634 | 5,442 | 5,814 | 27,617 | 17,615 | 7,638 | 13,914 | 1,858 | 203,786
Performing restructuring | 1,508 | 984 | 579 | 422 | 105 | 41 | 80 | 16 | 2,903 | 311 | 124 | 257 | 239 | 7,569
Non-performing loans | 1,743 | 3,316 | 899 | 710 | 246 | 344 | 145 | 201 | 1,584 | 654 | 1,325 | 470 | 72 | 11,708
Total | 218,548 | 116,580 | 152,730 | 43,229 | 36,237 | 28,179 | 31,720 | 23,576 | 96,989 | 82,786 | 48,989 | 56,116 | 2,381 | 938,061
1 Geographical areas are based on country of residence, except for private individuals for which the geographical areas are based on the primary country of risk.
Portfolio analysis per geographical area (*) The portfolio analysis per geographical area re-emphasises the international distribution of ING’s credit portfolio. The share of the Netherlands in the overall portfolio (ex-Central Banks) reduced to 21.8% (2022: 22.0%). The most noticeable trend in the Netherlands was the decrease in exposure with central banks (-€4.2 billion). Outstandings to private individuals are at 63.9% (2022: 62.5%) of total outstandings (excl. Central Banks). In Belgium, no substantial changes were observed in the portfolio. In terms of rating distribution in individual countries, the total share of investment grade/non-investment grade remains substantial for the Netherlands at 98.6% (2022: 98.5%) and in Belgium 96.6% (2022: 96.3%). Performing restructuring grade assets remained flat in the Netherlands at 0.7%, whereas Belgium decreased from 0.8% to 0.7%. The NPL share decreased in 2023, from 0.8% to 0.7% in the Netherlands, and from 2.8% to 2.7% in Belgium. In Challengers & Growth Markets, ING has a sizeable residential mortgages portfolio in Germany, Australia, Spain and Poland. The top five countries within Rest of Europe based on outstandings were: Italy (€18.7 billion), Romania (€11.5 billion), Switzerland (€11.2 billion), Türkiye (€7.2 billion) and Ireland (€4.7 billion). In Europe, outside the Benelux, rating distribution in most countries remained stable. The most noticeable changes in rating distribution were observed in Rest of Europe, where the development of the Russian portfolio caused a decrease into Performing Restructuring from 3.0% to 2.3%. Note the paragraph on Russian exposures in section 'Risk management at ING Group'. Apart from Russia, noticeable changes occurred in the UK, where NPL decreased from 1.2% to 0.6%. In terms of rating distribution for the Americas region, an increase in NPL is observed to 1.5% (from 0.8%), whereas in Asia, NPL decreased from 2.7% to 2.0%. In Africa, NPL increased from 3.0% to 13.0%, driven by one well-covered file. Credit risk mitigation (*) ING uses various techniques and instruments to mitigate the credit risk associated with an exposure and to reduce the losses incurred subsequent to a default by a customer. The most common terminology used in ING for credit risk protection is ‘cover’. While a cover may be an important mitigant of credit risk and an alternative source of repayment, generally it is ING’s practice to lend on the basis of the customer’s creditworthiness rather than exclusively relying on the value of the cover. Cover forms (*) Within ING, there are two distinct forms of covers. First, where the asset has been pledged to ING as collateral or security, ING has the right to liquidate it should the customer be unable to fulfil its financial obligation. As such, the proceeds can be applied towards full or partial compensation of the customer's outstanding exposure. This may be tangible (such as cash, securities, receivables, inventory, plant and machinery, and mortgages on real estate properties) or intangible (such as patents, trademarks, contract rights and licences). Second, where there is a third-party obligation, indemnification or undertaking (either by contract and/or by law), ING has the right to claim from that third party an amount if the customer fails on its obligations. The most common examples are guarantees, such as parent guarantees, export credit insurances or third-party pledged mortgages. Insurance or reinsurance covers, including comprehensive private risk insurance (CPRI) may be recognised as guarantees and effectively function in an equivalent manner. ING accepts credit risk insurance companies and export credit agencies (ECAs) as cover providers. Cover valuation methodology (*) General guidelines for cover valuation are established with the objective of ensuring consistent application within ING. These also require that the value of the cover is monitored on a regular basis. Covers are revalued periodically and whenever there is reason to believe that the market is subject to significant changes in conditions. The frequency of monitoring and revaluation depends on the type of cover. The valuation method also depends on the type of covers. For asset collateral, the valuation sources can be the customer’s balance sheet (e.g. inventory, machinery and equipment), nominal value (e.g. cash and receivables), market value (e.g. securities and commodities), independent valuations (e.g. commercial real estate) and market indices (e.g. residential real estate). For third-party obligations, the valuation is based on the value that is attributed to the contract between ING and that third party. Where collateral values are used in the calculation of stage 3 individual loan loss provisions, haircuts may be applied to the valuation in specific circumstances, to sufficiently include all relevant factors impacting future cash flows. ING applies haircuts to the collateral values of real estate, shipping and aviation assets that are used in the calculation of the loss-given-default in recovery scenarios. The haircut reflects the risks of adverse price developments between the moment of valuation of an asset and the actual settlement/cash receipt. Cover values (*) This section provides insight into the types of cover and the extent to which exposures benefit from collateral or guarantees. The disclosure differentiates between risk categories (lending, investment, money market and pre-settlement). The most relevant types of cover include mortgages, financial collateral (cash and securities) and guarantees. ING obtains cover that is eligible for credit risk mitigation under CRR/CRDIV, as well as cover that is not eligible. Collateral covering financial market transactions is valued on a daily basis, and as such not included in the following tables. To mitigate the credit risk arising from financial markets transactions, the bank enters into legal agreements governing the exchange of financial collateral (high-quality government bonds and cash). The cover values are presented for the total portfolio of ING, both the performing and non-performing portfolio. Our definition of non-performing is explained in detail in ‘Credit restructuring’ (below). The next table gives an overview of the collateralisation of the ING’s total portfolio.
Cover values including guarantees received (*)
in € million |  | Cover type and value | Collateralisation
2023 | Outstandings | Mortgages | Financial Collateral | Guarantees | Other covers | No Cover | Partially covered | Fully covered
Consumer lending | 360,124 | 804,994 | 22,401 | 25,269 | 29,070 | 6.2% | 2.0% | 91.8%
Business lending | 363,826 | 162,491 | 26,333 | 115,944 | 428,531 | 35.2% | 22.5% | 42.3%
Investment and money market | 158,506 |  |  | 1,040 | 549 | 99.0% | 0.6% | 0.4%
Total lending, investment and money market | 882,455 | 967,485 | 48,735 | 142,252 | 458,149 | 34.8% | 10.2% | 55.0%
of which NPL | 11,653 | 8,880 | 1,609 | 3,204 | 9,241 | 25.7% | 26.9% | 47.4%
Pre-settlement | 49,096 |  |  |  |  |  |  |
Total Group | 931,552 |  |  |  |  |  |  |
Cover values including guarantees received (*)
in € million |  | Cover type and value | Collateralisation
2022 | Outstandings | Mortgages | Financial Collateral | Guarantees | Other covers | No Cover | Partially covered | Fully covered
Consumer lending | 353,323 | 700,961 | 5,626 | 24,231 | 42,817 | 6.2% | 7.6% | 86.2%
Business lending | 379,405 | 167,122 | 29,501 | 118,294 | 438,864 | 37.5% | 22.3% | 40.2%
Investment and money market | 141,432 |  | 5 | 1,213 | 2 | 99.1% | 0.6% | 0.3%
Total lending, investment and money market | 874,160 | 868,083 | 35,132 | 143,738 | 481,683 | 34.8% | 12.9% | 52.3%
of which NPL | 11,637 | 7,738 | 1,007 | 3,648 | 3,045 | 25.2% | 25.7% | 49.2%
Pre-settlement | 63,901 |  |  |  |  |  |  |
Total | 938,061 |  |  |  |  |  |  |
Excluding the pre-settlement portfolio, 55.0% (2022: 52.3%) of ING’s outstandings were fully collateralised in 2023. Since investments traditionally do not require covers, the percentage for ‘no covers’ in this portfolio is above 95%. However, 99% of the investment outstanding is investment grade. Improved economic conditions in ING’s main markets contributed to improved collateral valuations, observed in consumer lending. Consumer lending portfolio (*) The consumer lending portfolio accounts for 38.7% (2022: 37.7%) of ING’s total outstanding, primarily consisting of residential mortgage loans and other consumer lending loans. As a result, most collateral consists of mortgages. Mortgage values are collected in an internal central database and in most cases external data is used to index the market value. A significant part of ING’s residential mortgage portfolio is in the Netherlands (34.8%), Germany (28.0%), Belgium including Luxembourg (13.3%) and Australia (10.9%). Note that the large increase in Financial Collateral and decrease in Other covers is related to a reclassification of certain cover types. Business lending portfolio (*) Business lending accounts for 39.1% (2022: 40.4%) of ING’s total outstanding. Business lending presented in this section does not include pre-settlement, investment and money market exposures. Credit quality (*)
Credit quality outstandings (*)
in € million | 2023 | 2022
Performing not past due | 795,942 | 799,990
Business lending performing past due | 8,825 | 7,659
Consumer lending performing past due | 846 | 780
Non-performing | 11,653 | 11,691
Total lending and investment | 817,266 | 820,120
Money market | 65,189 | 54,039
Pre-settlement | 49,096 | 63,901
Total | 931,552 | 938,061
Past due obligations (*) Retail Banking measures its portfolio in terms of payment arrears and determines on a monthly basis if there are any significant changes in the level of arrears. This methodology is applicable to private individuals, as well as business lending. An obligation is considered ‘past due’ if a payment of interest or principal is more than one day late. ING aims to help its customers as soon as they are past due by reminding them of their payment obligations. In its contact with the customers, ING aims to solve the (potential) financial difficulties by offering a range of measures (e.g. payment arrangements, restructuring). If the issues cannot be cured, for example because the customer is unable or unwilling to pay, the contract is sent to the recovery unit. The facility is downgraded to risk rating 20 (non-performing) when the facility or obligor – depending on the level at which the non-performing status is applied – is more than 90 days past due and to risk rating 21 or 22 in case of an exit scenario. ING has aligned the regulatory concept of non-performing with that of the definition of default. Hence, in WB, obligors are classified as non-performing when a default trigger occurs: •ING believes the borrower is unlikely to pay. The borrower has evidenced significant financial difficulty, to the extent that it will have a negative impact on the future cash flows of the financial asset. The following events could be seen as indicators of financial difficulty: •The borrower (or third party) has started insolvency proceedings; •A group company/co-borrower has NPL status; •Indication of fraud (affecting the company’s ability to service its debt); •There is doubt as to the borrower’s ability to generate stable and sufficient cash flows to service its debt; •Restructuring of debt. •ING has granted concessions relating to the borrower’s financial difficulty, the effect of which is a reduction in expected future cash flows of the financial asset below current carrying amount. •The obligor has failed in the payment of principal, interest or fees, the total past due amount is above the materiality threshold and this remains the case for more than 90 consecutive days. Further, WB has an individual name approach, using early warning indicators to signal possible future issues in debt service.
Ageing analysis (past due but performing): Consumer lending portfolio by geographic area, outstandings (*)
in € million | 2023 | 2022
Region | Past due for 1–30 days | Past due for 31–60 days | Past due for 61–90 days | Total | Past due for 1–30 days | Past due for 31–60 days | Past due for 61–90 days | Total
Europe | Belgium | 223 | 43 | 29 | 295 | 294 | 27 | 18 | 339
Germany | 89 | 40 | 18 | 147 | 68 | 34 | 13 | 116
Poland | 76 | 8 | 5 | 89 | 59 | 8 | 4 | 71
Netherlands | 67 | 24 | 6 | 97 | 36 | 10 | 5 | 51
Luxembourg | 21 | 2 | 2 | 25 | 43 | 4 | 2 | 48
Spain | 19 | 13 | 6 | 38 | 13 | 9 | 5 | 27
Rest of Europe | 64 | 19 | 12 | 94 | 62 | 16 | 8 | 86
America | 1 |  |  | 1 | 1 |  |  | 1
Asia |  |  |  | 1 |  |  |  |
Australia | 43 | 15 | 1 | 59 | 29 | 11 | 2 | 42
Total | 602 | 164 | 79 | 846 | 604 | 119 | 57 | 780
The past due but performing consumer lending outstanding increased by €66 million, mainly due to increase in 31-60 (+€45 million) and 61-90 (+€22 million). The largest increase was observed in the Netherlands (+€47 million) and Germany (+€31 million) mainly in the 1-30-day bucket; the largest decrease was seen in Belgium (-€44 million) driven by the decrease in the 1-30 bucket.
