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Risk management | 12 Months Ended
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Dec. 31, 2024
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 the 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.
Credit & counterparty risk categories (*) In the following table the different types of credit and counterparty risk categories are described and a reconciliation with the notes in the financial statements is also included:
Reconciliation between credit & counterparty risk categories and financial position (*)
Credit risk categories | Notes in the financial statements
Lending risk: 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. | 2 | Cash and balances with central banks
3 | Loans and advances to banks
4 | Financial assets at fair value through profit or loss
5 | Financial assets at fair value through other comprehensive income
7 | Loans and advances to customers
40 | Contingent liabilities and commitments
Investment risk: 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. | 4 | Financial assets at fair value through profit or loss
5 | Financial assets at fair value through other comprehensive income
6 | Debt securities
Money market risk: This 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. | 2 | Cash and balances with central banks
3 | Loans and advances to banks
7 | Loans and advances to customers
Pre-settlement risk: This 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). | 4 | Financial assets at fair value through profit or loss
14 | Financial liabilities at fair value through profit or loss
39 | Offsetting financial assets and liabilities
Settlement risk: This 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. | 4 | Financial assets at fair value through profit or loss
11 | Other assets
14 | Financial liabilities at fair value through profit or loss
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 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 and is expressed in quantitative and qualitative measures. 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 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). 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/CRD, 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); and •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, and 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 expert input, with the ratings being manually updated at least annually. More frequent reviews (e.g. quarterly) are performed where considered necessary. In line with evolving regulatory expectations on models and emerging industry practices, ING has embarked on 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. Overall portfolio (*) During 2024, ING’s portfolio size increased by €30.3 billion (3.2%) to €961.9 billion outstanding. Foreign exchange rate changes had a positive impact on the portfolio growth, mainly in WB, increasing total outstanding by €8.0 billion, driven by the appreciation of the US dollar against the euro. Retail banking increased by €18.9 billion mainly due to underlying growth in residential mortgages. Rating distribution (*) Overall, the rating class distribution remained stable in 2024. The share of investment grade rating classes increased from 76.8% to 78.1%, while the share of non-investment grade decreased from 21.2% to 19.9%. Performing restructuring outstandings decreased from 0.7% to 0.6% of the total portfolio, whereas non-performing loans increased from 1.3% to 1.4%. With respect to the rating distribution within the business lines, in WB, investment grade increased to 83.7% from 81.2%, while non-investment grade exposures decreased to 14.6% from 17.1% compared to 2023. Performing restructuring assets decreased from 0.7% to 0.6% of total Wholesale Banking assets where non-performing loans for WB increased from 1.0% to 1.2%. The non-performing loans (NPL) increase in Wholesale Banking is mainly caused by Russian exposures as well as a few large NPLs that are unrelated in terms of asset class, sector or geography. For Retail Banking, investment grade increased to 73.3% from 73.0%, while non-investment grade exposures decreased to 24.4% from 24.9% as compared to 2023. Performing restructuring increased to 0.7% from 0.6% whereas NPL remained constant at 1.5% in 2024. Industry (*) The industry breakdown is presented in accordance with the NAICS definition. The increase of €30.3 billion in total volume during 2024 was mainly due to the increase in Private Individuals (€18.4 billion), Non-Bank Financial Institutions (€8.8 billion) and Commercial Banks (€5.2 billion). The share of Private Individuals increased from 38.8% last year to 39.5%. Portfolio analysis per business line (*)
Outstandings per line of business (*)1, 2, 3
in € million | Wholesale Banking | Retail Banking | Corporate line | Total
Rating class | 2024 | 2023 | 2024 | 2023 | 2024 | 2023 | 2024 | 2023
Investment grade | 1 (AAA) | 53,363 |  | 52,665 |  | 29,151 |  | 34,683 |  | 1,790 |  | 2,284 |  | 84,304 |  | 89,631 |
2-4 (AA) | 72,462 |  | 67,034 |  | 63,187 |  | 59,655 |  | 4 |  | 6 |  | 135,653 |  | 126,694 |
5-7 (A) | 101,766 |  | 95,320 |  | 140,479 |  | 132,076 |  | 154 |  | 147 |  | 242,400 |  | 227,543 |
8-10 (BBB) | 129,429 |  | 123,081 |  | 155,375 |  | 146,295 |  | 3,662 |  | 2,357 |  | 288,466 |  | 271,733 |
Non-investment grade | 11-13 (BB) | 53,757 |  | 57,348 |  | 94,753 |  | 94,408 |  |  |  |  |  | 148,510 |  | 151,756 |
14-16 (B) | 7,396 |  | 12,234 |  | 31,165 |  | 29,330 |  |  |  |  |  | 38,561 |  | 41,565 |
17 (CCC) | 1,037 |  | 1,122 |  | 3,345 |  | 3,113 |  | 170 |  | 392 |  | 4,552 |  | 4,628 |
Performing Restructuring loans | 18 (CC) | 1,792 |  | 2,523 |  | 2,001 |  | 1,957 |  |  |  |  |  | 3,794 |  | 4,481 |
19 (C) | 560 |  | 535 |  | 1,760 |  | 1,313 |  |  |  |  |  | 2,321 |  | 1,848 |
Non-performing loans | 20-22 (D) | 5,204 |  | 4,051 |  | 8,100 |  | 7,622 |  |  |  |  |  | 13,303 |  | 11,673 |
Total | 426,767 |  | 415,914 |  | 529,317 |  | 510,452 |  | 5,779 |  | 5,186 |  | 961,863 |  | 931,552 |
Industry |  |  |  |  |  |  |  |  |
Private Individuals |  | 2,116 |  | 2,330 |  | 377,712 |  | 359,057 |  |  |  |  |  | 379,827 |  | 361,387 |
Central Banks |  | 61,091 |  | 70,139 |  | 15,044 |  | 21,740 |  | 1,785 |  | 2,269 |  | 77,919 |  | 94,147 |
Natural Resources |  | 39,974 |  | 40,511 |  | 1,925 |  | 1,883 |  |  |  |  |  | 41,899 |  | 42,394 |
Real Estate |  | 24,643 |  | 24,904 |  | 28,738 |  | 26,611 |  |  |  |  |  | 53,381 |  | 51,515 |
Commercial Banks |  | 40,962 |  | 37,342 |  | 6,662 |  | 6,183 |  | 3,616 |  | 2,515 |  | 51,240 |  | 46,040 |
Non-Bank Financial Institutions |  | 64,217 |  | 55,313 |  | 2,212 |  | 2,290 |  | 290 |  | 286 |  | 66,719 |  | 57,889 |
Central Governments |  | 48,389 |  | 45,316 |  | 8,107 |  | 7,304 |  | 1 |  | 1 |  | 56,497 |  | 52,621 |
Transportation & Logistics |  | 27,499 |  | 27,106 |  | 6,037 |  | 5,784 |  |  |  |  |  | 33,536 |  | 32,890 |
Utilities |  | 25,517 |  | 23,324 |  | 2,196 |  | 2,184 |  |  |  |  |  | 27,713 |  | 25,509 |
Food, Beverages & Personal Care |  | 13,827 |  | 13,503 |  | 10,419 |  | 9,883 |  |  |  |  |  | 24,246 |  | 23,386 |
Services |  | 8,844 |  | 9,128 |  | 13,442 |  | 12,872 |  | 27 |  | 24 |  | 22,312 |  | 22,023 |
General Industries |  | 10,512 |  | 12,039 |  | 8,812 |  | 9,086 |  |  |  |  |  | 19,324 |  | 21,126 |
Lower Public Administration |  | 6,959 |  | 6,211 |  | 19,598 |  | 17,493 |  |  |  |  |  | 26,557 |  | 23,704 |
Other | 52,218 |  | 48,748 |  | 28,412 |  | 28,082 |  | 62 |  | 92 |  | 80,691 |  | 76,922 |
Total | 426,767 |  | 415,914 |  | 529,317 |  | 510,452 |  | 5,779 |  | 5,186 |  | 961,863 |  | 931,552 |
Outstandings per line of business (*) - continued 1, 2, 3
in € million | Wholesale Banking | Retail Banking | Corporate line | Total
Region | 2024 | 2023 | 2024 | 2023 | 2024 | 2023 | 2024 | 2023
Europe | Netherlands | 44,421 |  | 54,938 |  | 164,590 |  | 156,182 |  | 1,903 |  | 2,366 |  | 210,913 |  | 213,486 |
Belgium | 26,506 |  | 24,171 |  | 95,584 |  | 91,744 |  |  |  | 7 |  | 122,091 |  | 115,921 |
Germany | 27,443 |  | 26,152 |  | 128,598 |  | 128,885 |  | 30 |  | 31 |  | 156,071 |  | 155,067 |
Poland | 21,190 |  | 20,346 |  | 30,946 |  | 28,971 |  |  |  |  |  | 52,136 |  | 49,317 |
Spain | 11,990 |  | 11,047 |  | 28,507 |  | 27,158 |  | 36 |  | 35 |  | 40,533 |  | 38,240 |
United Kingdom | 28,257 |  | 28,587 |  | 265 |  | 275 |  | 91 |  | 112 |  | 28,613 |  | 28,974 |
Luxembourg | 26,176 |  | 23,805 |  | 5,139 |  | 5,557 |  |  |  |  |  | 31,314 |  | 29,363 |
France | 24,351 |  | 21,528 |  | 3,122 |  | 3,108 |  | 3 |  | 14 |  | 27,476 |  | 24,650 |
Rest of Europe | 72,860 |  | 65,157 |  | 23,203 |  | 20,368 |  | 14 |  | 32 |  | 96,076 |  | 85,558 |
America | 86,402 |  | 78,851 |  | 2,402 |  | 2,042 |  | 232 |  | 222 |  | 89,037 |  | 81,114 |
Asia | 44,136 |  | 49,851 |  | 215 |  | 159 |  | 3,464 |  | 2,365 |  | 47,815 |  | 52,374 |
Australia | 10,887 |  | 9,409 |  | 46,723 |  | 45,977 |  | 8 |  | 2 |  | 57,618 |  | 55,389 |
