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Significant Accounting Policies | 6 Months Ended
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Jun. 30, 2024
Accounting Policies [Abstract] |
Significant Accounting Policies | Significant Accounting Policies Basis of Presentation The accompanying unaudited, consolidated financial statements of SLM Corporation (“Sallie Mae,” “SLM,” the “Company,” “we,” or “us”) have been prepared in accordance with generally accepted accounting principles in the United States of America (“GAAP”) for interim financial information. Accordingly, they do not include all the information and footnotes required by GAAP for complete consolidated financial statements. The consolidated financial statements include the accounts of SLM Corporation and its majority-owned and controlled subsidiaries after eliminating the effects of intercompany accounts and transactions. In the opinion of management, all adjustments considered necessary for a fair statement of the results for the interim periods have been included. The preparation of financial statements in conformity with GAAP requires management to make estimates and assumptions that affect the amounts reported in the consolidated financial statements and accompanying notes. Actual results could differ from those estimates. Operating results for the three and six months ended June 30, 2024 are not necessarily indicative of the results for the year ending December 31, 2024 or for any other period. These unaudited financial statements should be read in conjunction with the audited financial statements and related notes included in our Annual Report on Form 10-K for the year ended December 31, 2023 (the “2023 Form 10-K”). Consolidation The consolidated financial statements include the accounts of the Company and its majority-owned and controlled subsidiaries after eliminating the effects of intercompany accounts and transactions. We consolidate any variable interest entity (“VIE”) where we have determined we are the primary beneficiary. The primary beneficiary is the entity which has both: (i) the power to direct the activities of the VIE that most significantly impact the VIE’s economic performance; and (ii) the obligation to absorb losses or receive benefits of the entity that could potentially be significant to the VIE. Allowance for Credit Losses We maintain an allowance for credit losses for the lifetime expected credit losses on loans in our portfolios, as well as for future loan commitments, at the reporting date. In determining the lifetime expected credit losses on our Private Education Loan portfolio loan segments, we use a discounted cash flow method. This method requires us to project future principal and interest cash flows on our loans in those portfolios. To estimate the future expected cash flows, we use statistical loan-level models that consider life of loan expectations for defaults, prepayments, recoveries, and any other qualitative adjustments deemed necessary, to determine the adequacy of the allowance at each balance sheet date. These cash flows are discounted at the loan’s effective interest rate to calculate the present value of those cash flows. Management adjusts the effective interest rate used to discount expected cash flows to incorporate expected prepayments. The difference between the present value of those cash flows and the amortized cost basis of the underlying loans is the allowance for credit losses. Entities that measure credit losses based on the present value of expected future cash flows are permitted to report the entire change in present value as credit loss expense, but may alternatively report the change in present value due to the passage of time as interest income. We have elected to report the entire change in present value as credit loss expense. We estimate future default rates used in our current expected credit losses at a loan level using historical loss experience, current borrower characteristics, current conditions, and economic factors forecasted over a reasonable and supportable period. At the end of the reasonable and supportable forecast period, we immediately revert our forecasted economic factors to long-term historical averages. We estimate future prepayment speeds used in our current expected credit losses at a loan level using historical prepayment experience, current borrower characteristics, current conditions, and economic factors forecasted over a reasonable and supportable period. At the end of the reasonable and supportable forecast period, we immediately revert our forecasted economic factors to long-term historical averages. The reasonable and supportable forecast period is meant to represent the period in which we believe we can estimate the impact of forecasted economic factors in our expected losses. We use a two-year reasonable and supportable forecast period, although this period is subject to change as our view evolves on our ability to reasonably forecast economic conditions to estimate future losses. In estimating future default rates and prepayment speeds in our current expected credit losses, we use a combination of expected economic scenarios coupled with our historical experience to derive a base case adjusted for any qualitative factors (as described below). We also develop an adverse and favorable economic scenario. At each reporting date, we determine the appropriate weighting of these alternate scenarios based upon the current economic conditions and our view of the risks of alternate outcomes. This weighting of expectations is used in calculating our current expected credit losses recorded each period. In estimating recoveries, we use both estimates of what we would receive from the sale of defaulted loans as well as historical borrower payment behavior to estimate the timing and amount of future recoveries on charged-off loans. In addition to the above modeling approach, we also take certain other qualitative factors into consideration when calculating the allowance for credit losses, which could result in management overlays (increases or decreases to the allowance for credit losses). These management overlays can encompass a broad array of factors not captured by model inputs, including, but not limited to, changes in lending policies and procedures, including changes in underwriting standards, changes in servicing policies and collection administration practices, state law changes that could impact servicing and collection practices, charge-offs, recoveries not already included in the analysis, the effect of other external factors such as legal and regulatory requirements on the level of estimated current expected credit losses, the performance of the model over time versus actual losses, and any other operational or regulatory changes that could affect our estimate of future losses. The evaluation of the allowance for credit losses is inherently subjective, as it requires material estimates that may be susceptible to significant changes. If actual future performance in delinquency, charge-offs, and recoveries is significantly different than estimated, or management assumptions or practices were to change, this could materially affect the estimate of the allowance for credit losses, the timing of when losses are recognized, and the related provision for credit losses in our consolidated statements of income. When calculating our allowance for credit losses and liability for unfunded commitments, we incorporate several inputs that are subject to change period to period. These include, but are not limited to, CECL model inputs and any overlays deemed necessary by management. The most impactful CECL model inputs include: •Economic forecasts; •Weighting of economic forecasts; and •Recovery rates. Of the model inputs outlined above, economic forecasts, weighting of economic forecasts, and recovery rates are subject to estimation uncertainty, and changes in these inputs could have a material impact to our allowance for credit losses and the related provision for credit losses. In the second quarter of 2024, we implemented a loan-level future default rate model that includes current portfolio characteristics and forecasts of real gross domestic product and college graduate unemployment. In the second quarter of 2024, we also implemented a future prepayment speeds model to include forecasts of real gross domestic product, retail sales, SOFR, and the U.S. 10-year treasury rate. These models reduce the reliance on certain qualitative overlays compared to the previous default rate and prepayment speeds models. Prior to these changes, our loss models used forecasts of college graduate unemployment, retail sales, home price index, and median family income. Both the future default rate model and the future prepayment speeds model are used in determining the adequacy of the allowance for credit losses. The combined impact of these model enhancements and the changes in the related qualitative overlays did not have a material impact on the overall level of our allowance for credit losses. We obtain forecasts for our loss model inputs from Moody’s Analytics. Moody’s Analytics provides a range of forecasts for each of these inputs with various likelihoods of occurrence. We determine which forecasts we will include in our estimation of allowance for credit losses and the associated weightings for each of these inputs. At June 30, 2024, December 31, 2023, and June 30, 2023, we used the Baseline (50th percentile likelihood of occurring)/S1 (stronger near-term growth scenario - 10 percent likelihood of occurring)/S3 (unfavorable (or downside) scenario - 10 percent likelihood of occurring) and weighted them 40 percent, 30 percent, and 30 percent, respectively. Management reviews both the scenarios and their respective weightings each quarter in determining the allowance for credit losses.

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