Ageing analysis (past due but performing): Business lending portfolio by geographic area, outstandings (*)
in € million | 2023 | 2022
Region | Past due for 1–30 days | Past due for 31–60 days | Past due for 61–90 days | Total | Past due for 1–30 days | Past due for 31–60 days | Past due for 61–90 days | Past due for >90 days | Total
Europe | Belgium | 929 | 98 | 11 | 1,037 | 579 | 49 | 10 |  | 639
United Kingdom | 623 | 659 | 128 | 1,410 | 1,147 | 77 | 512 |  | 1,736
Luxembourg | 577 | 8 | 11 | 596 | 302 | 1 |  |  | 303
Netherlands | 509 | 10 | 12 | 531 | 730 | 30 | 15 |  | 775
Poland | 346 | 26 | 10 | 383 | 279 | 35 | 14 |  | 329
France | 58 | 132 |  | 190 | 83 | 6 |  |  | 90
Germany | 131 | 110 | 1 | 242 | 44 | 16 |  |  | 60
Rest of Europe | 972 | 2 | 2 | 977 | 474 | 239 | 1 | 1 | 715
America | 2,508 | 101 | 41 | 2,650 | 1,901 | 67 | 19 |  | 1,986
Asia | 284 |  | 22 | 306 | 553 | 48 |  |  | 601
Australia | 501 | 1 |  | 502 | 359 | 61 | 4 | 2 | 426
Total | 7,437 | 1,148 | 240 | 8,825 | 6,452 | 629 | 575 | 4 | 7,659
Total past due but performing outstanding of business lending increased by €1.2 billion. Increase is observed in the 1-30 days (€1.0 billion) past due bucket and in the 31-60 days (€0.5 billion), partly offset by a decrease in the 61–90 days past due bucket (-€336 million), driven by the United Kingdom (-€383 million). Credit restructuring (*) Global Credit Restructuring (GCR) is the dedicated and independent department that deals with non- performing loans and loans that hold a reasonable probability that ING will end up with a loss, if no specific action is taken. GCR handles accounts or portfolios requiring an active approach, which may include renegotiation of terms and conditions and business or financial restructuring. The loans are managed by GCR or by units in the various regions and business units. ING uses three distinct statuses to categorise the management of clients with (perceived) deteriorating credit risk profiles, i.e. there is increasing doubt as to the performance and the collectability of the client’s contractual obligations: •Watch list: Usually, a client is first classified as watch list when there are concerns of any potential or material deterioration in credit risk profile that may affect the ability of the client to adhere to its debt service obligations or to refinance its existing loans. Watch list status requires more than usual attention, increased monitoring and quarterly reviews. Some clients with a watch list status may develop into a restructuring status or even a recovery status. •Restructuring: A client is classified as restructuring when there are concerns about the client’s financial stability, credit worthiness, and/or ability to repay, but where the situation does not require the termination or acceleration of facilities or the liquidation of collateral. ING’s actions aim to maintain the going concern status of the client by: •restoring the client’s financial stability; •supporting the client’s turnaround; •restoring the balance between debt and equity; and •restructuring the debt to a sustainable situation. •Recovery: A client is classified as in recovery when ING and/or the client concludes that the client’s financial situation cannot be restored and a decision is made to terminate the (credit) relationship or even to enter into bankruptcy. ING prefers an amicable exit, but will enforce and liquidate the collateral or claim under the guarantees if deemed necessary. Watch list, restructuring and recovery accounts are reviewed at least quarterly by the front office, GCR and the relevant credit risk management executives. Forbearance (*) Forbearance occurs when a client is unable to meet their financial commitments due to financial difficulties they face or are about to face and ING grants concessions towards them. Forborne assets are assets in respect of which forbearance measures have been granted. Forbearance may enable clients experiencing financial difficulties to continue repaying their debt. For business clients, ING mainly applies forbearance measures to support clients with fundamentally sound business models that are experiencing temporary difficulties with the aim of maximising the client’s repayment ability and therewith avoiding a default situation or helping the client to return to a performing situation. For ING retail units, clear criteria have been established to determine whether a client is eligible for the forbearance process. Specific approval mandates are in place to approve the measures, as well as procedures to manage, monitor and report the forbearance activities. ING reviews the performance of forborne exposures at least quarterly, either on a case-by-case (business) or on a portfolio (retail) basis. All exposures are eligible for forbearance measures, i.e. both performing (risk ratings 1-19) and non- performing (risk ratings 20-22) exposures. ING uses specific criteria to move forborne exposures from non- performing to performing or to remove the forbearance statuses that are consistent with the corresponding European Banking Authority (EBA) standards. An exposure is reported as forborne for a minimum of two years. An additional one-year probation period is applied to forborne exposures that move from non- performing back to performing.
Summary Forborne portfolio (*)
in € million | 2023 | 2022
Business line | Outstandin gs | Of which: performi ng | Of which: non- performing | % of total portfolio | Outstandin gs | Of which: performi ng | Of which: non- performing | % of total portfolio
Wholesale Banking | 6,063 | 3,919 | 2,144 | 1.8% | 8,359 | 5,880 | 2,478 | 2.7%
Retail Banking | 7,026 | 4,128 | 2,898 | 1.4% | 8,080 | 4,973 | 3,107 | 1.6%
Total | 13,089 | 8,047 | 5,042 | 1.5% | 16,438 | 10,853 | 5,585 | 2.0%
Summary Forborne portfolio by forbearance type (*)
in € million | 2023 | 2022
Forbearance type | Outstandin gs | Of which: performi ng | Of which: non- performing | % of total portfolio | Outstandin gs | Of which: performi ng | Of which: non- performing | % of total portfolio
Loan modification | 11,881 | 7,550 | 4,331 | 1.4% | 15,317 | 10,428 | 4,889 | 1.9%
Refinancing | 1,208 | 497 | 711 | 0.1% | 1,121 | 426 | 695 | 0.1%
Total | 13,089 | 8,047 | 5,042 | 1.5% | 16,438 | 10,853 | 5,585 | 2.0%
As of 31 December 2023, ING’s total forborne assets decreased by €3.3 billion compared to 31 December 2022. WB decreased by €2.3 billion and Retail Banking decreased by €1.1 billion. The decreases are mainly caused by passing the two-year probation period following the Covid-19 pandemic. Wholesale Banking (*) As of December 2023, WB forborne assets amounted to €6.1 billion (2022: €8.4 billion), which represented 1.8% (2022: 2.7%) of the total WB portfolio.
Wholesale Banking: Forborne portfolio by geographical area (*)
in € million | 2023 | 2022
Region | Outstandings | Of which: performing | Of which: non- | Outstandings | Of which: performing | Of which: non-
performing | performing
Europe | Netherlands | 361 | 301 | 60 | 720 | 630 | 90
Belgium | 454 | 446 | 8 | 659 | 651 | 8
Germany | 288 | 148 | 139 | 580 | 466 | 115
United Kingdom | 583 | 425 | 158 | 1,044 | 721 | 323
Italy | 54 | 19 | 34 | 205 | 157 | 48
Norway | 6 | 0 | 6 | 33 | 0 | 33
Poland | 520 | 519 | 0 | 203 | 189 | 14
Rest of Europe | 1,421 | 1,142 | 279 | 2,176 | 1,749 | 427
America | 1,025 | 532 | 493 | 1,353 | 1,032 | 321
Asia | 1,198 | 277 | 921 | 1,107 | 143 | 964
Australia | 87 | 87 | 0 | 217 | 132 | 85
Africa | 68 | 23 | 45 | 61 | 10 | 51
Total | 6,063 | 3,919 | 2,144 | 8,359 | 5,880 | 2,478
Wholesale Banking: Forborne portfolio by economic sector (*)
in € million | 2023 | 2022
Industry | Outstandings | Of which: performing | Of which: non- performing | Outstandings | Of which: performing | Of which: non- performing
Natural Resources | 788 | 321 | 467 | 1,239 | 603 | 636
Real Estate | 1,320 | 1,254 | 66 | 2,000 | 1,917 | 84
Transportation & Logistics | 315 | 175 | 139 | 1,073 | 868 | 205
Food, Beverages & Personal Care | 866 | 465 | 401 | 1,082 | 543 | 539
Services | 284 | 254 | 30 | 697 | 665 | 32
Automotive | 138 | 98 | 40 | 172 | 125 | 46
Utilities | 510 | 255 | 255 | 469 | 255 | 214
General Industries | 145 | 74 | 71 | 255 | 176 | 80
Retail | 282 | 104 | 178 | 302 | 227 | 76
Chemicals, Health & Pharmaceuticals | 571 | 559 | 11 | 191 | 168 | 23
Builders & Contractors | 133 | 72 | 61 | 168 | 94 | 74
Other | 712 | 287 | 425 | 710 | 240 | 469
Total | 6,063 | 3,919 | 2,144 | 8,359 | 5,880 | 2,478
The main concentration of forborne assets in a single country was in United States with 13.6% (2022: 11.5%) of the total WB forborne assets. WB forborne assets decreased by €2.3 billion compared to 2022, also driven by passing the two-year probation period following the Covid-19 pandemic. Decrease is mainly visible in the performing forborne assets (-€2.0 billion), mainly in the industries Transportation & Logistics, Real Estate and Services. The decrease was partial offset by an increase for Chemicals, Health and Pharmaceuticals, driven by a few large individual clients. WB's forborne assets are mainly concentrated in Real Estate, Food Beverages & Personal Care, Natural Resources, Chemicals, Health & Pharmaceuticals, and Utilities. These five economic sectors accounted for 67% of the total WB forborne outstandings Retail Banking (*) As of the end of December 2023, Retail Banking forborne assets totalled €7.0 billion, which represented 1.4% of the total Retail Banking portfolio. The majority of forborne exposures were in private individuals with 50.5%.
Retail Banking: Forborne portfolio by geographical area (*)
in € million | 2023 | 2022
Region | Outstandings | Of which: performing | Of which: non- | Outstandings | Of which: performing | Of which: non-
performing | performing
Europe | Netherlands | 1,483 | 981 | 502 | 2,832 | 2,043 | 789
Belgium | 2,153 | 838 | 1,315 | 2,644 | 1,331 | 1,314
Germany | 1,309 | 1,064 | 246 | 804 | 610 | 194
Poland | 852 | 522 | 330 | 588 | 309 | 279
Türkiye | 25 | 15 | 10 | 64 | 31 | 32
Italy | 123 | 51 | 71 | 131 | 52 | 79
Romania | 135 | 49 | 86 | 124 | 53 | 71
Spain | 138 | 118 | 21 | 35 | 15 | 20
Rest of Europe | 88 | 58 | 30 | 73 | 48 | 25
America | 21 | 20 |  | 13 | 12 |
Asia | 2 | 1 | 1 | 3 | 1 | 1
Australia | 697 | 411 | 286 | 768 | 467 | 302
Africa |  |  |  | 1 |  |
Total | 7,026 | 4,128 | 2,898 | 8,080 | 4,973 | 3,107
The main concentration of forborne assets in a single country was in Belgium with 30.6% (2022: 32.7%) of total Retail Banking forborne assets and 45.4% (2022: 42.3%) of the non-performing forborne assets. Next to that, Netherlands had 21.1% (2022: 35.0%) of the total Retail forborne assets and Germany 18.6% (2022: 10.0%). The increase in Germany is driven by mortgages, where clients that choose for a repayment percentage below a certain threshold, though contractually allowed, are conservatively considered forborn. Non-performing loans (*) ING’s loan portfolio is under constant review. Loans to obligors that are considered more than 90 days past due and above applicable thresholds are reclassified as non-performing. For business lending portfolios, there generally are reasons for declaring a loan non-performing prior to the obligor being 90 days past due. These reasons include, but are not limited to, ING’s assessment of the customer’s perceived inability to meet its financial obligations, or the customer filing for bankruptcy or bankruptcy protection. The table below represents the breakdown by industry of credit risk outstandings that have been classified as non-performing.
Non-performing Loans: outstandings by economic sector and business lines (*)1
in € million | Wholesale Banking | Retail Benelux | Retail Challengers & Growth Markets | Total
Industry | 2023 | 2022 | 2023 | 2022 | 2023 | 2022 | 2023 | 2022
Private Individuals | 4 |  | 2,210 | 2,174 | 2,206 | 1,954 | 4,419 | 4,129
Natural Resources | 669 | 1,369 | 60 | 34 | 25 | 17 | 754 | 1,421
Food, Beverages & Personal Care | 565 | 672 | 363 | 438 | 157 | 122 | 1,085 | 1,233