Africa | 2,148 |  | 2,071 |  | 22 |  | 26 |  |  |  |  |  | 2,170 |  | 2,098 |
Total | 426,767 |  | 415,914 |  | 529,317 |  | 510,452 |  | 5,779 |  | 5,186 |  | 961,863 |  | 931,552 |
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. Portfolio analysis per geographical area (*) The portfolio analysis per geographical area re-emphasises the international distribution of ING’s credit portfolio. The Netherlands maintains the largest portfolio share in a single country with 21.9% (2023: 22.9%) of the total amount, followed by Germany with 16.2% (2023: 16.6%) and Belgium with 12.7% (2023: 12.4%). In terms of region, the majority of the portfolio balance remained in Europe with 80.0% (2023: 79.0%), followed by Americas with 9.3% (2023: 8.7%) and Australia with 6.0% (2023: 5.9%). The top five countries within Rest of Europe based on outstandings were Italy (€20.7 billion), Switzerland (€16.4 billion), Romania (€12.4 billion), Türkiye (€8.6 billion) and Ireland (€4.8 billion). The main contributors for the overall increase in outstanding are Americas (+€7.9 billion), Belgium (+€6.2 billion), Poland (+€2.8 billion) and France (+€2.8 billion). Private Individuals remained the largest composition of portfolio balances for the Netherlands at 58.3% (2023: 54.6%), Belgium at 37.4% (2023: 38.5%), Germany at 68.8% (2023: 66.5%) and Australia at 66.1% (2023: 65.6%). The decrease in Central Banks is mainly attributed to the Netherlands (€8.5 billion explaining the decrease in WB Netherlands outstanding), Germany (€5.0 billion) and Asia (€4.1 billion). In individual countries, the total share of investment grade/non-investment grade remains substantial for the Netherlands at 98.5% (2023: 98.6%), Germany at 98.9% (2023: 99.1%) and in Belgium 96.6% (2023: 96.6%). In Europe, the increase in investment grade was mainly witnessed in Poland (+€4.3 billion), France (+€3.2 billion), Spain (+€3.2 billion), Belgium (+€3.2 billion) and Luxembourg (+€3.2 billion). Non-investment grade decreased in the Netherlands (-€2.7 billion), Poland (-€1.8 billion), Luxembourg (-€1.2 billion) and Spain (-€1.0 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 | 2024
Private Individuals | 122,914 |  | 45,611 |  | 107,415 |  | 16,525 |  | 27,083 |  | 122 |  | 3,058 |  | 2,260 |  | 16,391 |  | 198 |  | 129 |  | 38,106 |  | 16 |  | 379,827 |
Central Banks | 22,529 |  | 10,196 |  | 13,966 |  | 1,729 |  | 510 |  | 1,935 |  | 5,737 |  |  |  | 10,913 |  |  |  | 9,525 |  | 879 |  |  |  | 77,919 |
Natural Resources | 2,197 |  | 1,531 |  | 881 |  | 778 |  | 152 |  | 3,021 |  | 2,503 |  | 405 |  | 11,212 |  | 8,475 |  | 8,989 |  | 1,593 |  | 159 |  | 41,899 |
Real Estate | 16,749 |  | 13,387 |  | 1,218 |  | 2,085 |  | 1,595 |  | 552 |  | 3,446 |  | 2,713 |  | 3,707 |  | 3,220 |  | 1,066 |  | 3,642 |  |  |  | 53,381 |
Commercial Banks | 1,285 |  | 314 |  | 4,129 |  | 695 |  | 376 |  | 4,733 |  | 5,268 |  | 5,074 |  | 7,779 |  | 10,700 |  | 9,394 |  | 1,336 |  | 157 |  | 51,240 |
Non-Bank Financial Institutions | 2,872 |  | 1,766 |  | 5,147 |  | 2,874 |  | 249 |  | 8,479 |  | 6,031 |  | 5,932 |  | 5,174 |  | 23,367 |  | 3,518 |  | 1,255 |  | 55 |  | 66,719 |
Central Governments | 1,416 |  | 11,009 |  | 51 |  | 9,435 |  | 5,308 |  | 48 |  | 82 |  | 3,202 |  | 9,203 |  | 15,377 |  | 288 |  | 488 |  | 589 |  | 56,497 |
Transportation & Logistics | 4,290 |  | 2,076 |  | 1,426 |  | 1,623 |  | 679 |  | 2,262 |  | 828 |  | 765 |  | 7,407 |  | 3,983 |  | 6,912 |  | 504 |  | 781 |  | 33,536 |
Utilities | 1,805 |  | 1,843 |  | 3,920 |  | 814 |  | 1,971 |  | 2,826 |  | 395 |  | 712 |  | 3,951 |  | 5,886 |  | 1,187 |  | 2,253 |  | 152 |  | 27,713 |
Food, Beverages & Personal Care | 7,377 |  | 3,690 |  | 695 |  | 2,215 |  | 351 |  | 328 |  | 1,393 |  | 1,102 |  | 3,008 |  | 2,498 |  | 1,168 |  | 406 |  | 14 |  | 24,246 |
Services | 4,919 |  | 8,431 |  | 1,852 |  | 1,538 |  | 122 |  | 869 |  | 540 |  | 310 |  | 1,271 |  | 1,265 |  | 516 |  | 680 |  |  |  | 22,312 |
General Industries | 4,568 |  | 2,690 |  | 1,059 |  | 2,824 |  | 219 |  | 301 |  | 539 |  | 484 |  | 3,862 |  | 2,039 |  | 708 |  | 23 |  | 8 |  | 19,324 |
Lower Public Administration | 782 |  | 6,824 |  | 7,435 |  | 608 |  | 557 |  |  |  | 246 |  | 3,091 |  | 476 |  | 1,554 |  | 44 |  | 4,941 |  |  |  | 26,557 |
Other | 17,208 |  | 12,722 |  | 6,876 |  | 8,394 |  | 1,361 |  | 3,137 |  | 1,248 |  | 1,426 |  | 11,722 |  | 10,475 |  | 4,372 |  | 1,513 |  | 238 |  | 80,691 |
Total | 210,913 |  | 122,091 |  | 156,071 |  | 52,136 |  | 40,533 |  | 28,613 |  | 31,314 |  | 27,476 |  | 96,076 |  | 89,037 |  | 47,815 |  | 57,618 |  | 2,170 |  | 961,863 |
Rating class |  |  |  |  |  |  |  |  |  |  |  |  |  |
Investment grade | 170,093 |  | 74,882 |  | 136,096 |  | 36,029 |  | 32,741 |  | 23,844 |  | 27,235 |  | 21,886 |  | 67,110 |  | 72,686 |  | 41,203 |  | 46,959 |  | 59 |  | 750,822 |
Non-Investment grade | 37,689 |  | 43,059 |  | 18,238 |  | 13,948 |  | 7,126 |  | 4,388 |  | 3,858 |  | 5,229 |  | 25,679 |  | 14,763 |  | 5,859 |  | 9,889 |  | 1,898 |  | 191,623 |
Performing restructuring | 1,579 |  | 1,078 |  | 305 |  | 701 |  | 234 |  | 59 |  | 56 |  | 54 |  | 1,369 |  | 443 |  | 30 |  | 203 |  | 4 |  | 6,114 |
Non-performing loans | 1,552 |  | 3,071 |  | 1,432 |  | 1,458 |  | 432 |  | 322 |  | 166 |  | 307 |  | 1,918 |  | 1,145 |  | 723 |  | 568 |  | 210 |  | 13,303 |
Total | 210,913 |  | 122,091 |  | 156,071 |  | 52,136 |  | 40,533 |  | 28,613 |  | 31,314 |  | 27,476 |  | 96,076 |  | 89,037 |  | 47,815 |  | 57,618 |  | 2,170 |  | 961,863 |
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 | 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 |
Non-performing loans | 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. 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), guarantees and other covers (mainly pledges). ING obtains covers that are eligible for credit risk mitigation under CRR/CRDIV, as well as covers that are 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.
Cover values including guarantees received (*)
in € million |  | Cover type and value | Collateralisation
2024 | Outstandings | Mortgages | Financial Collateral | Guarantees | Other covers | No cover | Partially covered | Fully covered
Consumer lending | 378,832 |  | 865,466 |  | 6,257 |  | 25,428 |  | 55,115 |  | 6.5 | % | 2.0 | % | 91.5 | %
Business lending | 368,570 |  | 163,143 |  | 24,838 |  | 119,410 |  | 484,148 |  | 34.1 | % | 23.7 | % | 42.2 | %
Investment and money market | 153,493 |  |  |  |  |  | 1,115 |  | 95 |  | 99.3 | % | — | % | 0.7 | %
Total lending, investment and money market | 900,894 |  | 1,028,609 |  | 31,095 |  | 145,953 |  | 539,357 |  | 33.6 | % | 10.5 | % | 55.9 | %
of which NPL | 13,295 |  | 10,427 |  | 194 |  | 3,093 |  | 11,109 |  | 27.6 | % | 27.7 | % | 44.7 | %
Pre-settlement | 60,968 |  |  |  |  |  |  |  |
Total Group | 961,863 |  |  |  |  |  |  |  |
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 | 931,552 |  |  |  |  |  |  |  |
The above tables gives an overview of the collateralisation of ING’s total portfolio. Excluding the pre-settlement portfolio, 55.9% (2023: 55.0%) of ING’s outstandings were fully collateralised in 2024. Since investments traditionally do not require covers, the ‘no covers’ percentage in this portfolio is over 99%. Consumer lending portfolio (*) The consumer lending portfolio accounts for 39.4% (2023: 38.7%) 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 (34.6%), Germany (27.5%), Belgium including Luxembourg (12.8%) and Australia (10.8%). Note that the large increase in Other covers and decrease in Financial Collateral is related to a reclassification of certain cover types. Business lending portfolio (*) Business lending accounts for 38.3% (2023: 39.1%) of ING’s total outstanding. Business lending presented in this section does not include pre-settlement, investment and money market exposures. Credit quality (*) I NG uses three distinct statuses to categorise the management of clients with (perceived) deteriorating credit risk profiles. ING usually classifies a client first with a “watch list” status 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. Furthermore, ING makes use of Early Warning Indicators (EWIs) in daily credit risk management processes in non-Retail portfolios which relate to a change in (internal and/or external) circumstances or outlook of the specific Obligor, the sector or portfolio. Some clients with a watch list or EWI status may develop into a performing restructuring status (performing loans that hold a reasonable probability that ING will end up with a loss, if no specific action is taken) or a non-performing status. When there is increasing doubt as to the performance and the collectability of the client’s contractual obligations the loans are managed by Global Credit Restructuring (GCR) or by restructuring units in the various regions and business units. The statuses and links with rating grades are illustrated in the table below.