Transportation & Logistics | 437 | 367 | 69 | 165 | 65 | 51 | 572 | 583
Services | 101 | 119 | 415 | 448 | 66 | 61 | 582 | 628
Real Estate | 592 | 172 | 398 | 486 | 64 | 54 | 1,053 | 712
General Industries | 111 | 114 | 270 | 268 | 115 | 100 | 497 | 482
Builders & Contractors | 124 | 139 | 291 | 244 | 162 | 110 | 577 | 493
Retail | 207 | 98 | 121 | 107 | 67 | 39 | 395 | 244
Utilities | 331 | 387 | 12 | 7 | 6 | 7 | 348 | 401
Chemicals, Health & Pharmaceuticals | 101 | 175 | 97 | 115 | 35 | 20 | 233 | 310
Telecom | 378 | 288 | 9 | 12 | 3 | 3 | 390 | 303
Other | 412 | 440 | 270 | 260 | 66 | 52 | 748 | 753
Total | 4,034 | 4,340 | 4,583 | 4,759 | 3,036 | 2,592 | 11,653 | 11,691
1 Based on lending and investment outstandings.
Non-performing Loans: outstandings by economic sectors and geographical area (*)
in € million | Region |  |  |  |  |  |  |  |  |  |  |  |  | Total
Industry | Netherlands | Belgium | Germany | Poland | Spain | United Kingdom | France | Luxembourg | Rest of Europe | America | Asia | Australia | Africa | 2023
Private Individuals | 609 | 1,535 | 885 | 225 | 235 | 3 | 8 | 45 | 489 | 2 | 2 | 380 | 1 | 4,419
Natural Resources | 30 | 60 | 1 | 23 |  |  |  | 55 | 164 | 31 | 369 |  | 20 | 754
Food, Beverages & Personal Care | 281 | 157 | 1 | 131 |  | 139 | 7 |  | 158 | 82 | 128 |  |  | 1,085
Transportation & Logistics | 110 | 50 | 2 | 51 | 47 | 20 |  | 1 | 168 | 49 | 1 | 2 | 72 | 572
Services | 121 | 342 | 2 | 55 | 2 |  | 3 | 8 | 13 | 37 |  |  |  | 582
Real Estate | 40 | 297 | 53 | 55 | 9 |  | 36 | 16 | 7 | 519 |  | 21 |  | 1,053
General Industries | 145 | 127 | 49 | 99 |  |  | 2 | 7 | 24 | 42 |  |  |  | 497
Builders & Contractors | 113 | 181 | 2 | 135 |  |  |  | 22 | 91 | 32 |  |  |  | 577
Retail | 51 | 82 | 36 | 52 |  |  | 2 |  | 14 | 149 | 7 |  |  | 395
Utilities | 14 | 5 |  | 21 |  |  |  |  | 18 | 153 | 138 |  |  | 348
Chemicals, Health & Pharmaceuticals | 31 | 77 | 13 | 25 |  |  | 64 |  | 11 | 12 |  |  |  | 233
Telecom | 12 | 1 | 28 | 3 |  |  |  |  | 13 | 56 | 277 |  |  | 390
Other | 28 | 239 | 42 | 55 |  | 2 | 1 | 6 | 23 | 46 | 128 |  | 179 | 748
Total | 1,586 | 3,153 | 1,114 | 929 | 293 | 165 | 124 | 162 | 1,193 | 1,210 | 1,050 | 403 | 272 | 11,653
Non-performing Loans: outstandings by economic sectors and geographical area (*)
in € million | Region |  |  |  |  |  |  |  |  |  |  |  |  | Total
Industry | Netherlands | Belgium | Germany | Poland | Spain | United Kingdom | France | Luxembourg | Rest of Europe | America | Asia | Australia | Africa | 2022
Private Individuals | 574 | 1,538 | 739 | 185 | 194 | 4 | 11 | 36 | 470 | 3 | 4 | 370 | 1 | 4,129
Natural Resources | 57 | 33 |  | 14 |  |  |  | 53 | 432 | 77 | 649 | 85 | 21 | 1,421
Food, Beverages & Personal Care | 310 | 179 | 24 | 109 |  | 173 | 7 |  | 228 | 77 | 126 |  |  | 1,233
Transportation & Logistics | 232 | 58 | 1 | 36 | 47 | 20 |  | 2 | 154 | 24 | 7 | 1 |  | 583
Services | 136 | 375 | 2 | 43 | 5 |  | 3 | 2 | 21 | 40 |  |  |  | 628
Real Estate | 89 | 376 |  | 54 |  | 84 | 25 | 19 | 7 | 47 |  | 11 |  | 712
General Industries | 127 | 142 | 17 | 78 |  |  | 31 | 2 | 26 | 58 |  |  |  | 482
Builders & Contractors | 65 | 187 | 2 | 86 |  |  |  | 20 | 101 | 32 |  |  |  | 493
Retail | 31 | 85 | 38 | 26 |  |  | 18 | 1 | 13 | 22 | 7 | 2 |  | 244
Utilities | 6 | 6 | 26 | 23 |  |  |  |  | 17 | 194 | 129 |  |  | 401
Chemicals, Health & Pharmaceuticals | 51 | 100 | 2 | 15 |  | 14 | 100 |  | 28 |  |  |  |  | 310
Telecom | 24 | 1 |  | 3 |  |  |  |  |  | 5 | 270 |  |  | 303
Other | 40 | 232 | 48 | 38 |  | 50 |  | 10 | 79 | 75 | 130 |  | 51 | 753
Total | 1,742 | 3,312 | 899 | 710 | 246 | 344 | 196 | 145 | 1,578 | 654 | 1,324 | 470 | 72 | 11,691
In 2023, the NPL portfolio stayed relatively flat at €11.7 billion. An increase in Challengers and Growth retail (+€0.4 billion) was offset by decreases in Wholesale Banking (-€0.3 billion) and Retail Benelux (-€0.2 bill ion). The increase in Challengers & Growth Markets was mainly witnessed in private individuals. In Wholesale Banking, the decrease in the Natural Resources sector was partially compensated by an increase in Real Estate sector, especially in the Americas. The top three countries by NPL outstanding are Belgium, the Netherlands and the Americas. Loan loss provisioning (*) ING recognises loss allowances based on the expected credit loss (ECL) model of IFRS 9, which is designed to be forward-looking. The IFRS 9 impairment requirements are applicable to on-balance sheet financial assets measured at amortised cost or fair value through other comprehensive income (FVOCI), such as loans, debt securities and lease receivables, as well as off-balance-sheet items such as undrawn loan commitments, financial- and non-financial guarantees issued. ING distinguishes between two types of calculation methods for credit loss allowances: •Collective 12-month ECL (Stage 1) and collective lifetime ECL (Stage 2) for portfolios of financial instruments, as well as collective lifetime ECL for credit-impaired exposures (Stage 3) below €1 million; •Individual lifetime ECL for credit-impaired (Stage 3) financial instruments with exposures above €1 million. IFRS 9 models (*) ING's IFRS 9 models leverage on the internal rating-based (IRB) models (PD, LGD, EAD), which include certain required conservatism. To include IFRS 9 requirements, such regulatory conservatism is removed from the ECL parameters (PD, LGD and EAD). The IFRS 9 models apply two other types of adjustments to the IRB ECL parameters: (i) to the economic outlook and (ii) for Stage 2 and Stage 3 assets only, to the lifetime horizon. The IFRS 9 model parameters are estimated based on statistical techniques and supported by expert judgement. ING has aligned the definition of default for regulatory purposes with the definition of ‘credit-impaired’ financial assets under IFRS 9 (Stage 3). ING has also aligned its definition of default between IFRS9 and the regulatory technical standards (RTS) and EBA guidelines. More information can be found in section 1.5.6 of the consolidated financial statements. Climate and environmental risks in IFRS 9 models (*) Climate risk drivers (physical and transition risks) can reduce the ability of businesses and households to fulfil their obligations due on existing lending contracts. These may also lead to depreciation/ erosion of collateral values which would translate into higher credit losses and loan-to-value ratios in the lending portfolio of ING. At this point in time it is not yet possible to incorporate climate risk separately into IFRS 9 ECL models given the lack of sufficient empirical historical data and data limitations in the risk assessments on client level. Where climate and environmental factors have impacted the economy in the recent past or present, these impacts will currently be implicitly embedded in ING's IFRS9 ECL models through the projected macroeconomic indicators (e.g. indirectly via GDP growth and unemployment rates). We note however that our ECL models are primarily sensitive to the short-term economic outlook as we use a three-year time horizon for macroeconomic outlook, after which a mean reversion approach is applied. With regard to our evaluation of climate-related matters, where such events have already occurred (e.g. floods, stranded assets etc.), the impact of such events is individually assessed in the calculation of Stage 3 Individual provisions or management adjustments to ECL models. For example, we consider whether affected assets have suffered from a significant increase in credit risk (or are credit impaired) and whether the ECL is appropriate. Over the near-term time horizon, ING plans to continue to refine its methodologies to evaluate climate risks. ING is working on putting into practice quantitative methodologies for climate and environmental (C&E) risk identification, materiality assessment and risk appetite setting. Refer to ESG risk paragraph for further details on ESG risk management. Going forward, ING aims to continue to close the gaps on climate risk data, which will enable us to embed climate risks eventually into the IFRS 9 ECL models. Portfolio quality (*) The table below describes the portfolio composition over the different IFRS 9 stages and rating classes. The Stage 1 portfolio represents 91.5% (2022: 91.5%) of the total gross carrying amounts, mainly composed of investment grade, while Stage 2 makes up 7.4% (2022: 7.3%) and Stage 3 makes up 1.2% (2022: 1.2%) of the total gross carrying amounts, respectively.
Gross carrying amount per IFRS 9 stage and rating class (*)1,2,3
in € million | 12-month ECL (Stage 1) | Lifetime ECL not credit impaired (Stage 2) | Lifetime ECL credit impaired (Stage 3) | Total
2023
Rating class | Gross Carrying Amount | Provisions | Gross Carrying Amount | Provisions | Gross Carrying Amount | Provisions | Gross Carrying Amount | Provisions
Investment grade | 1 (AAA) | 87,071 | 1 | 439 |  |  |  | 87,510 | 1
2-4 (AA) | 132,159 | 8 | 2,553 | 2 |  |  | 134,711 | 9
5-7 (A) | 231,018 | 24 | 6,188 | 6 |  |  | 237,206 | 30
8-10 (BBB) | 302,967 | 85 | 17,004 | 24 |  |  | 319,971 | 108
Non-Investment grade | 11-13 (BB) | 157,387 | 226 | 19,273 | 93 |  |  | 176,661 | 319
14-16 (B) | 26,414 | 164 | 19,336 | 455 |  |  | 45,750 | 618
17 (CCC) | 617 | 10 | 4,125 | 233 |  |  | 4,742 | 242
Performing Restructuring | 18 (CC) |  |  | 4,617 | 402 |  |  | 4,617 | 402
19 (C) |  |  | 1,919 | 221 |  |  | 1,919 | 221
Non-performing loans | 20-22 (D) |  |  |  |  | 11,956 | 3,887 | 11,956 | 3,887
Total | 937,633 | 517 | 75,454 | 1,435 | 11,956 | 3,887 | 1,025,043 | 5,839
1 Compared to the credit risk portfolio, the differences are mainly undrawn committed amounts (€151 billion) and other positions (€9 billion) not included in credit outstandings and non-IFRS 9 eligible assets (€67 billion, mainly pre-settlement exposures) included in credit outstandings but not in the gross carrying amounts. 2 Includes impact from change in accounting policy as disclosed in table Changes in gross carrying amounts and loan loss provisions. 3 Stage 3 lifetime credit impaired provision includes €11 million on purchased or originated credit impaired.
Gross carrying amount per IFRS 9 stage and rating class (*)1,2
in € million | 12-month ECL (Stage 1) | Lifetime ECL not credit impaired (Stage 2) | Lifetime ECL credit impaired (Stage 3) | Total
2022
Rating class | Gross Carrying Amount | Provisions | Gross Carrying Amount | Provisions | Gross Carrying Amount | Provisions | Gross Carrying Amount | Provisions
Investment grade | 1 (AAA) | 100,885 | 2 | 284 |  |  |  | 101,169 | 2
2-4 (AA) | 98,181 | 5 | 2,493 | 1 |  |  | 100,675 | 6
5-7 (A) | 177,617 | 23 | 4,596 | 4 |  |  | 182,214 | 27
8-10 (BBB) | 321,308 | 98 | 14,714 | 29 |  |  | 336,023 | 127
Non-Investment grade | 11-13 (BB) | 155,910 | 277 | 17,365 | 91 |  |  | 173,275 | 368
14-16 (B) | 23,649 | 168 | 19,386 | 471 |  |  | 43,035 | 639
17 (CCC) | 7,671 | 8 | 4,572 | 194 |  |  | 12,244 | 202
Performing Restructuring | 18 (CC) |  |  | 5,198 | 595 |  |  | 5,198 | 595
19 (C) |  |  | 2,116 | 293 |  |  | 2,116 | 293
Non-performing loans | 20-22 (D) |  |  |  |  | 11,708 | 3,841 | 11,708 | 3,841
Total | 885,222 | 581 | 70,725 | 1,679 | 11,708 | 3,841 | 967,655 | 6,101
1 Compared to the credit risk portfolio, the differences are mainly undrawn committed amounts (€150.1 billion) and other positions (€4.4 billion) not included in credit outstandings and non-IFRS 9 eligible assets (€116.1 billion, mainly guarantees, letters of credit and pre- settlement exposures) included in credit outstandings but not in the gross carrying amounts. 2 IAS 37 provisions are established for non-credit replacement guarantees not in the scope of IFRS 9. Total IAS 37 provisions (€109 million) are excluded.
Changes in gross carrying amounts and loan loss provisions (*) The table below provides a reconciliation by stage of the gross carrying amount and allowances for loans and advances to banks and customers, including loan commitments and financial guarantees. The transfers of financial instruments represent the impact of stage transfers upon the gross carrying/nominal amount and associated allowance for ECL. This includes the net-remeasurement of ECL arising from stage transfers, for example, moving from a 12-month (Stage 1) to a lifetime (Stage 2) ECL measurement basis. The net-remeasurement line represents the changes in provisions for facilities that remain in the same stage. Please note the following comments with respect to the movements observed in the table below: •The opening balance is impacted by a change in accounting policy following the adoption of IFRS 17, more specifically for loans with death waivers that no longer meet the ‘solely payments of principal and interest’ (SPPI) criterion (€-55 million loan loss provisions impact) which are no longer recorded at amortised cost, and a change in policy for non-financial guarantees that are subject to contractual indemnification rights which led to a reclassification of the existing IAS 37 provision (€109 million loan loss provisions impact) and a remeasurement of these non-financial guarantees (€42 million loan loss provisions impact). Reference is made to Note 1 'Basis of preparation and significant changes in the current reporting period'. •Stage 3 gross carrying amount increased slightly to €12.0 billion as at 31 December 2023 as new inflow into NPL (credit impaired) in 2023 was only partly offset by repayments, derecognitions and write-offs. Stage 3 provisions remained more or less flat at €3.9 billion. •Stage 2 gross carrying amount increased by €0.1 billio n from €75.4 billion (after changes in accounting policies) as at 31 December 2022 to €75.5 billi on as at 31 December 2023. An increase of Stage 2 exposure, driven by the implementation of an updated methodology for interest-only mortgages in the Netherlands, was offset by outflow due to improved macro economic outlook, decrease of exposure due to sales and repayments (including Russian exposure), forborne customers returning to Stage 1 after the probation period has ended and other improved risk drivers. As a result, Stage 2 provisions also decreased by €0.3 billion to €1.4 billion as of 31 December 2023.
Changes in gross carrying amounts and loan loss provisions (*)1, 2
in € million | 12-month ECL (Stage 1) | Lifetime ECL not credit impaired (Stage 2) | Lifetime ECL credit impaired (Stage 3) | Total
2023 | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions
Opening balance | 885,222 | 581 | 70,725 | 1,679 | 11,708 | 3,841 | 967,655 | 6,101
Impact of changes in accounting policies | 37,078 | 9 | 4,704 | 13 | 158 | 73 | 41,939 | 95
Adjusted Opening balance | 922,300 | 590 | 75,429 | 1,692 | 11,866 | 3,914 | 1,009,595 | 6,196
Transfer into 12-month ECL (Stage 1) | 11,832 | 28 | -11,583 | -239 | -249 | -36 |  | -247
Transfer into lifetime ECL not credit impaired (Stage 2) | -29,470 | -67 | 30,185 | 449 | -716 | -105 |  | 277
Transfer into lifetime ECL credit impaired (Stage 3) | -2,053 | -10 | -1,775 | -115 | 3,828 | 978 |  | 853
Net remeasurement of loan loss provisions |  | -149 |  | -94 |  | 59 |  | -184
New financial assets originated or purchased | 195,775 | 204 |  |  |  |  | 195,775 | 204
Financial assets that have been derecognised | -121,991 | -72 | -14,239 | -215 | -1,475 | -266 | -137,705 | -553
Net drawdowns and repayments | -38,758 |  | -2,525 |  | -229 |  | -41,511 |
Changes in models/risk parameters |  | 8 |  | 10 |  | 84 |  | 102
Increase in loan loss provisions |  | -58 |  | -204 |  | 714 |  | 452
Write-offs | -3 | -3 | -38 | -38 | -1,070 | -1,070 | -1,111 | -1,111
Recoveries of amounts previously written off |  |  |  |  |  | 71 |  | 71
Foreign exchange and other movements |  | -12 |  | -15 |  | 258 |  | 231
Closing balance | 937,633 | 517 | 75,454 | 1,435 | 11,956 | 3,887 | 1,025,043 | 5,839
1 Stage 3 lifetime credit impaired provision includes €11 million on purchased or originated credit impaired. 2 The addition to the loan provision (in the consolidated statement of profit or loss) amounts to €520 million of which €483 million related to IFRS 9 eligible financial assets, -€31 million related to non-credit replacement guarantees and €68 million to modification gains and losses on restructured financial assets.
Changes in gross carrying amounts and loan loss provisions (*)1, 2
in € million | 12-month ECL (Stage 1) | Lifetime ECL not credit impaired (Stage 2) | Lifetime ECL credit impaired (Stage 3)1 | Total
2022 | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions
Opening balance | 890,386 | 501 | 49,476 | 1,016 | 12,072 | 3,851 | 951,934 | 5,368
Transfer into 12-month ECL (Stage 1) | 8,513 | 21 | -8,105 | -142 | -408 | -47 |  | -168
Transfer into lifetime ECL not credit impaired (Stage 2) | -42,439 | -76 | 43,222 | 730 | -784 | -90 |  | 564
Transfer into lifetime ECL credit impaired (Stage 3) | -3,524 | -8 | -1,216 | -82 | 4,740 | 1,234 |  | 1,144
Net remeasurement of loan loss provisions |  | 8 |  | 223 |  | 199 |  | 430
New financial assets originated or purchased | 248,443 | 228 |  |  |  |  | 248,443 | 228
Financial assets that have been derecognised | -138,250 | -70 | -11,312 | -94 | -2,805 | -215 | -152,366 | -379
Net drawdowns and repayments | -77,907 |  | -1,340 |  | 21 |  | -79,226 |
Changes in models/risk parameters |  | -8 |  | 13 |  | 25 |  | 30
Increase in loan loss provisions |  | 95 |  | 648 |  | 1,106 |  | 1,849
Write-offs |  |  |  | -1 | -1,129 | -1,129 | -1,129 | -1,130
Recoveries of amounts previously written off |  |  |  |  |  | 71 |  | 71
Foreign exchange and other movements |  | -15 |  | 16 |  | -58 |  | -57
Closing balance | 885,222 | 581 | 70,725 | 1,679 | 11,708 | 3,841 | 967,655 | 6,101
1 Stage 3 Lifetime credit impaired provision includes €7 million on purchased or originated credit impaired.
2 The addition to the loan provision (in the consolidated statement of profit or loss) amounts to €1,861 million of which €1,850 million related to IFRS 9 eligible financial assets, -€3 million related to non-credit replacement guarantees and €14 million to modification gains and losses on restructured financial assets.
Modification of financial assets (*) The table below provides the following information: •Financial assets that were modified during the year (i.e. qualified as forborne) while they had a loss allowance measured at an amount equal to lifetime ECL. •Financial assets that were reclassified to stage 1 during the period.
Financial assets modified (*)
in € million | 2023 | 2022
Financial assets modified during the period |  |
Amortised cost before modification | 1,565 | 1,304
Net modification results | -75 | -124
Financial assets modified since initial recognition |  |
Gross carrying amount at 31 December of financial assets for which loss allowance has changed to 12-month measurement during the period | 2,599 | 2,382
Macroeconomic scenarios and sensitivity analysis of key sources of estimation uncertainty (*) Methodology (*) Our methodology in relation to the adoption and generation of macroeconomic scenarios is described in this section. We continue to follow this methodology in generating our probability-weighted ECL, with consideration of alternative scenarios and management adjustments supplementing this ECL where, in management's opinion, the consensus forecast does not fully capture the extent of recent credit or economic events. The macroeconomic scenarios are applicable to the whole ING portfolio in the scope of IFRS 9 ECLs. The IFRS 9 standard, with its inherent complexities and potential impact on the carrying amounts of our assets and liabilities, represents a key source of estimation uncertainty. In particular, ING’s reportable ECL numbers are sensitive to the forward-looking macroeconomic forecasts used as model inputs, the probability-weights applied to each of the three scenarios, and the criteria for identifying a significant increase in credit risk. As such, these crucial components require consultation and management judgement, and are subject to extensive governance. Baseline scenario (*) As a baseline for IFRS 9, ING has adopted a market-neutral view combining consensus forecasts for economic variables (GDP, unemployment) with market forwards (for interest rates, exchange rates and oil prices). Input from a leading third-party service provider is used to complement the consensus with consistent projections for variables for which there are no consensus estimates available (most notably house prices and – for some countries - unemployment), to generate alternative scenarios, to convert annual consensus information to a quarterly frequency and to ensure general consistency of the scenarios. As the baseline scenario is consistent with the consensus view it can be considered as free from any bias. The relevance and selection of macroeconomic variables is defined by the ECL models under credit risk model governance. The scenarios are reviewed and challenged by two panels of ING experts. The first panel consists of (economic) experts from Global Markets Research, risk and modelling, while the second panel consists of relevant senior managers in ING. Alternative scenarios and probability weights (*) Two alternative scenarios are taken into account: an upside and a downside scenario. The alternative scenarios have statistical characteristics as they are based on the forecast deviations of the leading third- party service provider. To understand the baseline level of uncertainty around any forecast, the leading third-party service provider keeps track of all its deviations (so called forecast errors) of the past 20 years. The distribution of forecast errors for GDP, unemployment, house prices and share prices is applied to the baseline forecast creating a broad range of alternative outcomes. In addition, to understand the balance of risks facing the economy in an unbiased way, the leading third-party service provider runs a survey with respondents from around the world and across a broad range of industries. In this survey respondents put forward their views of key risks. Following the survey results, the distribution of forecast errors (that is being used for determining the scenarios) may be skewed. For the downside scenario, ING has chosen for the 90th percentile of that distribution because this corresponds with the way risk management earnings-at-risk is defined within the Group. The upside scenario is represented by the 10th percentile of the distribution. The applicable percentiles of the distribution imply a 20 percent probability for each alternative scenario. Consequently, the baseline scenario has a 60 percent probability weighting. Please note that, given their technical nature, the downside and upside scenarios are not based on an explicit specific narrative. Macroeconomic scenarios applied (*) The macroeconomic scenarios applied in the calculation of loan loss provisions are based on the consensus forecasts. Baseline assumptions (*) The general picture that the consensus conveys is that global economic growth is going through a weak spell. Inflation has been coming down as energy prices have moderated, demand has weakened and pandemic-related supply problems have eased. Higher interest rates play their part in slowing demand. Although central banks could well be done with hiking interest rates – as inflation continues to fall – the lagged impact of past monetary tightening is still feeding through to the real economy. This is expected to cause GDP growth to come in even weaker in 2024, but pickup thereafter as the negative effects from tight monetary policy and high inflation fade. The high-interest rate environment is currently causing corrections in house prices, especially in markets where prices surged during the pandemic. Housing market corrections are assumed to gradually decrease in strength. The December 2023 consensus expects global output (as measured by the weighted average GDP growth rate of ING's 25 main markets) to slow from 2.6% in 2022, to 2.4% in 2023 and 1.8% in 2024. For 2025-2026, economic growth is expected to pick up again to 2.3% and 2.4%. The US economy outperformed expectations in 2023, driven by strong consumer spending and fiscal support. Still, headwinds have been building up, in part driven by the much higher interest rates currently experienced. While an outright recession is not expected, the consensus does foresee a marked slowdown in economic activity in the US in 2024. After this, the US economy is expected to gather strength again in 2025. The consensus expects the growth rate of the US economy to fall from 2.2% in 2023 to 0.9% in 2024 and to recover to 2% on average in 2025-2026. The eurozone economy is facing broad stagnation as it deals with the impact of the war in Ukraine, energy crisis, and higher interest rates. While inflation is coming down, the impact of last year’s purchasing power shock is still being felt. Monetary tightening works with a lag, which will still affect economic activity next year. Overall, the eurozone economy is forecasted to have grown by just 0.5% in 2023, but Germany has gone through recession reflecting its large (energy-intensive) manufacturing sector, and exposure to a disappointing Chinese economy. For next year, the recovery is expected to be very modest as consensus expects the eurozone to grow by only 0.8% in 2024, before recovering to 1.4% on average in 2025-2026. Elsewhere in Europe, the outlook is mixed. In Poland, after an expected 0.4% growth in 2023, the economy is expected to recover by 2.5% in 2024 led by household consumption growth. Wage growth is set to remain strong while inflation has subsided substantially. The consensus expectation for Türkiye is to see growth slow substantially in 2024 with unemployment remaining around 10% for the foreseeable future. The consensus sees economic growth in Türkiye slowing from 3.5% in 2023, to 2.1% in 2024 and increasing again to 3.3% on on average in 2025-2026. The Russian economy has started to recover from the 2022 recession, but growth is set to moderate after 1.7% in 2023 to 1.2% on average in 2024-2026. 2023 was supposed to be the year of a strong reopening for China, but growth did not meet expectations. Weak global demand for goods has resulted in a weaker than expected industrial performance and the real estate sector continues to be a source of concern. For the 2024-2026 period, economic growth is expected to come in at only 4.2% despite increased stimulus. The global economic slowdown and tighter monetary policy continue to weigh on economic growth in Australia. After growing by 1.7% in 2023, the outlook sees a growth rate of 1.3% for 2024 and some pick-up to 2.4% and 2.5% for 2025-2026. Unemployment is expected to run up a little more, from 4.1% in 2023 to 4.4% ,but decrease again thereafter. When compared to the December 2022 consensus forecast, used for the 2022 Annual Report, the December 2023 forecast assumes somewhat better economic circumstances in 2023 but weaker for 2024. The forecast assumes to have Global GDP increased by 2.4% in 2023 (compared to 1.3% assumed before) and is expected to grow by 1.8% in 2024 (2.4% assumed before). The upgrade for 2023 reflects the better than expected economic performance of the US and eurozone as a recession on the back of the energy crisis was avoided, while the downgrade for 2024 reflects the impact of substantial monetary tightening. Alternative scenarios and risks (*) Because of the possible consequences of geopolitical risks, uncertainty surrounding the forecasts was higher in 2022. During 2023, the uncertainty level around the forecasts gradually decreased. To take this into account, ING applies normal levels of dispersion in the alternative scenarios used for year-end 2023 provisioning. For year-end 2022 half-widened dispersion levels were used, by which the near-term dispersion of the forward-looking distributions (from which the alternative scenarios are derived) was larger than in normal times. The baseline scenario assumes a further easing of inflation in 2024 and relatively resilient labour markets. However, a longer period of weakness, due to even more concerning geopolitical tensions, persistent elevated inflation and a slowdown in China could lead to a more protracted and deeper economic slowdown. As such, the balance of risks to the baseline outlook is negative and the alternative scenarios have a downward skew in line with the outcomes of the Global Risk Survey from a leading third-party service provider. The downward skew remained stable compared to what has been applied for year-end 2022, continuing to reflect risks related to possible escalation of geopolitical tensions, a prolonged surge in inflation and tightening monetary policy. The downside scenario, though technical in nature, sees for most countries a recession in 2024. Unemployment increases strongly in this scenario and house prices in most countries show outright falls. The downside scenario captures a possible escalation of geopolitical tensions, a prolonged surge in inflation and further slowing in China. The upside scenario – while equally technical in nature – reflects the possibility of a better economic outturn if consumers spend more of their excess savings, geopolitical risks subside and China stimulus works out more strongly than expected. Management adjustments applied this year (*) In times of volatility and uncertainty where portfolio quality and the economic environment are changing rapidly, models alone may not be able to accurately predict losses. In these cases, management adjustments can be applied to appropriately reflect ECL. Management adjustments can also be applied where the impact of the updated macroeconomic scenarios is over- or under-estimated by the IFRS 9 models. ING has internal governance frameworks and controls in place to assess the appropriateness of all management adjustments, involving first- and second lines of defence.
Management adjustments to ECL models (*)
in € million | 2023 | 2022
Economic sector based adjustments | 36 | 71
Inflation and interest rate increases / second-order impact adjustments | 351 | 334
Mortgage portfolio adjustments | 126 | 105
Other post model adjustments | 64 | -57
Total management adjustments | 577 | 453
The economic sector-based adjustments of €36 million as of 31 December 2023 (€71 million as at 31 December 2022) fully relates to Business Banking clients that have benefited from Covid-19 related government support programmes in the Netherlands. In line with 2022, it became clearer during 2023 that the Covid-19 pandemic had less impact than expected on the number of defaults in related sectors with relatively high tax debt to be repaid; the economic sector-based management adjustment has therefore been partially released. The remaining management adjustment is related to sectors with relatively high tax debt to be repaid and is expected to materialise with delayed effect. The €10 million adjustment on the livestock farming sector in the Netherlands that was also included in December 2022, related to nitrogen reduction targets has been fully released following improved risk metrics. ING has performed further assessment for both WB and Retail Banking on the impact of second-order effects. The second-order adjustments as at December 2022 captured a wider range of indirect effects such as supply chain issues, staffing shortages and high energy prices. In 2023, the risks from high inflation and rapidly increasing interest rates became more apparent compared to other indirect effects, causing a shift in focus of this adjustment. This resulted in an overall adjustment for inflation and interest rate increases of €351 million in total as at 31 December 2023, of which €138 million (31 December 2022: €164 million) relates to Retail Banking segments and €213 million (31 December 2022: €170 million) to the Wholesale Banking segment. As the credit risk models in Retail Banking generally assume that inflation and interest rate increases risks materialise via other risk drivers such as GDP and unemployment rates with a delay, an overlay approach was determined. The methodology considers debt-to-income ratios and the percentage of loans that are expected to reprice within one year to timely estimate the expected credit losses related to reduced repayment capacity and affordability for private individuals and business clients in the Retail Banking segment. In Wholesale Banking, the IFRS 9 credit risk models mostly leverage on GDP growth as a generic macroeconomic variable. High inflation and rapidly increasing interest rates however trigger economic heterogeneity (i.e. some businesses benefit, while others suffer), as such the current circumstances are expected to cause more defaults than normally predicted using GDP growth. A sector-based heatmap approach was used to adjust the probability of default for clients in sectors that are expected to be significantly impacted by high inflation and increased interest rates, including refinancing risk. The adjustment is predominantly visible in the commercial real estate sector and reflected in Stage 1 and Stage 2. The overall mortgage portfolio adjustment as at 31 December 2023 increased to €126 million (31 December 2022: €105 million), as an adjustment of €115 million has been recognised in 2023 following new insights from a risk segmentation model that captures affordability, repayment and refinancing risk on performing mortgage customers with a bullet loan in the Netherlands. The mortgage portfolio adjustment that relates to significant increase of house prices was reduced to €11 million as at 31 December 2023, coming from €105 million as at December 2022. This decrease is reflecting the decline in house prices in various countries, subsequent materialisation into increased model-based ECL as well as an improved market outlook on the recovery value of residential real estate. ING still recognises a management adjustment related to house prices in Stage 2 and Stage 3 on the mortgage portfolios in Germany to maintain an appropriate level of ECL. The management adjustments are determined by calculating the impact of lower house prices on loan-to-value (LTV) and loss given default (LGD). Other post-model adjustments (PMAs) mainly relate to the impact of model redevelopment or recalibration and periodic model assessment procedures that have not been incorporated in the ECL models yet; the impact on total ECL can be positive or negative. These can result from both regular model maintenance and ING’s multi-year programme to update ECL models for the definition of default. These adjustments will be removed once updates to the specific models have been implemented. The change in balance compared to the previous reporting date is due to i) released PMAs because of model updates that have been implemented, and ii) new PMAs recognised for new redevelopments and recalibrations. Analysis on sensitivity (*) The table below presents the analysis on the sensitivity of key forward-looking macroeconomic inputs used in the ECL collective-assessment modelling process and the probability weights applied to each of the three scenarios. The countries included in the analysis are the most significant geographic regions in ING and for Wholesale Banking the US is the most significant in terms of both gross contribution to reportable ECL, and sensitivity of ECL to forward-looking macroeconomics. Accordingly, ING considers these portfolios to present the most significant risk of resulting in a material adjustment to the carrying amount of financial assets within the next financial year. ING also observes that, in general, the WB business is more sensitive to the impact of forward-looking macroeconomic scenarios. The purpose of the sensitivity analysis is to enable the reader to understand the extent of the impact from the upside and downside scenario on model-based reportable ECL. In the table below, the real GDP is presented in percentage year-on-year change, the unemployment in percentage of total labour force and the house price index (HPI) in percentage year-on-year change.