Credit quality outstandings (*)
in € million | 2024 | 2023
Performing not past due | 823,478 |  | 795,942 |
Business lending performing past due | 9,174 |  | 8,825 |
Consumer lending performing past due | 802 |  | 846 |
Non-performing | 13,295 |  | 11,653 |
Total lending and investment | 846,749 |  | 817,266 |
Money market | 54,145 |  | 65,189 |
Pre-settlement | 60,968 |  | 49,096 |
Total | 961,863 |  | 931,552 |
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 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 resolved, 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. The table below represents the breakdown of lending and investment credit risk outstandings that are performing by age and geographic area.
Ageing analysis (past due but performing): Consumer lending portfolio by geographic area, outstandings (*)
in € million | 2024 | 2023
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 | 185 |  | 49 |  | 29 |  |  | 263 |  | 223 |  | 43 |  | 29 |  |  | 295 |
Germany | 65 |  | 37 |  | 24 |  |  | 125 |  | 89 |  | 40 |  | 18 |  |  | 147 |
Poland | 61 |  | 9 |  | 4 |  |  | 74 |  | 76 |  | 8 |  | 5 |  |  | 89 |
Netherlands | 62 |  | 35 |  | 4 |  |  | 101 |  | 67 |  | 24 |  | 6 |  |  | 97 |
Luxembourg | 22 |  | 6 |  | 3 |  |  | 32 |  | 21 |  | 2 |  | 2 |  |  | 25 |
Spain | 12 |  | 16 |  | 8 |  |  | 36 |  | 19 |  | 13 |  | 6 |  |  | 38 |
Rest of Europe | 93 |  | 15 |  | 4 |  |  | 112 |  | 64 |  | 19 |  | 12 |  |  | 94 |
America | 0 |  | 0 |  | 0 |  |  | 1 |  | 1 |  | 0 |  | 0 |  |  | 1 |
Asia | 0 |  | 0 |  | 0 |  |  | 0 |  | 0 |  | 0 |  | 0 |  |  | 1 |
Australia | 38 |  | 19 |  | 2 |  |  | 59 |  | 43 |  | 15 |  | 1 |  |  | 59 |
Africa | 0 |  | 0 |  | 0 |  |  | 0 |  | 0 |  | 0 |  | 0 |  |  | 0 |
Total | 538 |  | 186 |  | 78 |  |  | 802 |  | 602 |  | 164 |  | 79 |  |  | 846 |
The past due but performing consumer lending outstanding decreased by €44 million,due to a decrease in 1-30 days (-€64 million) which was partially offset by an increase in 31-60 (+€22 million). The largest decrease was observed in Belgium (-€32 million) and Germany (-€22 million), mainly in the 1-30 days bucket. The largest increase was seen in Rest of Europe (+€18 million).
Ageing analysis (past due but performing): Business lending portfolio by geographic area, outstandings (*)
in € million | 2024 | 2023
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 | 1,187 |  | 17 |  | 13 |  |  | 1,217 |  | 929 |  | 98 |  | 11 |  |  | 1,037 |
United Kingdom | 830 |  | 8 |  |  |  |  | 838 |  | 623 |  | 659 |  | 128 |  |  | 1,410 |
Luxembourg | 367 |  | 51 |  | 5 |  |  | 423 |  | 577 |  | 8 |  | 11 |  |  | 596 |
Netherlands | 929 |  | 14 |  |  |  |  | 943 |  | 509 |  | 10 |  | 12 |  |  | 531 |
Poland | 173 |  | 17 |  | 19 |  |  | 209 |  | 346 |  | 26 |  | 10 |  |  | 383 |
Spain | 26 |  |  |  |  |  |  | 26 |  |  |  |  |  |  |  |  |  |
France | 194 |  |  |  |  |  |  | 194 |  | 58 |  | 132 |  |  |  |  | 190 |
Germany | 215 |  | 3 |  | 2 |  |  | 220 |  | 131 |  | 110 |  | 1 |  |  | 242 |
Rest of Europe | 630 |  | 4 |  | 46 |  |  | 681 |  | 972 |  | 2 |  | 2 |  |  | 977 |
America | 3,504 |  | 95 |  |  |  |  | 3,599 |  | 2,508 |  | 101 |  | 41 |  |  | 2,650 |
Asia | 310 |  |  |  |  |  |  | 310 |  | 284 |  |  |  | 22 |  |  | 306 |
Australia | 469 |  | 6 |  |  |  |  | 475 |  | 501 |  | 1 |  |  |  |  | 502 |
Africa | 39 |  |  |  |  |  |  | 39 |  |  |  |  |  |  |  |  |  |
Total | 8,873 |  | 215 |  | 86 |  |  | 9,174 |  | 7,437 |  | 1,148 |  | 240 |  |  | 8,825 |
Total past due but performing outstanding of business lending increased by €0.3 billion. Increase was witnessed in the 1–30 days past due bucket (€1.4 billion) which was offset by the decrease observed in the 31-60 days (-€0.9 billion) and 61-90 days (-€0.2 billion) past due buckets. The largest increase was in the Americas (€0.9 billion) while the largest decrease was in the United Kingdom (-€0.6 billion). 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. The aim is to maximise the client’s repayment ability, thereby avoiding a default situation, or help 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 | 2024 | 2023
Business line | Outstandings | Of which: performing | Of which: non-performing | % of total portfolio | Outstandings | Of which: performing | Of which: non-performing | % of total portfolio
Wholesale Banking | 5,934 |  | 3,191 |  | 2,743 |  | 1.9 | % | 6,063 |  | 3,919 |  | 2,144 |  | 1.8 | %
Retail Banking | 6,883 |  | 3,987 |  | 2,897 |  | 1.3 | % | 7,026 |  | 4,128 |  | 2,898 |  | 1.4 | %
Total | 12,817 |  | 7,178 |  | 5,640 |  | 1.5 | % | 13,089 |  | 8,047 |  | 5,042 |  | 1.5 | %
Summary Forborne portfolio by forbearance type (*)
in € million | 2024 | 2023
Forbearance type | Outstandings | Of which: performing | Of which: non-performing | % of total portfolio | Outstandings | Of which: performing | Of which: non-performing | % of total portfolio
Loan modification | 11,726 |  | 6,734 |  | 4,993 |  | 1.4 | % | 11,881 |  | 7,550 |  | 4,331 |  | 1.4 | %
Refinancing | 1,091 |  | 444 |  | 647 |  | 0.1 | % | 1,208 |  | 497 |  | 711 |  | 0.1 | %
Total | 12,817 |  | 7,178 |  | 5,640 |  | 1.5 | % | 13,089 |  | 8,047 |  | 5,042 |  | 1.5 | %
As of 31 December 2024, ING’s total forborne assets decreased by €272 million compared to 31 December 2023. WB decreased by €129 million and Retail decreased by €143 million. Wholesale Banking (*) As of December 2024, WB forborne assets amounted to €5.9 billion (2023: €6.1 billion), which represented 1.9% (2023: 1.8%) of the total WB portfolio.
Wholesale Banking: Forborne portfolio by geographical area (*)
in € million | 2024 | 2023
Region | Outstandings | Of which: performing | Of which: non-performing | Outstandings | Of which: performing | Of which: non-performing
Europe | Netherlands | 217 |  | 69 |  | 148 |  | 361 |  | 301 |  | 60 |
Belgium | 172 |  | 165 |  | 7 |  | 454 |  | 446 |  | 8 |
Germany | 372 |  | 62 |  | 310 |  | 288 |  | 148 |  | 139 |
United Kingdom | 444 |  | 266 |  | 178 |  | 583 |  | 425 |  | 158 |
Italy | 389 |  | 353 |  | 36 |  | 54 |  | 19 |  | 34 |
Norway | 0 |  | 0 |  | 0 |  | 6 |  | 0 |  | 6 |
Poland | 630 |  | 284 |  | 346 |  | 520 |  | 519 |  | 0 |
Rest of Europe | 1,339 |  | 940 |  | 399 |  | 1,421 |  | 1,142 |  | 279 |
America | 1,586 |  | 867 |  | 719 |  | 1,025 |  | 532 |  | 493 |
Asia | 652 |  | 111 |  | 541 |  | 1,198 |  | 277 |  | 921 |
Australia | 79 |  | 34 |  | 44 |  | 87 |  | 87 |  | 0 |
Africa | 54 |  | 40 |  | 15 |  | 68 |  | 23 |  | 45 |
Total | 5,934 |  | 3,191 |  | 2,743 |  | 6,063 |  | 3,919 |  | 2,144 |
Wholesale Banking: Forborne portfolio by economic sector (*)
in € million | 2024 | 2023
Industry | Outstandings | Of which: performing | Of which: non-performing | Outstandings | Of which: performing | Of which: non-performing
Natural Resources | 781 |  | 424 |  | 356 |  | 788 |  | 321 |  | 467 |
Real Estate | 1,115 |  | 703 |  | 412 |  | 1,320 |  | 1,254 |  | 66 |
Transportation & Logistics | 214 |  | 83 |  | 131 |  | 315 |  | 175 |  | 139 |
Food, Beverages & Personal Care | 810 |  | 415 |  | 395 |  | 866 |  | 465 |  | 401 |
Services | 211 |  | 176 |  | 34 |  | 284 |  | 254 |  | 30 |
Automotive | 332 |  | 183 |  | 149 |  | 138 |  | 98 |  | 40 |
Utilities | 677 |  | 301 |  | 376 |  | 510 |  | 255 |  | 255 |
General Industries | 127 |  | 70 |  | 58 |  | 145 |  | 74 |  | 71 |
Retail | 149 |  | 21 |  | 128 |  | 282 |  | 104 |  | 178 |
Chemicals, Health & Pharmaceuticals | 668 |  | 136 |  | 532 |  | 571 |  | 559 |  | 11 |
Builders & Contractors | 122 |  | 118 |  | 5 |  | 133 |  | 72 |  | 61 |
Other | 729 |  | 561 |  | 168 |  | 712 |  | 287 |  | 425 |
Total | 5,934 |  | 3,191 |  | 2,743 |  | 6,063 |  | 3,919 |  | 2,144 |
Net decrease in WB is driven by the performing forborne exposures -€728 million. Non-performing forborne assets increased by €599 million, mainly in Chemicals, Health & Pharmaceuticals and in Real Estate in line with earlier mentioned NPL increases. WB's forborne assets are mainly concentrated in real estate; food, beverages & personal care; natural resources; chemicals, health & pharmaceuticals; and utilities. These five sectors accounted for 68.3% of the total WB forborne outstandings. Retail Banking (*) As of 31 December 2024, Retail Banking forborne assets amounted to €6.9 billion (2023: €7.0 billion), which represented 1.3% (2023: 1.4%) of the total RB portfolio. 26.7% of the forborne exposures were in Private Individuals.