Sensitivity analysis as at December 2023 (*)
|  | 2024 | 2025 | 2026 | Un-weighted ECL (€ mln) | Probability -weighting | Reportable ECL (€ mln)1
Netherlands Upside scenario | Real GDP | 1.3 | 3.3 | 2.8 | 214 | 20% | 310
Unemployment | 3.7 | 3.3 | 3.3
HPI | 10.4 | 11.2 | 4.0
Baseline Scenario | Real GDP | 0.8 | 1.6 | 1.5 | 282 | 60%
Unemployment | 4.1 | 4.3 | 4.5
HPI | 0.9 | 3.0 | 3.9
Downside scenario | Real GDP | -1.7 | -1.2 | 0.1 | 487 | 20%
Unemployment | 5.9 | 7.2 | 8.1
HPI | -10.9 | -7.4 | 3.7
Germany Upside scenario | Real GDP | 1.4 | 3.1 | 1.6 | 472 | 20% | 525
Unemployment | 2.6 | 2.0 | 1.7
HPI | 0.9 | 6.6 | 8.0
Baseline Scenario | Real GDP | 0.5 | 1.3 | 1.2 | 513 | 60%
Unemployment | 3.0 | 3.0 | 3.0
HPI | -1.4 | 3.4 | 4.5
Downside scenario | Real GDP | -2.4 | -1.4 | 0.3 | 615 | 20%
Unemployment | 4.5 | 5.2 | 5.5
HPI | -6.0 | -0.8 | 0.4
Belgium Upside scenario | Real GDP | 1.5 | 2.7 | 2.3 | 568 | 20% | 619
Unemployment | 5.3 | 5.0 | 4.9
HPI | 1.3 | 5.6 | 4.5
Baseline Scenario | Real GDP | 0.9 | 1.5 | 1.8 | 604 | 60%
Unemployment | 5.6 | 5.5 | 5.4
HPI | 0.4 | 5.2 | 3.9
Downside scenario | Real GDP | -1.3 | -0.2 | 1.2 | 713 | 20%
Unemployment | 7.3 | 8.0 | 7.9
HPI | -2.2 | 3.9 | 2.6
United States Upside scenario | Real GDP | 1.8 | 3.2 | 3.4 | 102 | 20% | 165
Unemployment | 4.1 | 3.3 | 3.1
HPI | 0.6 | 8.7 | 8.7
Baseline Scenario | Real GDP | 0.9 | 1.9 | 2.1 | 144 | 60%
Unemployment | 4.5 | 4.5 | 4.4
HPI | -0.7 | 3.5 | 3.3
Downside scenario | Real GDP | -1.3 | -1.4 | -0.1 | 292 | 20%
Unemployment | 6.6 | 8.2 | 8.8
HPI | -4.2 | -2.7 | -3.0
1 Excluding management adjustments.
Sensitivity analysis as at December 2022 (*)
|  | 2023 | 2024 | 2025 | Un-weighted ECL (€ mln) | Probability -weighting | Reportable ECL (€ mln)1
Netherlands Upside scenario | Real GDP | 2.2 | 2.3 | 2.9 | 274 | 20% | 381
Unemployment | 4.0 | 3.9 | 3.8
HPI | 13.0 | 11.8 | 2.5
Baseline Scenario | Real GDP | 0.2 | 1.4 | 1.8 | 349 | 60%
Unemployment | 4.5 | 4.8 | 4.9
HPI | 3.7 | 3.7 | 2.4
Downside scenario | Real GDP | -4.2 | 0.7 | 0.9 | 583 | 20%
Unemployment | 6.4 | 7.8 | 8.7
HPI | -8.0 | -6.5 | 2.2
Germany Upside scenario | Real GDP | 1.7 | 2.3 | 1.8 | 606 | 20% | 745
Unemployment | 2.6 | 2.2 | 1.8
HPI | 0.6 | 3.9 | 6.2
Baseline Scenario | Real GDP | -0.7 | 1.4 | 1.5 | 726 | 60%
Unemployment | 3.2 | 3.1 | 3.1
HPI | -1.8 | 0.9 | 2.7
Downside scenario | Real GDP | -4.8 | 0.1 | 1.0 | 942 | 20%
Unemployment | 4.8 | 5.3 | 5.6
HPI | -6.2 | -3.3 | -1.4
Belgium Upside scenario | Real GDP | 1.7 | 2.1 | 2.1 | 535 | 20% | 596
Unemployment | 5.5 | 5.5 | 5.3
HPI | 2.3 | 2.6 | 3.1
Baseline Scenario | Real GDP | — | 1.6 | 1.8 | 584 | 60%
Unemployment | 6.1 | 6.3 | 6.1
HPI | 1.4 | 2.2 | 2.5
Downside scenario | Real GDP | -3.2 | 1.0 | 1.5 | 692 | 20%
Unemployment | 7.5 | 8.5 | 8.4
HPI | -1.2 | 0.9 | 1.2
United States Upside scenario | Real GDP | 3.0 | 1.5 | 3.4 | 100 | 20% | 221
Unemployment | 3.4 | 2.8 | 2.5
HPI | 3.7 | 7.4 | 8.1
Baseline Scenario | Real GDP | 0.2 | 1.1 | 2.3 | 188 | 60%
Unemployment | 4.3 | 4.4 | 3.9
HPI | 2.5 | 2.2 | 2.8
Downside scenario | Real GDP | -4.1 | 0.2 | 0.6 | 442 | 20%
Unemployment | 6.4 | 7.7 | 8.2
HPI | -1.2 | -3.8 | -3.5
1 Excluding management adjustments.
When compared to the sensitivity analysis of 2022, the macroeconomic inputs for 2023 are somewhat more favourable and for 2024 less favourable as the lagged impact of monetary tightening is still feeding through to the real economy. The sensitivities for 2022 contain half-widened dispersion around the upside and downside scenarios, whereas half-widened dispersion was removed for these scenarios for the 2023 sensitivity analysis following a continued decrease in forecast uncertainty. On a total ING level, the unweighted model ECL for all collective provisioned clients in the upside scenario was €2,510 million, in the baseline scenario €2,802 million and in the downside scenario €3,668 million. This reconciles as follows to the reported ECLs:
Reconciliation of model (reportable) ECL to total ECL (*)
in € million | 2023 | 2022
Total reportable collective provisions | 2,856 | 3,209
ECL from individually assessed impairments | 2,406 | 2,439
ECL from management adjustments | 577 | 453
Total ECL | 5,839 | 6,101
Criteria for identifying a significant increase in credit risk (SICR) (*) All assets and off-balance-sheet items that are in scope of IFRS 9 impairment and which are subject to collective ECL assessment are allocated a 12-month ECL if deemed to belong in Stage 1, or a lifetime ECL if deemed to belong in Stages 2 or 3. An asset belongs in Stage 2 if it is considered to have experienced a significant increase in credit risk since initial origination or purchase. ING considers the credit risk of an asset to have significantly increased when either a threshold for absolute change in lifetime probability of default (PD) or a relative change in lifetime PD is reached. It should be noted that the lifetime PD thresholds are not the only drivers of stage allocation. An asset can also change stages as a result of other triggers, such as having over 30 days arrears, being on a watch list or being forborne. Refer to section 1.5.6 of Note 1 ‘Basis of preparation and significant accounting policies’ for an exhaustive list. Furthermore, this analysis is rudimentary in the sense that other parameters would change when an asset changes stages. Absolute lifetime PD threshold The absolute threshold is a fixed value calibrated per portfolio/segment and provides a fixed threshold that, if exceeded by the difference between lifetime PD at reporting date and lifetime PD at origination, triggers Stage 2 classification. The expert-based thresholds for the absolute change in lifetime PD vary between 75bps for Retail portfolios, 100bps for WB and 250bps for SMEs, based on the characteristics of the specific portfolio. ING is in the process of refining the thresholds on a portfolio level. These have already been implemented for part of the portfolio, resulting in calibrated instead of expert-based absolute lifetime PD thresholds. Relative lifetime PD threshold The relative threshold defines a relative increase of the lifetime PD beyond which a given facility is classified in Stage 2 because of significant increase in credit risk. The relative threshold is dependent on the individual PD assigned to each facility at the moment of origination and a scaling factor calibrated in the model development phase that is optimised depending on the observed default rates and overall average riskiness of the portfolio. While the scaling factor is associated with a whole portfolio/segment, the PD at origination is facility-specific and, in this sense, the relative threshold may differ facility by facility. Ultimately the relative threshold provides a criterion to assess whether the ratio (i.e. increase) between lifetime PD at reporting date and lifetime PD at origination date is deemed a significant increase in credit risk. If the threshold is breached, SICR is identified and Stage 2 is assigned to the given facility. The threshold for the relative change in lifetime PD is inversely correlated with the PD at origination; the higher the PD at origination, the lower the threshold. The logic behind this is to allow facilities originated in very favourable ratings to downgrade for longer without the need of a Stage 2 classification. In fact, it is likely that such facilities will still be in favourable ratings even after a downgrade of a few notches. On the contrary, facilities originated in already unfavourable ratings grades are riskier and even a single-notch downgrade might represent a significant increase in credit risk and thus a tighter threshold will be in place. Still, the relative threshold is relatively sensitive for investment grade assets while the absolute threshold primarily affects non-investment grade assets. Average threshold ratio In the table below the average increase in PD at origination needed to be classified in Stage 2 is reported, taking into account the PD at origination of the facilities included in each combination of asset class and rating quality. In terms of rating quality, assets are divided into 'investment grade' and 'non-investment grade' facilities. Rating 18 and 19 are not included in the table since facilities are not originated in these ratings and they constitute a staging trigger of their own (i.e. if a facility is ever to reach rating 18 or 19 at reporting date, it is classified in Stage 2). In the table, values are weighted by IFRS 9 exposure and shown for both year-end 2022 and year-end 2023. To represent the thresholds as a ratio (i.e. how much should the PD at origination increase in relative terms to trigger Stage 2 classification) the absolute threshold is recalculated as a relative threshold for disclosure purposes. Since breaching only relative or absolute threshold triggers Stage 2 classification, the minimum between the relative and recalculated absolute threshold is taken as value of reference for each facility.
Quantitative SICR thresholds (*)
| 2023 | 2022
Average threshold ratio | Investment grade (rating grade 1-10) | Non-investment grade (rating grade 11-17) | Investment grade (rating grade 1-10) | Non-investment grade (rating grade 11-17)
Asset class category |  |  |  |
Mortgages | 2.5 | 2.3 | 2.7 | 2.3
Consumer Lending | 2.9 | 2.1 | 2.8 | 1.8
Business Lending | 2.7 | 2.1 | 2.8 | 2.1
Governments and Financial Institutions | 3.0 | 1.9 | 3.0 | 1.9
Other Wholesale Banking | 2.8 | 1.8 | 2.8 | 1.9
As it is apparent from the disclosures above, as per ING’s methodology, the threshold is tighter the higher the riskiness at origination of the assets, illustrated by the difference between the average threshold applied to investment grade facilities and non-investment grade facilities. Sensitivity of ECL to PD lifetime PD thresholds The setting of PD threshold bands requires management judgement and is a key source of estimation uncertainty. On Group level, the total model ECL on performing assets, which is the ECL collective- assessment without taking management adjustments into account, is €1,412 million. To demonstrate the sensitivity of the ECL to these PD thresholds bands, hypothetically solely applying the upside scenario would result in total model ECL on performing assets of €1,054 million and a decrease in the Stage 2 ratio by -0.5%-percentage point, while solely applying the downside scenario in our models would result in total model ECL on performing assets of €2,290 million and an increase in the Stage 2 ratio by 3.8%-point. Market risk Introduction (*) Market risk is the risk that movements in market variables, such as interest rates, equity prices, foreign exchange rates, credit spreads and real estate prices negatively impact the bank’s earnings, capital, market value or liquidity position. Market risk either arises through positions in banking books or trading books. The banking book positions are intended to be held for the long term (or until maturity) or for the purpose of hedging other banking book positions. The trading book positions are typically held with the intention of short-term trading or to hedge other positions in the trading book. This means that financial instruments in the trading books should be free of trade restrictions. Policies and processes are in place to monitor the inclusion of positions in either the trading or banking book as well as to monitor the transfer of risk between the trading and banking books. ING recognises the importance of sound market risk management and bases its market risk management framework on the need to identify, assess, control and manage market risks. The approach consists of five recurring activities: risk identification, risk assessment, risk control, risk monitoring and risk reporting. Governance (*) A governance framework has been established defining specific roles and responsibilities of business management units, market risk management units, and internal approval bodies per activity. Supervision of market risk falls under the responsibility of the EB/MBB and is delegated to the ALCO function, where ALCO Bank is the highest approval authority and sets the market risk appetite. ALCO Bank monitors ING’s adherence to the risk appetite for market risk and sets additional limits where appropriate. These limits are cascaded through the organisation through lower level ALCOs. This ALCO structure facilitates top-down risk management, limit setting, limit monitoring and control of market risk. FR maintains a limit framework in line with ING’s Risk Appetite Framework. The businesses are responsible for adhering to the limits which are reviewed on an annual basis and are ultimately approved by the ALCO Bank. Limit excesses are reported to senior management in line with the ALM Risk Appetite Statement Setting procedure and Market Risk in the Trading Book Framework, upon which the business needs to act accordingly. To adhere to the established limit framework, ING implements hedging and risk mitigation strategies that range from the use of traditional market instruments, such as interest rate swaps, to more sophisticated hedging strategies. The organisational structure facilitates top-down risk management by recognising that risk taking and risk management occur to a large extent at the regional/local level. Bottom-up reporting from regional/local units to head office units allows each management level to assess the market risks relevant at the respective levels. Several committees govern communication between the parties involved in market risk management: •The Market Risk Model Committee (MRMC), is the dedicated authority for the approval of all funding & liquidity risk, market risk (includes banking and trading risk), counterparty credit risk and business risk models and parameters for ING Bank within its mandate delegated by ALCO Bank. •The Valuation Model Committee approves pricing models for trading and banking books. Financial Risk provides risk reporting to the EB and MBB, the ALCO Bank and the senior executive management of related business functions. The following sections elaborate on the various elements of the risk management framework for: •Market risk economic capital; •Market risk in banking books; •Market risk in trading books. Market risk in banking books (*) ING makes a distinction between the trading and banking (non-trading) books. Positions in banking books originate from the market risks inherent in commercial products that are sold to clients, Group Treasury exposures, and from the investment of our own funds (core capital). Both the commercial products and the products used to hedge related market risk exposures are intended to be held until maturity, or at least for the long term. Risk transfer (*) Market risks in the banking book are managed via the risk transfer process. In this process the interest rate, FX, funding and liquidity risks are transferred from the commercial books through matched funding or replication to Group Treasury, where centrally managed. The scheme below presents the transfer and management process of market risks in the banking books. Risk measurement (*) The main concepts and metrics used for measuring market risk in the banking book are described below per risk type. Interest rate risk in banking book (*) Interest rate risk in the banking book is defined as the exposure of a bank’s earnings, capital, and market value to adverse movements in interest rates originated from positions in the banking book. Governance (*) The management of interest rate risk follows the Interest Rate Risk in the Banking Book (IRRBB) framework as approved by ALCO Bank. This framework describes roles, responsibilities, risk metrics, and the policies and procedures related to interest rate risk management. Furthermore, ALCO Bank reviews and sets the risk appetite for interest rate risk on an annual basis. The risk appetite is translated into limits for the interest rate risk metrics. As a result of this framework, ING centralises interest rate risk management from commercial books (that capture the products sold to clients) to globally managed interest rate risk books. This enables a clear demarcation between commercial business results and results based on unhedged interest rate positions. ING distinguishes between three types of activities that generate interest rate risk in the banking book: •Investment of own funds; •Commercial business; •Group Treasury exposures including strategic interest rate positions. Group Treasury is responsible for managing the investment of own funds (core capital). Capital is invested for longer periods to contribute to stable earnings within the risk appetite boundaries set by ALCO Bank. The main objective is to maximise the economic value of the capital investment book while having stable earnings. Commercial activities can result in linear interest rate risk, for example, tenors and duration of new production and re-pricing of assets differ from