Retail Banking: Forborne portfolio by geographical area (*)
in € million | 2024 | 2023
Region | Outstandings | Of which: performing | Of which: non-performing | Outstandings | Of which: performing | Of which: non-performing
Europe | Netherlands | 1,548 |  | 1,134 |  | 414 |  | 1,483 |  | 981 |  | 502 |
Belgium | 1,942 |  | 800 |  | 1,142 |  | 2,153 |  | 838 |  | 1,315 |
Germany | 1,379 |  | 1,052 |  | 327 |  | 1,309 |  | 1,064 |  | 246 |
Poland | 777 |  | 403 |  | 374 |  | 852 |  | 522 |  | 330 |
Türkiye | 13 |  | 9 |  | 4 |  | 25 |  | 15 |  | 10 |
Italy | 122 |  | 43 |  | 79 |  | 123 |  | 51 |  | 71 |
Romania | 173 |  | 72 |  | 101 |  | 135 |  | 49 |  | 86 |
Spain | 159 |  | 127 |  | 31 |  | 138 |  | 118 |  | 21 |
Rest of Europe | 102 |  | 50 |  | 52 |  | 88 |  | 58 |  | 30 |
America | 22 |  | 17 |  | 6 |  | 21 |  | 20 |  |  |
Asia | 1 |  |  |  | 1 |  | 2 |  | 1 |  | 1 |
Australia | 646 |  | 279 |  | 367 |  | 697 |  | 411 |  | 286 |
Africa |  |  |  |  |  |  |  |  |  |  |  |
Total | 6,883 |  | 3,987 |  | 2,897 |  | 7,026 |  | 4,128 |  | 2,898 |
The main concentration of forborne assets in a single country was in Belgium with 28.2% (2023: 30.6%) of total Retail Banking forborne assets and 39.4% (2023: 45.4%) of the non-performing forborne assets, followed by the Netherlands with 22.5% (2023: 21.1%) and Germany having 20.0% (2023: 18.6%) of the total Retail forborne assets. Non-performing loans (*) ING has aligned the regulatory concept of non-performing with that of the definition of default. Hence, borrowers 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; and •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. The table below represents the breakdown of credit risk outstandings that have been classified as non-performing by sector and business line.
Non-performing Loans: outstandings by economic sector and business lines (*)1
in € million | Wholesale Banking | Retail Banking |  | Total
Industry | 2024 | 2023 | 2024 | 2023 |  |  | 2024 | 2023
Private Individuals | 4 |  | 4 |  | 4,766 |  | 4,416 |  |  |  | 4,769 |  | 4,419 |
Natural Resources | 965 |  | 669 |  | 99 |  | 85 |  |  |  | 1,064 |  | 754 |
Food, Beverages & Personal Care | 452 |  | 565 |  | 357 |  | 520 |  |  |  | 809 |  | 1,085 |
Transportation & Logistics | 347 |  | 437 |  | 157 |  | 134 |  |  |  | 504 |  | 572 |
Services | 102 |  | 101 |  | 394 |  | 481 |  |  |  | 495 |  | 582 |
Real Estate | 831 |  | 592 |  | 603 |  | 462 |  |  |  | 1,434 |  | 1,053 |
General Industries | 236 |  | 111 |  | 451 |  | 385 |  |  |  | 687 |  | 497 |
Builders & Contractors | 51 |  | 124 |  | 445 |  | 453 |  |  |  | 496 |  | 577 |
Retail | 157 |  | 207 |  | 224 |  | 188 |  |  |  | 381 |  | 395 |
Utilities | 582 |  | 331 |  | 19 |  | 18 |  |  |  | 600 |  | 348 |
Chemicals, Health & Pharmaceuticals | 654 |  | 101 |  | 185 |  | 132 |  |  |  | 839 |  | 233 |
Telecom | 151 |  | 378 |  | 12 |  | 12 |  |  |  | 163 |  | 390 |
Other | 666 |  | 412 |  | 387 |  | 336 |  |  |  | 1,052 |  | 748 |
Total | 5,196 |  | 4,034 |  | 8,099 |  | 7,619 |  |  |  | 13,295 |  | 11,653 |
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 | 2024
Private Individuals | 646 |  | 1,461 |  | 1,066 |  | 210 |  | 304 |  | 10 |  | 8 |  | 44 |  | 545 |  | 2 |  | 2 |  | 469 |  |  |  | 4,769 |
Natural Resources | 13 |  | 54 |  |  |  | 33 |  |  |  |  |  |  |  |  |  | 569 |  | 31 |  | 343 |  | 21 |  |  |  | 1,064 |
Food, Beverages & Personal Care | 196 |  | 154 |  | 1 |  | 93 |  |  |  | 23 |  | 5 |  |  |  | 158 |  | 51 |  | 127 |  |  |  |  |  | 809 |
Transportation & Logistics | 93 |  | 40 |  | 3 |  | 124 |  | 47 |  |  |  |  |  | 1 |  | 136 |  |  |  |  |  | 1 |  | 59 |  | 504 |
Services | 57 |  | 293 |  | 5 |  | 87 |  | 2 |  | 1 |  | 3 |  | 5 |  | 10 |  | 34 |  |  |  |  |  |  |  | 495 |
Real Estate | 12 |  | 374 |  | 63 |  | 114 |  | 59 |  |  |  | 59 |  | 90 |  | 6 |  | 606 |  |  |  | 52 |  |  |  | 1,434 |
General Industries | 153 |  | 123 |  | 24 |  | 147 |  | 20 |  |  |  | 2 |  | 1 |  | 170 |  | 17 |  | 30 |  |  |  |  |  | 687 |
Builders & Contractors | 68 |  | 175 |  | 5 |  | 162 |  |  |  |  |  |  |  | 7 |  | 78 |  |  |  |  |  |  |  |  |  | 496 |
Retail | 53 |  | 97 |  | 39 |  | 62 |  |  |  |  |  | 3 |  |  |  | 14 |  | 97 |  | 15 |  | 1 |  |  |  | 381 |
Utilities | 13 |  | 8 |  | 25 |  | 21 |  |  |  | 285 |  |  |  |  |  | 12 |  | 128 |  | 109 |  |  |  |  |  | 600 |
Chemicals, Health & Pharmaceuticals | 37 |  | 94 |  | 84 |  | 340 |  |  |  | 1 |  | 110 |  |  |  | 113 |  | 36 |  |  |  | 24 |  |  |  | 839 |
Telecom | 7 |  | 1 |  |  |  | 3 |  |  |  |  |  | 44 |  |  |  | 14 |  | 90 |  | 4 |  |  |  |  |  | 163 |
Other | 202 |  | 198 |  | 117 |  | 60 |  |  |  | 2 |  | 72 |  | 17 |  | 90 |  | 54 |  | 92 |  |  |  | 150 |  | 1,052 |
Total | 1,549 |  | 3,071 |  | 1,432 |  | 1,457 |  | 432 |  | 322 |  | 307 |  | 166 |  | 1,916 |  | 1,145 |  | 723 |  | 567 |  | 210 |  | 13,295 |
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 |
In 2024, the NPL portfolio increased to €13.3 billion, an increase in Wholesale Banking (+€1.2 billion) together with an increase in Retail Banking (+€0.5 billion). The increase in Wholesale Banking was mainly witnessed in real estate; chemicals, health & pharmaceuticals; and in natural resources, partially offset by telecom. In Retail Banking, the increase was concentrated in Private Individuals. The top three countries by NPL outstanding are Belgium, the Netherlands and Poland. 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; and •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 IFRS 9 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 the depreciation/erosion of collateral values, which would translate into higher credit losses and loan-to-value ratios in the lending portfolio of ING. Currently, it is not yet possible to fully 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. However, ING has taken next steps in 2024 to more explicitly cover for climate-risk drivers in loan loss provisioning. A management adjustment to ECL models for business clients was introduced to specifically cover for the medium- to long-term transition risk on high greenhouse gas-emitting sectors. For households, particularly the mortgage book, next step in the development will be to apply differentiation in collateral valuation based on EPC labels. Additionally, where climate and environmental factors have impacted the economy in the recent past or present, these impacts are implicitly embedded in ING’s IFRS 9 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 specific climate-related matters, particularly physical risk events that 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, collective SICR 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. Furthermore as at 31 December 2024 we have reported a management adjustment for the increased expected credit risk in the Mortgage and Consumer Lending portfolio in Spain due to payment holidays provided for customers with collateral or activities in the Valencia area impacted by the severe floods in the fourth quarter of 2024. For more, see ‘Management adjustments applied this reporting period’. Going forward, ING aims to continue to improve on climate risk data, which will enable us to further embed climate risks into the IFRS 9 ECL models. For further details on ESG risk management, see ‘ESG risk’. Portfolio quality (*) The table below describes the portfolio composition over the different IFRS 9 stages and rating classes. The Stage 1 portfolio represents 91.1% (2023: 91.5%) of the total gross carrying amounts, mainly composed of investment grade, while Stage 2 makes up 7.6% (2023: 7.3%) and Stage 3 makes up 1.3% (2023: 1.2%) of the total gross carrying amounts, respectively.
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
Rating class | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions
|  | 2024 | 2023 | 2024 | 2023 | 2024 | 2023 | 2024 | 2023 | 2024 | 2023 | 2024 | 2023 | 2024 | 2023 | 2024 | 2023
Investment grade | 1 (AAA) | 79,076 |  | 87,071 |  | 1 |  | 1 |  | 281 |  | 439 |  |  |  |  |  |  |  |  |  |  |  |  |  | 79,357 |  | 87,510 |  | 1 |  | 1 |
2-4 (AA) | 140,671 |  | 132,159 |  | 10 |  | 8 |  | 1,579 |  | 2,553 |  | 1 |  | 2 |  |  |  |  |  |  |  |  |  | 142,250 |  | 134,711 |  | 11 |  | 9 |
5-7 (A) | 244,241 |  | 231,018 |  | 22 |  | 24 |  | 6,908 |  | 6,188 |  | 8 |  | 6 |  |  |  |  |  |  |  |  |  | 251,149 |  | 237,206 |  | 29 |  | 30 |
8-10 (BBB) | 310,324 |  | 302,967 |  | 55 |  | 85 |  | 24,683 |  | 17,004 |  | 55 |  | 24 |  |  |  |  |  |  |  |  |  | 335,008 |  | 319,971 |  | 110 |  | 108 |
Non-Investment grade | 11-13 (BB) | 154,348 |  | 157,387 |  | 190 |  | 226 |  | 18,479 |  | 19,273 |  | 91 |  | 93 |  |  |  |  |  |  |  |  |  | 172,827 |  | 176,661 |  | 281 |  | 319 |
14-16 (B) | 25,377 |  | 26,414 |  | 124 |  | 164 |  | 17,433 |  | 19,336 |  | 366 |  | 455 |  |  |  |  |  |  |  |  |  | 42,811 |  | 45,750 |  | 490 |  | 618 |
17 (CCC) | 905 |  | 617 |  | 8 |  | 10 |  | 3,992 |  | 4,125 |  | 173 |  | 233 |  |  |  |  |  |  |  |  |  | 4,897 |  | 4,742 |  | 181 |  | 242 |
Performing Restructuring | 18 (CC) |  |  |  |  |  |  |  |  | 4,059 |  | 4,617 |  | 233 |  | 402 |  |  |  |  |  |  |  |  |  | 4,060 |  | 4,617 |  | 233 |  | 402 |
19 (C) |  |  |  |  |  |  |  |  | 2,474 |  | 1,919 |  | 203 |  | 221 |  |  |  |  |  |  |  |  |  | 2,474 |  | 1,919 |  | 203 |  | 221 |
Non-performing loans | 20-22 (D) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | 13,742 |  | 11,956 |  | 4,509 |  | 3,887 |  | 13,742 |  | 11,956 |  | 4,509 |  | 3,887 |
Total | 954,943 |  | 937,633 |  | 409 |  | 517 |  | 79,888 |  | 75,454 |  | 1,130 |  | 1,435 |  | 13,742 |  | 11,956 |  | 4,509 |  | 3,887 |  | 1,048,574 |  | 1,025,043 |  | 6,049 |  | 5,839 |
1 Compared to the credit risk portfolio, the differences are mainly undrawn committed amounts (€156 billion; 2023: €151 billion) and other positions (€6 billion; 2023: €9 billion ) not included in credit outstandings and non-IFRS 9 eligible assets (€75 billion; 2023: €67 billion), mainly pre-settlement exposures) included in credit outstandings but not in the gross carrying amounts. 2 Stage 3 lifetime credit impaired provision includes €21 million (2023: €11 million) on purchased or originated credit impaired. 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: •Stage 3 gross carrying amount increased by €1.7 billion from €12.0 billion as at 31 December 2023 to €13.7 billion as at 31 December 2024, mainly as a result of €4.8 billion net inflow into NPL (credit impaired) in 2024 which is offset by €1.8 billion derecognitions and repayments and €1.3 billion write-offs and disposals. Following the increase in carrying amount, Stage 3 provisions increased by €0.6 billion. •Stage 2 gross carrying amounts increased by €4.4 billion from €75.5 billion as at 31 December 2023 to €79.9 billion as at 31 December 2024, largely driven by €23.7 billion net transfers from Stage 1 into Stage 2, including the impact of changes in risk drivers (including updated macro-economic forecasts), model redevelopments mainly for Wholesale Banking models and new Stage 2 overlays. This was offset by a decrease of exposure by €16.2 billion due to derecognised financial assets (including sales), repayments and €2.9 billion exposure moving to Stage 3. Stage 2 provisions decreased by €0.3 billion to €1.1 billion as of 31 December 2024.
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 | 12-month ECL (Stage 1) | Lifetime ECL not credit impaired (Stage 2) | Lifetime ECL credit impaired (Stage 3) | Total
| Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions | Gross carrying amount | Provisions
| 2024 | 2023
Opening balance | 937,633 | 517 | 75,454 | 1,435 | 11,956 | 3,887 | 1,025,043 | 5,839 | 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) | 20,486 | 22 | -20,236 | -195 | -249 | -34 | 0 | -207 | 11,832 | 28 | -11,583 | -239 | -249 | -36 | 0 | -247
Transfer into lifetime ECL not credit impaired (Stage 2) | -43,155 | -49 | 43,900 | 429 | -745 | -96 | 0 | 285 | -29,470 | -67 | 30,185 | 449 | -716 | -105 | 0 | 276
Transfer into lifetime ECL credit impaired (Stage 3) | -2,980 | -18 | -2,856 | -235 | 5,836 | 1,802 | 0 | 1,548 | -2,053 | -10 | -1,775 | -114 | 3,828 | 978 | 0 | 853
Net remeasurement of loan loss provisions | 0 | -181 | 0 | -137 | 0 | 185 | 0 | -133 | 0 | -149 | 0 | -94 | 0 | 59 | 0 | -183
New financial assets originated or purchased | 212,516 | 192 | 0 | 0 | 0 | 0 | 212,516 | 192 | 195,775 | 204 | 0 | 0 | 0 | 0 | 195,775 | 204
Financial assets that have been derecognised | -126,858 | -76 | -11,840 | -153 | -1,450 | -257 | -140,148 | -485 | -121,991 | -72 | -14,239 | -215 | -1,475 | -266 | -137,705 | -552
Net drawdowns and repayments | -41,763 |  | -4,393 |  | -309 |  | -46,465 |  | -38,758 |  | -2,525 |  | -229 |  | -41,511 |
Changes in models/risk parameters | 0 | 8 | 0 | -6 | 0 | -22 | 0 | -20 | 0 | 7 | 0 | 11 | 0 | 84 | 0 | 102
Increase in loan loss provisions |  | -102 |  | -297 |  | 1,578 |  | 1,179 |  | -58 |  | -203 |  | 714 |  | 452
Write-offs 3 | 0 | 0 | 0 | 0 | -1,017 | -1,017 | -1,017 | -1,017 | -3 | -3 |  | 0 | -787 | -787 | -790 | -790
Disposals 3 | -935 | -1 | -141 | -8 | -279 | -215 | -1,355 | -225 | 0 | 0 | -38 | -38 | -283 | -283 | -321 | -321
Recoveries of amounts previously written off |  | 0 |  | 0 |  | 69 |  | 69 | 0 | 0 | 0 | 0 | 0 | 71 | 0 | 71
Foreign exchange and other movements | 0 | -5 | 0 | 0 | 0 | 208 | 0 | 203 | 0 | -12 | 0 | -15 | 0 | 257 | 0 | 231
Closing balance | 954,943 | 409 | 79,888 | 1,130 | 13,742 | 4,509 | 1,048,574 | 6,049 | 937,633 | 517 | 75,454 | 1,435 | 11,956 | 3,887 | 1,025,043 | 5,839
1 Stage 3 lifetime credit impaired provision includes €21 million (2023:€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 €1,194 million (2023: €520 million) of which €1,170 million (2023: €483 million) related to IFRS 9 eligible financial assets, €9 million (2023: €-31 million) related to non-credit replacement guarantees and €15 million (2023: €67 million) to modification gains and losses on restructured financial assets. 3 Table was updated for presentation purposes to disaggregate utilisation of the provision between write-offs and disposals. Comparatives have been updated accordingly. 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 | 2024 | 2023
Financial assets modified during the period |  |
Amortised cost before modification | 1,888 |  | 1,565 |
Net modification results | -107 |  | -75 |
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 | 1,506 |  | 2,599 |
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 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 diverging between major blocs. US growth is expected to continue to outpace European markets, while China is expected to continue on a declining growth trend, but still at higher rates than seen in advanced markets. Inflation is expected to remain near target for most advanced economies, although it is set to remain above target for the United States. With interest rates moderating, although some uncertainty about this path for the US exists at this point, monetary conditions should turn more favourable for growth. For the housing market, continued price growth is expected for almost all main markets. The December 2024 consensus expects global output (as measured by the weighted average GDP growth rate of ING’s 25 main markets) to slow from 2.5 percent in 2024 to 2.4 percent in 2025. For 2026-2027, economic growth is expected to come in at 2.4 percent and 2.3 percent respectively. The American economy continues to perform very well despite signs of a slowing labour market around the summer of 2024. Inflation has come down to more benign levels, which has prompted the Federal Reserve to lower interest rates. Still, inflation remains above the 2% target and the outlook for inflation has become more uncertain. The US administration’s economic plans are set to stimulate the economy for 2025, as reflected in the increased expectations for 2025 GDP of consensus forecasters. The consensus expects the growth rate of the US economy to slow from 2.7 percent in 2024 to 2.0 percent for both 2025 and 2026. The eurozone economy has seen some growth return in 2024 after a long period of stagnation that started during the energy crisis. Still, expectations for 2025 remain modest. The export environment continues to be plagued by weak global demand and investments are stymied by high interest rates and weak manufacturing performance. Expectations of a pickup in growth over the course of the year hang on domestic drivers like a pickup in real wage growth and continued rate cuts from the ECB at the start of the year. Consensus expects the eurozone to have grown by only 0.8 percent in 2024, before recovering slightly to 1.0 percent and 1.2 percent in 2025 and 2026 respectively. Elsewhere in Europe, the outlook is more upbeat. In Poland, domestic demand appears to remain the key growth driver over the near-term forecast. Foremost, consumers remain willing to spend, encouraged by elevated wage growth and a resilient labour market. The economy is expected to grow by 2.7 percent in 2024, picking up to 3.5 percent in 2025 and 3.8 percent in 2026. The consensus expectation for Türkiye is to see growth slow, which is being confirmed by weak