those of liabilities. Also, interest rate risk can arise from customer behaviour and/or convexity risk, depending on the nature of the underlying product characteristics. Customer behaviour risk is defined as the potential future (value) loss due to deviations in the actual behaviour of clients versus the modelled behaviour with respect to the embedded options in commercial products. General sources of customer behaviour risk, among other things, include the state of the economy, competition, changes in regulation, legislation and tax regime, developments in the housing market and interest rate developments. From an interest rate risk perspective, commercial activities can typically be divided into the following main product types: savings and current accounts (funds entrusted), demand deposits, mortgages and loans. Savings and demand deposits are generally invested in such a way that both the value is hedged and the sensitivity of the margin to market interest rates is minimised. The contractual nature of investments (assets) implies that the adjustment to market rates (repricing of assets) is not immediate, therefore the interest rate risk can arise with either positive or negative impact on the income. Interest rate risk is modelled based on the stability of deposits and the pass-through rate. This takes account of different elements, such as pricing strategies, volume developments and the level and shape of the yield curve. Savings volumes are typically assumed to be relatively stable and less sensitive to rate changes. Interest rate risk for mortgages arises due to prepayment or other embedded optionalities. In modelling this risk, both interest-rate-dependent pre-payments and constant prepayments, are considered. Next to a dependence on interest rates, modelled prepayments may include other effects such as loan-to-value, seasonality and the reset date of the loan. In addition, the interest sensitivity of embedded offered rate options may be considered. Wholesale Banking loans typically do not experience interest-rate-dependent prepayment behaviour. These portfolios are match-funded taking the constant prepayment model into account and typically do not contain significant convexity risk. Wholesale Banking loans can have an all-in rate floor or a floor on a reference rate. Customer behaviour in relation to mortgages, loans, savings and demand deposits is modelled, based on extensive research. However, the substantial change in the interest rate environment makes extensive research more challenging than before and may increase model risk. Models are backtested and updated when deemed necessary in an annual procedure. Model parameters and the resulting risk measures are approved by (local) ALCO and are closely monitored on a monthly basis. Linear risk transfers take place from commercial business books to the treasury book (Group Treasury), if necessary, by using estimations of customer behaviour. The originating commercial business is ultimately responsible for estimating customer behaviour, leaving convexity risk and (unexpected) customer behaviour risk with the commercial business. Risk measurement and the risk transfer process take place at least monthly. However, if deemed necessary, additional risk transfers can take place. The commercial business manages the convexity risk that is the result of products that contain embedded options, like mortgages. Here the convexity risk is defined as the optionality effects in the value due to interest rate changes, excluding the first-order effects. In some cases, convexity risk is transferred from the commercial books to treasury books using cap/floor contracts and swaptions. In the following sections, the interest rate risk exposures in the banking books are presented. ING quantifies risk measures from both earnings and value perspectives. Net interest income (NII)-at-Risk is used to provide the earnings perspective and the net present value (NPV)-at-Risk figures provide the value perspective. Please note that the interest rate risk that stems from the commercial business is assumed to be linearly hedged but no additional corrective management actions are taken into account in the NPV-at-Risk measure. In the NII-at-Risk measure, a more dynamic hedging process is taken into account. During 2023, the following activities related to the risk measurement for IRRBB were performed: •Annual review of the risk appetite for market risks in the banking book including further enhancements; •Further assessment and development of sub-risk types, such as tenor basis risk, vega optionality risk and client behaviour risk; •Set up of standardised risk measurement related to global crisis risk; •Annual review of the interest rates scenarios used for calculating NII-at-Risk and NPV-at-Risk; •Annual savings/current account model updates; •Annual update of parameters of prepayment models for market developments; •Further enhancement of the IRRBB framework in relation to upcoming regulatory requirements (e.g. anticipation on implementation and measurement of the upcoming regulatory metric NII SOT, development of additional requirements coming forward from latest EBA guidelines); •Further strengthening of customer behaviour risk and model risk frameworks Net interest income (NII) at Risk (*) The NII-at-Risk measures the impact of changing interest rates on the forecasted net interest income (before tax) of the banking book, excluding the impacts of credit spread sensitivity, fees and fair value impact. Future projected balance sheet developments are included in this risk metric. NII-at-Risk is a metric that helps provide insight as to what extent ING's NII under alternative interest rates developments deviates from what was assumed in our dynamic plan projections. In its risk management, ING monitors the NII-at-Risk under a three-year timeframe. Interest rates are stressed during the first year versus the prevailing curve, taking gradual changes over the first year. The rate changes considered comprise both upward and downward scenarios, as well as parallel (equal movements across the yield curve) and non-parallel scenarios. The impact of changing interest rates on ING’s NII is predominantly caused by the following factors: •Change in returns of (re-)investments of client deposits; •Change in client deposit rates (mainly savings), (partially) tracking changes in market interest rates; •Change in the amortization profile of mortgages, due to an increase or decrease in expected prepayments; •Higher/lower returns of (re-)investments of capital investment; •Open interest rate positions, leading to changes in return because of different market rates; •Assumed volume development of the balance sheet in line with ING's dynamic plan. For projecting the change in client deposit rates, ING uses a client rate model that describes the relation to market interest rates and client deposit rates. The model is calibrated under a range of interest rate scenarios. Per scenario the actual change in client deposit rates may deviate from this calibrated model. The actual NII development of customer deposits may, indeed, differ from the provided scenarios, depending on, amongst others, actual interest rate and savings client rate evolution, as well as changes to ING’s balance sheet composition such as net deposit growth and relative share of savings deposits and non-remunerated current accounts. The NII-at-Risk figures in the table below reflect a parallel, linear interest rate movement during a year ('ramped') under the assumption of balance sheet developments in line with the ING's dynamic plan with a time horizon of one year. The majority of the risk comes from fixed-rate positions, most notably non- remunerated current accounts and variable-rate savings accounts. The NII-at-Risk is primarily driven by the difference in sensitivity of client liabilities, mainly savings, versus the sensitivity of client assets and investments to rate changes. The investment of own funds only impacts the earnings sensitivity marginally, as only a relatively small part has to be (re)invested within the one-year horizon.
NII-at-Risk banking book per currency - year one (*)
in € million | 2023 | 2022
| Ramped, unfloored | Ramped, unfloored
parallel ▼ | parallel ▲ | parallel ▼ | parallel ▲
By currency |  |  |  |
Euro | -165 | 155 | -119 | 114
US dollar | -12 | 12 | -1 | 2
Other | -62 | 69 | -23 | 27
Total | -239 | 236 | -142 | 142
EUR ramped (unfloored) is at +/- 110bps in 1 year (2022: +/-100bps) |  |  |  |
USD ramped (unfloored) is at +/- 110bps in 1 year (2022: +/-120bps) |  |  |  |
The change in NII under a declining and upward interest rate scenarios may not be equal. This is due to different expected reactions in prepayment behaviour of mortgages and different pricing developments of commercial loans and deposits products (mainly savings). This is caused by embedded options, explicit or implicit pricing floors and other (assumed) pricing factors. Year-on-year variance analysis (*) In our customer deposit composition current accounts decreased and term deposits increased. Over 2023 the one-year asset repricing versus liability repricing increased, leading to a higher NII-at-Risk. In 2023 central banks tightened monetary conditions, a continuation of a trend started in 2022 to counter high inflation. The interest rates, however, stabilised in the second half of the year as inflation started to ease. ING applied a dynamic hedging process, by which interest rate risk was transferred from the business to Group Treasury and subsequently hedged in the markets. This process mitigates interest rate risk resulting in a lower sensitivity for rate changes of ING's NII. However, the main drivers of a potential change of NII sensitivity are balance sheet developments, specifically relating to mortgages, loans and savings. In the eurozone, mortgage production was impacted by an increase in interest rates. Next to the impact on new production, the prepayment incentive generally decreased due to the increase in interest rates. The funds entrusted volume did not change significantly. The impact of explicit and implicit floors on both rates of client assets and savings phased out in the course of the year on the back of the interest rate increases. Pre-existing hedges, as executed by Group Treasury, were also adjusted continuously throughout the year to hedge any interest rate risk coming from higher interest rates. Furthermore, ING’s investment of own funds took place against a lower duration reducing sensitivity. Excluding Model Risk, the total NII-at-Risk remains relatively limited in comparison to ING’s total interest income. Net Present Value (NPV) at Risk (*) NPV-at-Risk measures the impact of changing interest rates on the value of the positions in the banking book. The NPV-at-Risk is defined as the outcome of an instantaneous increase or decrease in interest rates from applying currency-specific scenarios. The NPV-at-Risk asymmetry between the downward and upward shock is mainly caused by convexity risk in the mortgage and savings portfolio. The full value impact cannot be directly linked to the financial position or profit or loss account, as fair value movements in banking books are not necessarily reported through the profit or loss account or through other comprehensive income (OCI). The changes in value are expected to materialise over time in the profit and loss account if interest rates develop according to forward rates throughout the remaining maturity of the portfolio. The majority of the risk comes from the investments of own funds and from positions exhibiting negative convexity due to embedded optionality (most notably variable rate savings rate and fixed rate mortgages).
NPV-at-Risk banking books per currency (*)
in € million | 2023 | 2022
| unfloored | unfloored
parallel ▼ | parallel ▲ | parallel ▼ | parallel ▲
By currency |  |  |  |
Euro | -291 | -645 | 392 | -926
US dollar | 186 | -178 | 137 | -147
Other | 131 | -146 | 66 | -79
Total | 27 | -969 | 594 | -1,153
EUR ramped (unfloored) is at +/- 110bps (2022: +/-100bps) |  |  |  |
USD ramped (unfloored) is at +/- 110bps (2022: +/-120bps) |  |  |  |
Year-on-year variance analysis (*) The overall NPV sensitivity remained limited in 2023, reflecting of proper risk transfer and hedging process. The worst case scenario, parallel up, remained relatively unchanged. Most of the year-to-year move is coming from mortgages, partly offset by savings and derivatives from hedging activities. IBOR transition (*) In line with the recommendations of the Financial Stability Board, a fundamental review of important interest rates benchmarks has been undertaken. Some interest benchmarks have been reformed, while others have or will be replaced by risk-free rates (RFR) and discontinued. USD LIBOR in its current form ceased on 30 June 2023, whereas the cessation of GBP, CHF, JPY, and EUR LIBOR rates occurred on 31 December 2021. To support these changes, the financial sector has issued several guidance papers and other initiatives to help phase the transition. To facilitate the transition away from USD LIBOR, for new USD contracts, ING started using the recommended alternative rates based on SOFR in 2022. Also, during 2022 and 2023, ING sought to ensure that existing loan and derivative contracts were either transitioned to alternative rates or transition arrangements agreed, such as taking steps to ensure a large portion of the derivative portfolio was covered by ISDA fallbacks. Despite extensive and timely communication on the desirability of fully agreeing transition arrangements before 30 June 2023, some clients have agreed to complete the required work before the first interest reset date after cessation. A limited number of clients in restructuring or those subject to sanctions need to rely on existing fallback language or synthetic LIBOR. Due to the discontinuation of this important rate, ING, its customers, and the financial services industry have faced and continue to face a number of risks. These risks include legal, financial, operational, reputational and conduct risk. Legal risks are related to any required changes to existing transactions. Financial risks may arise due to declining liquidity and may impact a contract directly or the ability to hedge the risks in that contract. Operational risks arise due to the requirement to adapt IT systems, trade reporting infrastructure and operational processes. Conduct risk also plays a role, as renegotiation of loan contracts requires active engagement from all parties to a contract and may lead to negotiations concentrated in a period close to actual cessation. ING continues to work with the very limited number of clients that are yet to complete the USD LIBOR transition. The progress of the IBOR transitioning between 2018 and 2023 was tracked in the global ING IBOR programme, governed by business line steering committees, which reported to a central IBOR steering committee. The programme coordinated the actions necessary to manage the required changes to internal processes and systems, including pricing, risk management, legal documentation, hedge arrangements, as well as the impact on customers. The limited amount of remaining USD LIBOR contracts continues to be monitored within the commercial business lines following the closure of the bank-wide IBOR programme in November 2023. The ING Benchmark Committee continues to monitor market developments and reform plans for other rates, to anticipate the impact on any related risks. One such development concerns the plans published by the Polish National Working Group, which advises the market to be ready for a cessation of WIBOR and WIBID reference rates at the end of 2027 (originally expected in 2025, but in the last quarter of 2023 the cessation date was postponed to the end of 2027) with the offering of financial products using the new benchmark (Warsaw Interest Rate Overnight or WIRON) to progress gradually and no new products using WIBOR and WIBID beyond 1 July 2024. The WIBOR rates are used in several of our lending and derivative products, and hence a project team has been established to manage the transition. WIBOR transition is especially important for our Polish subsidiary ING Bank Slaski S.A. with a significant amount of Polish zloty-denominated assets and liabilities including derivatives that are continuously rebalanced to hedge the risk exposures. The tables below summarise the approximate gross exposures of ING that have yet to transition related to USD LIBOR and WIBOR. For WIBOR, as of 31 December 2023, they exclude exposures expiring before the transition date 31 December 2027 and of 31 December 2022 they exclude exposures expiring before the previously expected transition date 1 January 2025. Therefore, WIBOR exposures are not directly comparable between 31 December 2023 and 31 December 2022 as a consequence of the recent developments in the Polish National Working Group.