incoming data. Consensus expects growth to slow from 3.1 percent in 2024 to 2.6 percent in 2025, in part due to soft demand from export orders. For 2026, a recovery to 3.5 percent is expected. The Russian economy is expected to slow substantially in 2025 after a strong 2024. Growth is expected to drop from 3.7 percent in 2024 to 1.6 percent in 2025 and 1.3 percent in 2026. For China, economic underperformance continues as it still struggles with the impact of the real estate correction and weak domestic demand. Large scale stimulus plans and a possible bottoming out of the real estate market do help economic forecasts for the short-run, although medium term consensus continues to be downbeat for the moment. For 2024, consensus expects 4.8 percent growth, down to 4.4 percent in 2025 and 4.1 percent in 2026. Economic momentum in Australia is expected to be soft. The economy is lacking a clear growth engine, with the private sector clearly struggling against restrictive policy settings and consumers facing a tough outlook. Growth is expected to have come in at 1.2 percent in 2024, with just moderate pick-up expected for 2025 to 2.0 percent and 2.4 percent for 2026. When compared to the June 2024 consensus forecast, the December 2024 forecast is relatively stable. Global GDP is expected to increase by 2.5 percent in 2024 (compared to 2.4 percent assumed before) and is expected to grow by 2.4 percent in 2025 (2.3 percent assumed before). With the energy crisis and pandemic now further behind us, the consensus for economic activity in major markets is showing smaller deviations over time despite economic and geopolitical uncertainty still being very prevalent. Alternative scenarios and risks (*) The baseline scenario assumes continued steady economic growth. However, a longer period of weakness, due to even more concerning geopolitical tensions, persistent elevated inflation and trade tensions 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 Oxford Economics’ Global Risk Survey. The downside scenario – though technical in nature – sees a recession in 2025 and 2026 for most countries. Unemployment increases strongly in this scenario and house prices in most countries show outright falls. The downside scenario captures the possible impact from escalating geopolitical tensions, increased trade tensions and persistent elevated inflation. The upside scenario – while equally technical in nature – reflects the possibility of a better economic out-turn because of a substantial loosening of monetary policy, and policy stimulus in China. Management adjustments applied this reporting period (*) 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, as well as to reflect the impact of model redevelopment or recalibration and periodic model assessment procedures that have not been incorporated in the IFRS 9 models yet. ING has internal governance frameworks and controls in place to assess the appropriateness of all management adjustments.
Management adjustments to ECL models (*)
in € million | 2024 | 2023
Commercial Real Estate/ Inflation and Interest rate increases | 50 |  | 351 |
Economic sector / portfolio based adjustments | 38 |  | 36 |
Mortgage portfolio adjustments | 112 |  | 126 |
Climate transition risk | 29 |  |  |
Other post model adjustments | -27 |  | 64 |
Total management adjustments | 203 |  | 577 |
As the ING credit risk models generally assume that inflation and interest rate increase risks materialise via other risk drivers, such as GDP and unemployment rates with a delay, an overlay approach was determined in previous financial years to timely estimate the expected credit losses (ECL) related to reduced repayment capacity and affordability for private individuals and business clients in the Retail Banking segment. As inflationary stress has decreased since origination of the overlay approach and the limited observed impact in both the Retail and the Wholesale Banking segment, no management adjustment is reported as at 31 December 2024, with exception of a management adjustment of €50 million for the Commercial Real Estate portfolio (31 December 2023: €351 million in total). The €50 million management adjustment related to the Commercial Real Estate portfolio is reported in Wholesale Banking (€33.5 million) and in Business Banking in the Netherlands (€16.5 million) because the prevailing risks from increased levels of interest rates and inflation still exist for this sector in these portfolios. This management adjustment is reflected in Stage 1 and Stage 2.Furthermore, in specific parts in the Retail Banking segment, that were previously included in the inflation and interest rate increases overlay and where increased risked not yet captured in the credit risk models are still observed, specific portfolio-based adjustments have been recognised. As at 31 December 2024, the economic sector / portfolio based adjustments in Stage 2 of €38 million in total included a management adjustment of €14 million for the increased expected credit risk in the Mortgage and Consumer Lending portfolio in Spain due to payment holidays provided for customers with collateral or activities in the areas impacted by the severe floods in the Valencia area in the fourth quarter of 2024. Furthermore, adjustments have been taken in the Business Banking portfolio in Germany (€10 million) to cover for the increased uncertainty in the German economy and to the Mortgage portfolio in Australia (€15 million) to cover for affordability risk from inflation and interest rate increases. The economic sector adjustments as at 31 December 2023 of €36 million, fully related to Business Banking clients that had benefited from government support programmes in the Netherlands during the Covid-19 pandemic. This adjustment was released in full in 2024 as the risk was considered to be no longer present in the portfolio. The overall mortgage portfolio adjustment as at 31 December 2024 decreased to €112 million (31 December 2023: €126 million). The management adjustment in Stage 2 for the risk segmentation model that captures affordability, repayment and refinancing risk on performing mortgage customers with a bullet loan in the Netherlands was decreased to €112 million (31 December 2023: €115 million). The mortgage portfolio adjustment that related to the overvaluation of house prices was released in full in 2024 (31 December 2023: €11 million). As of 31 December 2024, an adjustment of €29 million was introduced to cover for the impact of climate transition risk in Wholesale Banking (€17 million) and in Business Banking (€12 million). Climate transition risk is expected to lead to a structural change in credit risk, which means specific business activities will become structurally riskier due to environmental policies, technological progress or changes in market sentiment and preferences. The current IFRS 9 models do not capture this (novel) risk. The management adjustment to ECL models for business clients was made to specifically cover for the medium- to long-term transition risk on high greenhouse gas-emitting sectors and is reported in Stage 2. Other post-model adjustments 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 result from both regular model maintenance and ING’s multiyear programme to update ECL models. These adjustments will be removed once updates to the specific models have been implemented. The change in balance compared to 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 2024 (*)
|  | 2025 | 2026 | 2027 | Unweighted ECL (€ mln) | Probability-weighting | Reportable ECL (€ mln)1
Netherlands Upside scenario | Real GDP | 2.6 |  | 3.0 |  | 2.5 |  | 193 |  | 20 | % | 270 |
Unemployment | 3.5 |  | 3.3 |  | 3.3 |
HPI | 18.9 |  | 11.7 |  | 2.5 |
Baseline scenario | Real GDP | 1.5 |  | 1.4 |  | 1.5 |  | 249 |  | 60 | %
Unemployment | 4.0 |  | 4.1 |  | 4.3 |
HPI | 9.1 |  | 3.5 |  | 2.4 |
Downside scenario | Real GDP | -0.4 |  | -1.4 |  | -0.2 |  | 411 |  | 20 | %
Unemployment | 5.7 |  | 7.2 |  | 8.1 |
HPI | -3.7 |  | -7.2 |  | 2.2 |
Germany Upside scenario | Real GDP | 2.0 |  | 2.8 |  | 1.6 |  | 510 |  | 20 | % | 548 |
Unemployment | 2.9 |  | 2.4 |  | 2.0 |
HPI | 5.4 |  | 8.9 |  | 9.9 |
Baseline scenario | Real GDP | 0.5 |  | 1.1 |  | 1.2 |  | 540 |  | 60 | %
Unemployment | 3.4 |  | 3.3 |  | 3.2 |
HPI | 2.6 |  | 5.6 |  | 6.3 |
Downside scenario | Real GDP | -1.7 |  | -1.7 |  | 0.3 |  | 609 |  | 20 | %
Unemployment | 4.7 |  | 5.6 |  | 5.9 |
HPI | -1.7 |  | 1.3 |  | 2.2 |
Belgium Upside scenario | Real GDP | 2.2 |  | 2.6 |  | 2.1 |  | 534 |  | 20 | % | 579 |
Unemployment | 5.1 |  | 5.0 |  | 4.9 |
HPI | 4.8 |  | 4.5 |  | 4.4 |
Baseline scenario | Real GDP | 1.1 |  | 1.5 |  | 1.6 |  | 569 |  | 60 | %
Unemployment | 5.7 |  | 5.7 |  | 5.6 |
HPI | 3.2 |  | 4.1 |  | 3.8 |
Downside scenario | Real GDP | -0.6 |  | -0.2 |  | 1.1 |  | 654 |  | 20 | %
Unemployment | 7.0 |  | 8.0 |  | 8.0 |
HPI | 1.2 |  | 2.9 |  | 2.5 |
United States Upside scenario | Real GDP | 3.1 |  | 3.5 |  | 3.2 |  | 74 |  | 20 | % | 113 |
Unemployment | 3.4 |  | 2.4 |  | 2.3 |
HPI | 4.3 |  | 8.4 |  | 9.4 |
Baseline scenario | Real GDP | 2.0 |  | 2.0 |  | 2.0 |  | 101 |  | 60 | %
Unemployment | 4.2 |  | 4.1 |  | 4.0 |
HPI | 3.3 |  | 3.7 |  | 3.9 |
Downside scenario | Real GDP | -0.1 |  | -1.1 |  | -0.4 |  | 187 |  | 20 | %
Unemployment | 5.9 |  | 7.3 |  | 8.0 |
HPI | -0.7 |  | -3.0 |  | -2.5 |
1 Excluding management adjustments.
Sensitivity analysis as at December 2023 (*)
|  | 2024 | 2025 | 2026 | Unweighted 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.