Non derivative Financial instruments to transition to alternative benchmarks (*)
in € million at 31 December 2023 | Financial Assets non- derivative | Financial Liabilities non-derivative | Off balance sheet commitments
| Carrying value | Carrying value | Nominal value
By benchmark rate |  |  |
USD LIBOR | 915 | 16 | 9
WIBOR | 18,064 |  | 1,021
Total | 18,979 | 16 | 1,030
Non derivative Financial instruments to transition to alternative benchmarks (*)
in € million at 31 December 2022 | Financial Assets non- derivative | Financial Liabilities non-derivative | Off balance sheet commitments
| Carrying value | Carrying value | Nominal value
By benchmark rate |  |  |
USD LIBOR | 30,040 | 1,637 | 7,644
WIBOR | 22,154 |  | 1,411
Total | 52,194 | 1,637 | 9,055
The total of non-derivative financial assets linked to USD LIBOR is reduced from €30,040 million on 31 December 2022 to €915 million on 31 December 2023. The majority of the non-derivative financial assets exposure on 31 December 2023 are related to contracts using synthetic USD LIBOR up until the transition to SOFR is complete during 2024. In addition, ING reduced its committed undrawn credit facilities linked to USD LIBOR from €7,644 million on 31 December 2022 to €9 million on 31 December 2023. The total of non- derivative financial liabilities linked to USD LIBOR is reduced from €1,637 million on 31 December 2022 to €16 million on 31 December 2023. The remaining non-derivative financial instruments linked to USD LIBOR are expected to transition before the next interest rate reset date. Therefore, the remaining exposure to USD LIBOR is expected to reduce further during the first half of 2024.
Derivative Financial instruments to transition to alternative benchmarks (*)
| 31 December 2023 | 31 December 2022
in € million | Nominal value | Nominal value
By benchmark rate 1 |  |
USD LIBOR | 151 | 495,318
WIBOR | 77,238 | 136,318
Total | 77,388 | 631,636
1 For cross-currency swaps all legs of the swap are included that are linked to a main IBOR that is significant to ING. In addition to the amounts in the table above, ING transitioned the interest rate swaptions that referred to the USD LIBOR ICE swap rate (nominal value on 31 December 2022: €10,810 million). The transition of these contracts was in general governed by a specific ISDA protocol. Derivative financial instruments linked to USD LIBOR were reduced from €495,318 million on 31 December 2022 to only €151 million on 31 December 2023. The majority of derivatives linked to USD LIBOR rates were transacted with clearing houses and transitioned through a standardised exercise during the second quarter of 2023. For non-centrally cleared derivatives, the main transition occurred using the ISDA IBOR fallback arrangements. The majority of the remaining derivative financial instruments linked to USD LIBOR are related to contracts using synthetic USD LIBOR and linked to non-derivative financial assets that are expected to transition before the next interest rate reset date during 2024. Therefore, a steady reduction of these last few USD LIBOR contracts is expected during the first half of 2024. Given that IBOR reform may have various accounting implications, the International Accounting Standards Board (IASB) has undertaken a two-phase project: • Phase 1 addresses those issues that affect financial reporting before the replacement of an existing benchmark. This allows ING to apply a set of temporary exceptions to continue hedge accounting even when there is uncertainty about contractual cash flows arising from the reform. Under these temporary exceptions, interbank offered rates are assumed to continue unaltered for the purposes of hedge accounting until such time as the uncertainty is resolved. • Phase 2 focuses on issues that may affect financial reporting when the existing benchmark rate is reformed or replaced. Phase 2 amendments to IFRS relate mainly to accounting for changes in the basis for determining the contractual cash flows of financial assets and liabilities due to the IBOR reform and impact on hedge accounting when an existing benchmark rate is reformed or replaced with an alternative risk-free rate. Specifically, Phase 2 amendments require that the effective interest rate on debt financial instruments is adjusted, and hedge accounting continues on transition to risk free rates, but only to the extent that the modifications made to financial instruments are those necessary to implement the IBOR reform and that the new basis for calculating cash flows is ‘economically equivalent’ to the previous basis. By applying these mandatory amendments, ING Group avoids recognising modification gains and losses on debt instruments that would otherwise be required in the absence of Phase 2 amendments (changes to debt instruments resulting from IBOR reform are treated as a reset to the instrument’s variable interest rate). In addition, ING Group avoids hedge accounting discontinuations when modifying both hedged items and hedging instruments (and related hedge documentation) as a consequence of IBOR reform that would otherwise be required in the absence of Phase 2 amendments. As explained above, Phase 1 and Phase 2 IBOR amendments to IFRS, amongst other changes, provide specific hedge accounting reliefs that allow hedge accounting relationships to continue when IBOR Reform is ongoing. Phase 1 reliefs cease to apply when uncertainty arising from IBOR reform is no longer present with respect to the timing and amount of the IBOR-based cash flows of the relevant instruments. It is ING Group’s policy to cease to apply Phase 1 reliefs when the applicable contract (either hedging instrument or hedged item) is actually modified. As a result, for these hedge accounting relationships the applicable Phase 1 reliefs ceased to apply and Phase 2 became applicable. Refer to section 1.5.4 of Note 1 ‘Basis of preparation and material accounting policy information’ for more information on the Phase 1 and Phase 2 amendments. Furthermore, hedging relationships are being amended to incorporate the new benchmark rates. During 2023, ING focused on USD LIBOR contracts and all hedging relationships have been amended to incorporate the new benchmark rates and do no longer reference USD LIBOR rates. On 31 December 2023, Phase 1 reliefs are applicable to WIBOR indexed fair value and cash flow hedge accounting relationships as there is uncertainty arising from the WIBOR reform with respect to the timing and the amount of the underlying cash flows that the Group is exposed to. Therefore, for WIBOR financial instruments designated in hedge accounting the applicable Phase 1 reliefs will continue to apply until the relevant contract is modified. At that point in time, Phase 2 reliefs will become applicable. For these affected fair value and cash flow hedge relationships ING assumes that the WIBOR based cash flows from the hedging instrument and hedged item will remain unaffected. The same assumption is used to assess the likelihood of occurrence of the forecast transactions that are subject to cash flow hedges. The hedged cash flows in cash flow hedges directly impacted by the WIBOR reform still meet the highly probable requirement, assuming the WIBOR benchmark on which the hedged cash flows are based is not altered as a result of the reform. The total gross notional amounts of hedging instruments that are used in the ING's hedge accounting relationships for which the Phase 1 amendments to IAS39 were applied are:
Notional amounts of Hedging instruments (*)
| 31 December 2023 | 31 December 2022
in € million | Nominal value | Nominal value
By benchmark rate |  |
USD LIBOR |  | 28,316
WIBOR | 89,338 | 57,774
As at 31 December 2023, all USD LIBOR hedging relationships have been amended to incorporate the new benchmark rates and do no longer reference USD LIBOR rates. At 31 December 2022 approximately 89% of the notional amounts for USD LIBOR had a maturity date beyond the transition date 30 June 2023. Approximately 29% of the notional amounts for WIBOR have a maturity date beyond the new transition date 31 December 2027. At 31 December 2022, approximately 71% of the notional amounts for WIBOR had a maturity date beyond the previously expected transition date of 1 January 2025. The WIBOR amounts are not directly comparable between 31 December 2023 and 31 December 2022 as a consequence of the recent postponement of the cessation of WIBOR by the Polish National Working Group. The notional amounts of the derivative hedging instruments provide a close approximation of the extent of the risk exposure ING manages through these hedging relationships. Credit spread risk in banking books (*) Credit spread risk is defined as risk driven by the changes of the market price for credit risk, for liquidity and potentially other characteristics of credit-risky instruments, which is not captured by another existing prudential framework such as IRRBB or by expected credit/(jump-to-) default risk. EBA guidelines The updated guidelines on credit spread risk in the banking book (CSRBB) became effective on 31 December 2023. Following the EBA guidelines, the scope in terms of positions and metrics has been reviewed and updated during 2023. Metrics used are NPV-at-Risk, NII-at-Risk and Market Value Changes-at Risk and view the positions across different accounting treatments. Governance The management of credit spread risk follows the CSRBB framework as approved by ALCO Bank. This framework describes roles, responsibilities, risk metrics, and the policies and procedures related to credit spread risk management. Furthermore, ALCO Bank reviews and sets the risk appetite for credit spread risk on an annual basis. The risk appetite is translated into limits for the risk metrics. Foreign exchange (FX) risk in banking books (*) FX exposures in banking books result from core banking business activities (business units doing business in currencies other than their base currency), foreign currency investments in subsidiaries (including realised net profit and loss), and strategic equity stakes in foreign currencies. The policy regarding these exposures is briefly explained below. Governance – Core banking business (*) Every business unit hedges the FX risk resulting from core banking business activities into its base currency to prevent volatility in profit and loss. Consequently, assets and liabilities are matched in terms of currency, within certain friction limits. Governance – FX translation result (*) ING’s strategy is to protect the CET1 ratio against adverse impact from FX rate fluctuations, while limiting the volatility in the profit and loss account due to this CET1 hedging and limiting the RWA impact under the regulatory framework. Hedge accounting is applied to the largest extent possible. Taking this into account, the CET1 ratio hedge can be achieved by deliberately taking foreign currency positions equal to certain target positions, such that the CET1 capital and risk-weighted assets are equally sensitive in relative terms to changing FX rates. Risk profile – FX translation result (*) The following table presents the currency exposures in the banking books for the most important currencies for the FX translation result. Positive figures indicate long positions in the respective currency. As a result of the strategy to hedge the CET1 ratio an open structural FX exposure exists. To measure the volatility of the CET1 ratio from FX rate fluctuations, different metrics are used including the CET1 Ratio-at-Risk. The impact is controlled via the Solvency and Market Risk Banking Book RAS.
Foreign currency exposures banking books (*)
in € million | Foreign Investments | Hedges | Net exposures
| 2023 | 2022 | 2023 | 2022 | 2023 | 2022
US Dollar | 10,337 | 10,470 | -3,416 | -2,376 | 6,921 | 8,093
Pound Sterling | 1,659 | 1,364 | -156 | -58 | 1,503 | 1,306
Polish Zloty | 3,976 | 2,714 | -1,254 | -321 | 2,722 | 2,393
Australian Dollar | 3,620 | 3,781 | -2,273 | -2,673 | 1,346 | 1,108
Turkish Lira | 517 | 750 | 0 | 0 | 517 | 750
Chinese Yuan | 1,815 | 1,871 | -348 | 0 | 1,466 | 1,871
Russian Rouble | 375 | 391 | 0 | 0 | 375 | 392
Romanian Leu | 895 | 860 | -134 | -154 | 761 | 706
Thai Baht | 1,128 | 1,109 | -697 | -699 | 431 | 410
Other currency | 3,704 | 3,771 | -2,897 | -2,908 | 806 | 863
Total | 28,024 | 27,081 | -11,175 | -9,189 | 16,849 | 17,892
*The FX sensitivity is expressed as the FX spot equivalent position. Equity price risk in banking books (*) Governance (*) ING maintains a portfolio with substantial equity exposure in its banking books. Local offices are responsible for the management of the equity investment positions. Financial Risk is responsible for monitoring the regulatory capital for equity investments on a monthly basis and acts independently from ING/local management when monitoring these positions. Risk profile (*) Equity price risk arises from the possibility that an equity security’s price will fluctuate, affecting the values of the equity security itself as well as other instruments whose values react similarly to the particular security, a defined basket of securities, or a securities index. ING’s equity exposure mainly consists of the investments in associates and joint ventures of €1,509 million (2022: €1,500 million) and equity securities held at fair value through other comprehensive income (FVOCI) of €1,885 million (2022: €1,887 million).The value of equity securities held at FVOCI is directly linked to equity security prices with increases/decreases being recognised in the revaluation reserve. Investments in associates and joint ventures are measured in accordance with the equity method of accounting and the balance sheet value is therefore not directly linked to equity security prices. The equity sensitivity is expressed as the equity position. Year-on-year variance analysis (*) In 2023, the revaluation reserve equity securities decreased by €34 million from €1,187 million to €1,152 million mainly due to revaluation of the shares in Bank of Beijing with €-24 million. In 2023, the equity securities at fair value through OCI decreased slightly by €2 million.