When compared to the sensitivity analysis of 2023, the macroeconomic inputs are overall more favourable. This is driven by an improved macroeconomic outlook, mainly because economies prove to be rather resilient to increased interest rates, particularly in the US, as well as recovery in house prices in, among others, the Netherlands. On a total ING level, the unweighted ECL for all collective provisioned clients in the upside scenario was €2,721 million, in the baseline scenario €2,949 million and in the downside scenario €3,533 million compared to €3,020 million reportable collective provisions as at 31 December 2024 (excluding all management adjustments). To perform the sensitivity analysis, a point in time reportable ECL is used as input, which slightly deviates from the total Model ECL as reported below:
Reconciliation of reportable collective ECL to total ECL (*)
in € million | 2024 | 2023
Total reportable collective provisions | 2,975 |  | 2,856 |
ECL from individually assessed impairments | 2,871 |  | 2,406 |
ECL from management adjustments | 203 |  | 577 |
Total ECL | 6,049 |  | 5,839 |
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 (SICR) since initial origination or purchase. The main determinant of SICR is a quantitative test, whereby the lifetime probability of default (PD) of an asset at each reporting date is compared against its lifetime PD determined at the date of initial recognition. If either a threshold for absolute change in lifetime PD or a threshold for relative change in lifetime PD is reached, the item is considered to have experienced a SICR (for more details on absolute and relative thresholds, see the following sections). Furthermore, any facility which shows an increase of 200 percent between the PD at the date of initial recognition and the lifetime PD at the reporting date (i.e. threefold increase in PD) must be classified as Stage 2. This is considered a backstop within the quantitative assessment of SICR. In Wholesale Banking, significant increase in lifetime PD is not considered plausible for assets of obligors with a credit rating at the reporting date in the top range of investment grade. As of 2024, the assets of these Wholesale Banking obligors are excluded from the assessment of significant increase in credit risk triggers. For these obligors the qualitative significant increases in credit risk triggers remain applicable (see the section below on Qualitative SICR triggers). These are for example the Watchlist and/ or forbearance triggers. Finally, the 30 days past due backstop also remains applicable for the top range of investment grade exposures to ensure significant increase in credit risk recognition. 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 absolute threshold is calibrated during model development. 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 a 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. 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 2023 and year-end 2024. 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 (*)
| 2024 | 2023
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 | 2.9 |  | 2.4 |  | 2.5 |  | 2.3 |
Consumer lending | 2.8 |  | 2.1 |  | 2.9 |  | 2.1 |
Business lending | 2.7 |  | 2.1 |  | 2.7 |  | 2.1 |
Governments and financial institutions | 2.9 |  | 1.9 |  | 3.0 |  | 1.9 |
Other Wholesale Banking | 2.7 |  | 1.9 |  | 2.8 |  | 1.8 |
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, was €1,328 million as at 31 December 2024 (31 December 2023: €1,412 million). To demonstrate the sensitivity of the ECL to these PD threshold bands, hypothetically solely applying the upside scenario would result in total model ECL on performing assets of €1,066 million and a decrease in the Stage 2 ratio by 0.5%-point, while solely applying the downside scenario would result in total model ECL on performing assets of €1,911 million and an increase in the Stage 2 ratio by 1.9%-point. Qualitative SICR thresholds It should be noted that the lifetime PD thresholds are not the only drivers of stage allocation as ING Group also relies on a number of qualitative indicators to identify and assess SICR. An asset can also change stages as a result of other triggers, such as having over 30 days arrears (used as a backstop), collective SICR assessment, being on a watch list, being under intensive care management, having a substandard internal rating or being forborne. 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. 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. The following sections elaborate on the various elements of the risk management framework for: •Market risk in banking books; •Market risk in trading books; and •Market risk capital. 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 they are 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. 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 due to different re-pricing properties of assets and liabilities. Also, interest rate risk can arise from customer behaviour and/or convexity risk, depending on the nature of the underlying product characteristics. To determine the interest rate risk in particular products (like savings, mortgages) specific assumptions may need to be made. 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. This is achieved by creating the investment profile distributed from short term to long term, which dampens the immediate impact from changes in the market rates as well as stabilises margin in the longer horizon. 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. 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 analysis of historical data. However, the substantial change in the interest rate environment in recent years makes the analysis 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 this 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. 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 NPV-at-Risk is measured under a direct interest rate shock. Hence no additional, corrective hedges are included in the measure. The NII-at-Risk measure is measured for interest rate movements over a period, whereby (assumed) corrective hedges are included in the risk metric. 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 (dynamic plan) are included in this risk metric. NII-at-Risk provides insight into the sensitivity of ING’s NII under shocked interest rate scenarios against what is projected in a base case scenario. In its risk management, ING monitors the NII-at-Risk under a three-year time frame. Interest rates are shocked during the first year of analysis through the gradual application of shock. The rate changes considered encompass both upward and downward scenarios, as well as both 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 amortisation 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; and •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 between 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, among other things, 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 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 mainly influenced by the difference in the sensitivity between client liabilities and client assets and investments to rate changes. The primary factor of NII-at-Risk are the investments of current accounts, while the investments of own funds have a marginal effect, as only a relatively small portion needs to be (re)invested within a one-year period.
NII-at-Risk banking book per currency - year one (*)
in € million | 2024 | 2023
| Ramped, floored | Ramped, floored
parallel ▼ | parallel ▲ | parallel ▼ | parallel ▲
By currency |  |  |  |
Euro | -146 |  | 160 |  | -165 |  | 155 |
US dollar | -4 |  | 5 |  | -12 |  | 12 |
Other | -2 |  | 21 |  | -62 |  | 69 |
Total | -153 |  | 186 |  | -239 |  | 236 |
EUR ramped (floored at -100bps) is at +/- 120bps in 1 year (2023: +/-110bps)
USD ramped (floored at -100bps) is at +/- 120bps in 1 year (2023: +/-110bps)
The change in NII under 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. The metrics mentioned above are internal metrics, which therefore deviate from the regulatory NII SOT metrics. Year-on-year variance analysis (*) In 2024, in response to falling inflation, most central banks (including ECB and FED) began to ease their monetary policy, executing series of rates cuts. 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. The impact of explicit and implicit floors on both rates of client assets and savings remains limited. Pre-existing hedges, as executed by Group Treasury, were also adjusted continuously throughout the year to hedge any interest rate risk coming from lower interest rates. Most of the year-on-year change in NII sensitivity is due to enhancements in risk management framework in one of non-EUR locations. These enhancements are aimed at mitigating sensitivity in a volatile rate environment and include increasing the granularity of risk transferred positions and implementing limits to trigger an intra-month recalibration of the hedges. 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 and fixed rate mortgages). The metrics mentioned above are internal metrics, which therefore deviate from the regulatory EVE SOT metrics.
NPV-at-Risk banking books per currency (*)
in € million | 2024 | 2023
| floored | floored
parallel ▼ | parallel ▲ | parallel ▼ | parallel ▲
By currency |  |  |  |
Euro | 154 |  | -1,613 |  | -291 |  | -645 |
US dollar | 274 |  | -266 |  | 186 |  | -178 |
Other | 321 |  | -329 |  | 131 |  | -146 |
Total | 749 |  | -2,208 |  | 27 |  | -969 |
EUR (floored at -100bps) is at +/- 120bps (2023: +/-110bps)
USD (floored at -100bps) is at +/- 120bps (2023: +/-110bps)
Year-on-year variance analysis (*) The overall NPV sensitivity increased considerably over last year. The worst-case scenario remains the parallel up, while the shock used in calculation for main currencies (EUR and USD) increased from 110bps to 120bps on the back of increased volatility observed in the market during 2023. This increase partially explains the observed higher sensitivity. The other important factor impacting end of 2024 utilisation is convexity: with lower interest rates, the increase in convexity on mortgages and savings accounts was observed, especially for EUR positions. Lastly, Group Treasury actively managed the position and executed investments for capital in the anticipation of possible further rates decreases. The impact of the benchmark rate reform (*) In line with the recommendations of the Financial Stability Board, a fundamental review of important interest rates benchmarks has been undertaken. Some interest rate benchmarks have been reformed, while others have or will be replaced by risk-free rates 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. In 2024, the benchmark rate reform of only one reference rate, to which the Group has significant exposures as at 31 December 2024, was continuing (i.e. WIBOR). The WIBOR rate is expected to be ceased and replaced by a risk-free rate (RFR) by 31 December 2027. The Steering Committee of the National Working Group (NWG SC) appointed in connection with the WIBOR benchmark reform the decision (published on 10 December 2024) on the selection of the proposed index with the technical name 'WIRF –' as the ultimate interest rate benchmark in Poland to replace the WIBOR benchmark. On 24 January 2025, the Steering Committee of the National Working Group has selected target name POLSTR (Polish Short Term Rate) for this index. The chosen index is calculated based on unsecured deposits of Credit and Financial Institutions. Thus, the NWG SC has reviewed and modified its previous decision to select WIRON as alternative RFR in Poland. The WIBOR rate is still expected to be ceased and replaced by a new RFR ("WIRF -") by 31 December 2027. Due to the discontinuation of WIBOR, ING, its customers, and in general those market participants with exposure to such benchmark rates will be faced with a number of risks. These risks include legal, financial, operational, reputational and conduct risk. 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 Śląski 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, excluding exposures expiring before the transition date 31 December 2027 for WIBOR.
Non-derivative financial instruments to transition to alternative benchmarks (*)
| Financial assets non-derivative | Financial liabilities non-derivative | Off balance sheet commitments
in € million at 31 December 2024 | Carrying value | Carrying value | Nominal value
By benchmark rate |  |  |
USD LIBOR |  |  |  |  |  |
WIBOR | 19,202 |  | 134 |  | 1,544 |
Total | 19,202 |  | 134 |  | 1,544 |
in € million at 31 December 2023 |  |  |
By benchmark rate |  |  |
USD LIBOR | 915 |  | 16 |  | 9 |
WIBOR | 18,064 |  |  |  | 1,021 |
Total | 18,979 |  | 16 |  | 1,030 |
Derivative financial instruments to transition to alternative benchmarks (*)
| 31 December 2024 | 31 December 2023
in € million | Nominal value | Nominal value
By benchmark rate |  |
USD LIBOR |  |  | 151 |
WIBOR | 110,189 |  | 77,238 |
Total | 110,189 |  | 77,388 |
See sections 1.5.4 and 1.5.7 of Note 1 ‘Basis of preparation and material accounting policy information’ for information on the Phase 1 and Phase 2 amendments. As at 31 December 2024, 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 IAS 39 were applied are:
Notional amounts of hedging instruments (*)
| 31 December 2024 | 31 December 2023
in € million | Nominal value | Nominal value
By benchmark rate |  |
WIBOR | 99,663 |  | 89,338 |
As at 31 December 2024, 32% (31 December 2023: 29%) of the notional amounts for WIBOR have a maturity date beyond 31 December 2027. 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 (CSRBB) (*) 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. CSRBB framework is implemented based on EBA Guidelines. Metrics used are NPV-at-Risk, NII-at-Risk and Market Value Changes-at Risk and view the positions across different accounting treatments. Credit spread risk is not part of the internal risk transfer towards Group Treasury and therefore remains in the business unit it originated in. Group Treasury itself is also an important driver of credit spread risk via its HQLA investment portfolio and issuance activities. Risk appetite limits are set on a combination of metrics and accounting scopes and are cascaded to local ALCOs depending on the type of limit and materiality. Metrics and limits are monitored and reported monthly to ALCO Bank, local ALCOs and various stakeholders. 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. 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. FX translation (*) 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 (*) 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 Financial Risk RAS.
Foreign currency exposures banking books (*)
in € million | Foreign Investments | Hedges | Net exposures
| 2024 | 2023 | 2024 | 2023 | 2024 | 2023
US Dollar | 11,251 |  | 10,337 |  | -4,823 |  | -3,416 |  | 6,429 |  | 6,921 |
Pound Sterling | 1,674 |  | 1,659 |  | -484 |  | -156 |  | 1,190 |  | 1,503 |
Polish Zloty | 4,292 |  | 3,976 |  | -1,616 |  | -1,254 |  | 2,677 |  | 2,722 |
Australian Dollar | 3,373 |  | 3,620 |  | -2,161 |  | -2,273 |  | 1,212 |  | 1,346 |
Turkish Lira | 557 |  | 517 |  | 0 |  | 0 |  | 557 |  | 517 |
Chinese Yuan | 2,439 |  | 1,815 |  | -830 |  | -348 |  | 1,609 |  | 1,466 |
Russian Rouble | 396 |  | 375 |  | 0 |  | 0 |  | 396 |  | 375 |
Romanian Leu | 913 |  | 895 |  | -176 |  | -134 |  | 736 |  | 761 |
Thai Baht | 1,266 |  | 1,128 |  | -838 |  | -697 |  | 428 |  | 431 |
Other currency | 3,346 | 3,704 | -2,748 | -2,897 | 599 | 806
Total | 29,509 | 28,024 | -13,675 | -11,175 | 15,834 | 16,849
* The FX sensitivity is expressed as the FX spot equivalent position. Equity price risk in banking books (*) ING maintains a portfolio with substantial equity exposure in its banking books. 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 €1,679 million (2023: €1,509 million) and equity securities held at fair value through other comprehensive income (FVOCI) of €2,562 million (2023: €1,885 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 2024, the revaluation reserve equity securities increased by €664 million from €1,152 million to €1,816 million mainly due to revaluation of the shares in Bank of Beijing with €652 million. In 2024, the equity securities at fair value through OCI increased by €678 million.
Revaluation reserve equity securities at fair value through other comprehensive income (*)
in € million | 2024 | 2023
Positive re-measurement | 1,820 |  | 1,158 |
Negative re-measurement | -4 |  | -6 |
Total | 1,816 |  | 1,152 |
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 2024, 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. With respect to the trading portfolios, Trading Risk Management (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 remain 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: it 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 2024, there was one outlier for hypothetical P&L and zero outliers for actual P&L. The hypothetical outlier occurred in the third quarter of 2024, mainly due to higher interest rate and FX market movements. 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 percent 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. The trading book stress tests evaluate the impact on the bank’s trading book under severe but plausible stress scenarios, using a 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 | 2024 |  | 2023
Foreign exchange |  | Foreign exchange |
US Dollar | -93 |  | Japanese Yen | 61 |
Turkish Lira | 84 |  | Taiwan Dollar | -58 |
Korean Won | 62 |  | Romanian Leu | 58 |
Japanese Yen | 61 |  | Chinese Yuan | 49 |
Chinese Yuan | -37 |  | Hong Kong Dollar | -38 |
Most important interest rate and credit spread sensitivities at year-end (*)
in € thousand | 2024 |  | 2023
Interest rate (BPV) 1 |  | Interest rate (BPV) 1 |
Euro | -799 |  | Euro | -309 |
US Dollar | -198 |  | Czech Koruna | 71 |
British Pound | -189 |  | Korean Won | -41 |
Korean Won | -54 |  | US Dollar | -40 |
Philippine Peso | -54 |  | British Pound | -35 |
Credit spread (CSO1) 2 |  | Credit spread (CSO1) 2 |
United States | 193 |  | Germany | 405 |
Netherlands | -165 |  | Netherlands | 120 |
France | -113 |  | Korea | -111 |
Poland | 69 |  | Japan | 106 |
Germany | 49 |  | United Kingdom | 101 |
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 (*)
| 2024 | 2023
in € thousand | Corporate | Financial institutions | Corporate | Financial institutions
Credit spread (CSO1) 1 |  |  |  |
Risk classes |  |  |  |
1 (AAA) | -2 |  | -118 |  | 0 |  | 0 |
2–4 (AA) | -44 |  | -27 |  | 12 |  | 50 |
5–7 (A) | 49 |  | -246 |  | 57 |  | 50 |
8–10 (BBB) | 93 |  | -76 |  | 106 |  | 13 |
11–13 (BB) | 38 |  | -13 |  | 25 |  | -25 |
14–16 (B) | 23 |  | -12 |  | 17 |  | -4 |
17–22 (CCC and NPL) | 4 |  | 2 |  | -8 |  | -20 |
Total | 162 |  | -489 |  | 208 |  | 65 |
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 obligations 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 pre-defined boundaries. The following sections elaborate on the various elements of funding and liquidity risk: •Funding and liquidity risk framework; •Funding and liquidity risk management strategy and objectives; •Funding and liquidity adequacy and risk appetite; •Funding and liquidity risk indicators; •Liquidity stress testing; and •Contingency funding planning. Funding and liquidity risk framework (*) Macroeconomic and market environment are important considerations in ING’s F&L framework. The macroeconomic environment is comprised of 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 macroeconomic indicators, e.g. shifts in GDP, inflation rates, unemployment rates, and public deficit/surplus; •developments and risks arising from geopolitical tensions and 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; and •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. 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. 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 53% and 22% 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. ING’s long-term professional funding is well diversified across maturities and currencies. The main portion of long-term professional funding is euro and US dollar denominated, which is in line with the currency composition of customer lending. 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 regular basis to substantiate and reflect the management view on the current funding and liquidity position as well as the potential future challenges. The Liquidity Adequacy Statement is an important part of ING’s ILAAP process. Additionally, ING completes ad hoc funding and liquidity assessments if deemed necessary. In 2024, ING focused on the implementation of activities which steered the LCR higher and diversified the funding mix in order to ensure funding stability for the bank into the F&L risk appetite. 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, combined 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. The F&L risk appetite statements are translated into 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 (SFS) 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. 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 and 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-stress scenarios. The design of the framework is based on empirical evidence supplemented by expert judgment. 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; and •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 minimum) 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 monthly 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 2024, this included stress-test scenarios assessing the impact of a shutdown of US short- and long-term funding markets and FX markets. The internal stress scenarios and their corresponding results serve as input in the decision on holding additional contingency measures. Contingency funding planning (*) ING’s contingent F&L risks are addressed in its Contingency Capital and Funding Plan (CCFP).The objectives of the CCFP include the following: •Establishment of a monitoring framework to detect approaching contingent events as well as their impact on ING’s F&L position; •Provision of a plan for responding to various and increasing levels of a bank’s liquidity and capital shortfall under adverse and stressed conditions; •Designation of management responsibilities, crisis communication methods and channels, and reporting requirements; •Identification of contingent capital and liquidity sources that can be used under various and increasing adverse as well as stressed circumstances; and •Description of steps which should be taken to ensure that the bank’s sources of capital and liquidity are sufficient to fund scheduled operating requirements and meet the institution’s commitments with minimal costs and disruption. 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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