Revaluation reserve equity securities at fair value through other comprehensive income (*)
in € million | 2023 | 2022
Positive re-measurement | 1,158 | 1,190
Negative re-measurement | -6 | -4
Total | 1,152 | 1,187
Market risk in trading books (*) Within the trading portfolios, the positions are maintained in the financial markets. These positions are often a result of transactions with clients and may benefit from short-term price movements. In 2023, ING continued its strategy of undertaking trading activities to develop its client-driven franchise and deliver a differentiating experience by offering multiple market and trading products. Governance (*) The Financial Markets Risk Committee (FMRC) is the market risk committee that, within the risk appetite set by the ALCO Bank, sets the market risk limits both on an aggregated level and on a desk level. The FMRC has delegated authority from ALCO Bank for the management of market risk related to all trading and banking book activities within Financial Markets (FM). Financial Risk/ Trading Risk Management (FR/ TRM) advises both FMRC and ALCO Bank on the market risk appetite of the trading activities. With respect to the trading portfolios, TRM focuses on the management of market risks of Wholesale Banking (mainly Financial Markets) as this is the only business line within ING where trading activities take place. Trading activities include facilitation of client business and market making. TRM is responsible for the development and implementation of trading risk policies and risk measurement methodologies, and for reporting and monitoring risk exposures against approved trading limits. TRM also reviews trading mandates and global limits, and performs the gatekeeper role in the product review process (PARP). Risk measurement (*) ING uses a comprehensive set of methodologies and techniques to measure market risk in trading books: Value at Risk (VaR) and Stressed Value at Risk (SVaR), Incremental Risk Charge (IRC), and stress testing. Systematic validation processes are in place to validate the accuracy and internal consistency of data and parameters used for the internal models and modelling processes. Value at Risk (*) TRM uses the historical simulation VaR methodology (HVaR) as its primary risk measure. The HVaR for market risk quantifies, with a one-sided confidence level of 99 percent, the maximum overnight loss that could occur in the trading portfolio of ING due to changes in risk factors (e.g. interest rates, equity prices, foreign exchange rates, credit spreads, implied volatilities) considering the positions remains unchanged for a time period of one day. Next to general market movements in these risk factors, HVaR also takes into account market data movements for specific moves in, for example, the underlying issuer or securities. A single model which diversifies general and specific risk is used. In general, a full revaluation approach is applied, while for a limited number of linear trading positions and risk factors in commodity and equity risk classes a sensitivity- based approach is applied. The potential impact of historical market movements on today’s portfolio is estimated, based on equally weighted observed market movements of the previous year (260 business days). When simulating potential movements in risk factors, depending on the risk factor type, either an absolute or a relative shift is used. The data used in the computations is updated daily. ING uses HVaR with a one-day horizon for internal risk measurement, management control, and backtesting, and HVaR with a 10-day horizon for determining regulatory capital. To compute HVaR with a 10-day horizon, the one-day risk factor shifts are scaled by the square root of 10 and then used as an input for the revaluation. The same model is used for all legal entities within ING with market risk exposure in the trading portfolio. Limitations (*) HVaR has some limitations: HVaR uses historical data to forecast future price behaviour, but future price behaviour could differ substantially from past behaviour. Moreover, the use of a one-day holding period (or 10 days for regulatory capital calculations) assumes that all positions in the portfolio can be liquidated or hedged in one day. In periods of illiquidity or market events, this assumption may not hold. Also, the use of a 99 percent confidence level means that HVaR does not take into account any losses that occur beyond this confidence level. Backtesting (*) Backtesting is a technique for the ongoing monitoring of the plausibility of the HVaR model in use. Although HVaR models estimate potential future trading results, estimates are based on historical market data. In a backtest, the actual daily trading result (excluding fees and commissions) is compared with the one-day HVaR. In addition to using actual results for backtesting, ING also uses hypothetical results, which exclude the effects of intraday trading, fees, and commissions. When an actual or a hypothetical loss exceeds the HVaR, an ‘outlier’ occurs. Based on ING’s one-sided confidence level of 99 percent, an outlier is expected once in every 100 business days. On an overall level in 2023, there was one outlier for hypothetical P&L and zero outliers for actual P&L. The hypothetical outliers occurred during the past year concentrated in the first quarter of 2023, mainly due to the increased market volatility arising from major American banks being collapsed. Stressed HVaR (*) The stressed HVaR (SVaR) is intended to replicate the HVaR calculation that would be generated on the bank’s current portfolio with inputs calibrated to the historical data from a continuous 12-month period of significant financial stress relevant to the bank’s portfolio. To calculate SVaR, ING uses the same model that is used for 1DHVaR, with a 10-day horizon. The data for the historical stress period used currently includes the height of the credit crisis around the fall of Lehman Brothers (2008-2009), and this choice is reviewed regularly. The historical data period is chosen so that it gives the worst scenario loss estimates for the current portfolio. The same SVaR model is used for management purposes and for regulatory purposes. The same SVaR model is used for all legal entities within ING with market risk exposure in the trading portfolio. Incremental risk charge (*) The incremental risk charge (IRC) for ING is an estimate of the default and migration risks for credit products (excluding securitisations) in the trading book, over a one-year capital horizon, with a 99.9% confidence level. Trading positions (excluding securitisations) of ING, which are subject to specific interest rate risk included in the internal model approach for market risk regulatory capital, are in scope of the IRC model. By model choice, equity is excluded from the model. For the calculation of IRC, ING performs a Monte Carlo simulation based on a multi-factor t-copula. In the multi-factor IRC model the supervisory asset correlations are no longer applicable and the calibration of the correlations is based on historical market data. The rating change is simulated for all issuers over the different liquidity horizons (i.e. time required to liquidate the position or hedge all significant risks) within one year. Movements across different rating categories and probabilities of default are governed by a credit-rating transition matrix. An internal transition matrix along with internal LGDs is used, to comply with the consistency requirement. The financial impact is then determined for the simulated migration to default, or for the simulated migration to a different rating category, based on LGD or credit spread changes, respectively. The liquidity horizon has been set to the regulatory minimum of three months for all positions in scope. ING reviews the liquidity horizons on a yearly basis, based on a structured assessment of the time it takes to liquidate the positions in the trading portfolio. Stress testing and event risk (*) Stress testing is a valuable risk management tool. In addition to the bank-wide stress test framework as described in the stress-testing section, Trading Risk Management performs stress tests specific to the trading book with various frequencies. ING went live with a new Trading Book Stress Testing model in 2023, replacing the previously used Event Risk model. With the new model, the trading book stress tests evaluate the impact on the bank’s trading book under severe but plausible stress scenarios, using full revaluation approach. The framework is based on historical as well as hypothetical scenarios. The stress result is an estimate of the profit and loss caused by a potential event and its worldwide impact for ING. The results of the stress tests are used for decision-making aimed at maintaining a financially healthy going-concern institution after a severe event occurs. In stress scenarios, shocks are applied to prices (credit spreads, interest rates, equity, commodities, and FX rates) and volatilities. Depending on the type of the stress test, additional scenario assumptions can be made, for example on correlations, dividends, or recovery rates. The structural scenarios are defined to cover market moves in various directions and capture different asset class correlations. Scenarios are calculated using full revaluation approach. The worst scenarios are determined for each product line, business line and super business line, and compared against limits. Sensitivities (*) As part of the risk monitoring framework, TRM actively monitors the sensitivities of the trading portfolios. Sensitivities measure the impact of movements in individual market risk factors (foreign exchange rates, interest rates, credit spreads, equity and commodity prices) on profit and loss results of the trading positions and portfolios. The following tables show the five largest trading positions in terms of sensitivities to foreign exchange, interest rate and credit spread risk factor movements. These largest exposures also reflect concentrations of risk in FX risk per currency, interest rate risk per currency, and credit spread risk per country, rating and sector. Due to the nature of the trading portfolios, positions in the portfolios can change significantly from day to day, and sensitivities of the portfolios can change daily accordingly.
Most important foreign exchange year-end trading positions (*)
in € million | 2023 |  | 2022
Foreign exchange |  | Foreign exchange |
Japanese Yen | 61 | US Dollar | -187
Taiwan Dollar | -58 | Romanian Leu | 88
Romanian Leu | 58 | Japanese Yen | 86
Chinese Yuan | 49 | Chinese Yuan | 32
Hong Kong Dollar | -38 | South Korean Won | 28
Most important interest rate and credit spread sensitivities at year-end (*)
in € thousand | 2023 |  | 2022
Interest Rate (BPV) 1 |  | Interest Rate (BPV) 1 |
Euro | -309 | Euro | -334
Czech koruna | 71 | British Pound | -95
Korean Won | -41 | US Dollar | -79
US Dollar | -40 | Taiwan Dollar | 67
British Pound | -35 | Japanese Yen | 63
Credit Spread (CSO1) 2 |  | Credit Spread (CSO1) 2 |
Germany | 405 | Netherlands | 162
Netherlands | 120 | United States | 151
Korea | -111 | Japan | 102
Japan | 106 | France | 88
United Kingdom | 101 | Germany | 87
1 Basis point value (BPV) measures the impact on value of a one basis point increase in interest rates. 2 Credit Spread Sensitivity (CS01) measures the impact on value of a one basis point increase in credit spreads. Exposures to supranational institutions are not assigned to a specific country.
Credit spread sensitivities per risk class and sector at year-end (*)
| 2023 | 2022
in € thousand | Corporate | Financial Institutions | Corporate | Financial Institutions
Credit Spread (CSO1) 1 |  |  |  |
Risk classes |  |  |  |
1 (AAA) | 0 | 0 | 2 | -1
2–4 (AA) | 12 | 50 | -1 | -7
5–7 (A) | 57 | 50 | 154 | -13
8–10 (BBB) | 106 | 13 | 249 | -11
11–13 (BB) | 25 | -25 | 7 | 7
14–16 (B) | 17 | -4 | 23 | -4
17–22 (CCC and NPL) | -8 | -20 | 3 | -7
Not rated | 0 | 0 | 0 | 0
Total | 208 | 65 | 437 | -36
1 Credit Spread Sensitivity (CS01) measures the impact on value of a 1 basis point increase in credit spreads.
Funding and liquidity risk (*) Introduction (*) Funding and liquidity (F&L) risk is the risk that ING or one of its subsidiaries cannot meet their financial liabilities upon their maturity date at a reasonable cost and in a timely manner. ING incorporates funding and liquidity risk management in its business strategy, and has established a funding and liquidity risk framework to manage these risks within predefined boundaries. A high-level overview of the F&L framework is provided in the next figure. Governance (*) Funding and liquidity risk management within ING falls under the supervision of the ALCO Bank function, which approves the funding and liquidity risk appetite and subsequently cascades it throughout the organisation. ALCO Bank has delegated the responsibilities of the Internal Capital and Liquidity Adequacy Assessment Process (ICLAAP) and documents, as per the ICLAAP framework of ING, to the ICLAAP Committee. The ICLAAP Committee therefore focuses on technical liquidity documents and oversees business processes and deliverables concerning the Internal Liquidity Adequacy Assessment Process (ILAAP). The EB, MBB, and staff departments from the CRO and CFO domains, as well as Group Treasury, have oversight of, and are responsible for, managing funding and liquidity risks. ING’s funding and liquidity risk governance is based on the three-lines-of-defence structure. This sets a clear division of responsibilities as well as an independent risk control challenge process. Group Treasury and the business lines have the first-line-of-defence functions, which include management of ING’s (regulatory) liquidity and funding position, maintaining access to the professional funding markets, and managing the liquidity buffer. Financial Risk, as the second line of defence, is responsible for developing and maintaining ING’s policies, standards and guidelines on F&L risk management, as well as for setting the F&L risk appetite through stress testing and other risk measurement activities. The Finance function is responsible for management information and regulatory reporting related to funding and liquidity risk management. As the third line of defence, Corporate Audit Services is responsible for independently assessing the design, effectiveness, and implementation of the Funding & Liquidity framework.
Funding and liquidity management strategy and objectives (*) The main objective of ING’s funding and liquidity risk management is to maintain sufficient liquidity to fund the commercial activities of ING, both under normal and stressed market circumstances across various locations, currencies, and tenors. ING’s funding consists mainly of retail and corporate deposits contributing around 50 percent and 25 percent of total funding, respectively. These funding sources provide a relatively stable funding base. The remainder of the required funding is attracted primarily through a combination of long-term and short-term professional funding. Group Treasury manages the professional funding in line with the F&L risk appetite, with the aim of ensuring a sufficiently diversified and stable funding base. Funding and liquidity adequacy and risk appetite (*) ING identifies key drivers of short-term and future liquidity and funding needs on an ongoing basis through the periodic risk identification process. Taking into consideration the identified risk drivers, ING regularly assesses its current and future liquidity adequacy and, if deemed necessary, takes action to further improve ING’s liquidity position and maintain sufficient counterbalancing capacity. A liquidity adequacy statement is formulated on a quarterly basis to substantiate and reflect the management view on the current funding and liquidity position as well as the potential future challenges. The quarterly liquidity adequacy statement is an important part of ING’s ILAAP process. Additionally, ING completes ad-hoc funding and liquidity assessments if deemed necessary. Following the banking turmoil in March 2023, ING completed a lessons-learned deep-dive. This deep-dive included peer and internal analysis of deposit structures and liquidity buffers, and a review of the influence of mobile banking apps and social media on financial institutions. The F&L risk appetite subsequently incorporated elements of this analysis, as well as the impacts of quantitative tightening, TLTRO III redemptions, increased competition for savings in the higher-interest-rate-environment and potentially higher deposit outflows in certain countries, in the 2023 review. ING assesses its F&L adequacy through three lenses – stress, economic and normative: •Through the stress lens, ING evaluates its ability to withstand periods of prolonged F&L stress for both normative and economic requirements or limits under idiosyncratic, market-related, a combination of idiosyncratic and market-related and climate risk scenarios, which lead to customer deposit outflows, deterioration of access to funding markets, and lower liquidity value of counterbalancing capacity. •Through the economic lens, ING assesses the extent to which its customers, professional counterparties and investors are comfortable to provide deposits and funding in the tenors, currencies, and instruments necessary to sustainably fund the business (intraday, short-term and long-term) in a going-concern situation. •Through the normative lens, ING ascertains that the bank is in the position to meet current and future home and host regulatory requirements. For each lens, ING has established a related set of risk appetite statements, which define ING’s risk appetite commensurate with the principles of liquidity adequacy. These risk appetite statements are summarised in the following illustration:
The F&L risk appetite statements are translated into a number of metrics with appropriate boundaries and instruments, which are used to regularly measure and manage ING’s funding and liquidity risk. The risk appetite with respect to the stress lens aims to have sufficient counterbalancing capacity under various internally defined stress scenarios. Regarding the economic perspective, an internally defined stable funding to loans (SFtL) ratio and stable funding surplus metric (supplemented by other metrics) is used to stimulate a diversified funding base and to prevent overreliance on professional funding. Finally, the liquidity coverage ratio (LCR) and the net stable funding ratio (NSFR) regulatory metrics are monitored in terms of both ING’s risk appetite and normative requirements. The macroeconomic and market environment are also important considerations in ING’s funding and liquidity framework. The macroeconomic environment comprises various exogenous factors over which ING has no control, but which may have a material impact on ING’s F&L position. The main macroeconomic factors analysed on a regular basis include: •Performance of global and local economic performance e.g. shifts in GDP, inflation rates, unemployment rates, and public deficit/surplus; •Developments and risks arising from geopolitical tensions and related trends; •Monetary policy with a focus on the alternative monetary measures employed by central banks in recent years as a result of the global energy crisis and the recent period of high inflation; •Regulatory requirements; e.g. understanding the changing regulatory landscape as well as the impact of ING’s actions on existing regulatory boundaries. The strategic ambitions of ING, together with the design and execution of the funding plan, are assessed under both current and projected market conditions. An emphasis is placed on understanding overall market trends and developments, credit rating changes, and peer comparisons. Liquidity stress-testing (*) Funding and liquidity stress-testing forms part of the overall F&L framework. It allows ING to examine the effects of exceptional but plausible future events on ING’s funding and liquidity position. It also provides insight into which entities, business lines or portfolios are vulnerable to which types of risk drivers or scenarios. The stress-testing framework encompasses the funding and liquidity risks of the consolidated balance sheet of ING Group, including all entities, business lines as well as on-, and off-balance-sheet positions. The net liquidity position (NLP) is the main stress-testing measure and is measured at different time buckets. The stress-testing framework considers idiosyncratic, market-wide, combined (idiosyncratic and market- wide), and climate and environmental stress scenarios. The design of the framework is based on empirical evidence supplemented by expert judgement. The framework can be extended to additional ad-hoc scenarios. For example, it can be used as input for firm-wide stress testing and reverse stress testing. Outcomes of the stress tests are considered in the key aspects of ING’s F&L risk framework and F&L risk management, including: •Risk Appetite Framework (through risk appetite statements); •Risk identification and assessment; •Monitoring of the liquidity and funding position; •Business actions (if needed); •Contingency funding plan; •Early-warning indicators. The funding and liquidity stress-testing framework is also subject to regular internal validation by model validation. In line with supervisory expectations, ING’s liquidity position is stress tested on at least a monthly basis using scenarios that form part of the F&L risk appetite statement. The results of all internal stress scenarios are monitored and assessed on a regular basis. In addition, ad-hoc scenarios based on current economic and market developments are run to determine their potential impacts on the funding and liquidity position of ING. In 2023, this included stress test scenarios dedicated to the impact of rapid deposit outflows on the bank, as well as a shutdown of short and long-term funding markets. The internal stress scenarios also serve as input in the decision on additional contingency measures. Contingent F&L risks are addressed in the contingency capital and funding plan with a focus on early- warning indicators as well as organisation and planning of liquidity management in times of stress. The contingency funding measures are developed in conjunction with the ING recovery plan and are reviewed and tested on a regular basis

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