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JANUARY 2019

ANALYTIC-DRIVEN DATAHelps Auto Lenders Mit igate Risk

& Become CECL Compliant

1 Analytic-Driven Data Helps Auto Lenders Mitigate Risk & Become CECL Compliant

Black Book White Paper

Analytic-Driven Data Helps Auto Lenders Mitigate Risk & Become CECL Compliant

The financial crisis of 2008 uncovered a systemic weaknesses in the recoginition of losses by lenders, particularly the timing of credit losses. During that period, even as macroeconomic conditions were beginning to detrioriate, the balance sheets of lenders appeared robust. The incurred loss model of loss reporting and provisioning requires that lenders need to provision only for losses already incurred, and not for losses they expect to occur. This results in delayed loss recognition by lenders.

In addition, this practice contributes to pro-cyclicality as lending increases during booms and reduces sharply during busts. Such changes in lending can worsen the length and impact of recession as lenders pull back from supplying credit at the time credit is needed the most. The regulators called for a revision of the accounting standards to diminish pro-cyclicality and improve provisioning for credit losses.

Even though the automotive markets remain steady as we wrapped up 2018, the Financial Accounting Standards Board (FASB) is looking to ensure that financial institutions have solid measures in place to ensure it has reserves for any future losses based on the life of each auto loan. As a result, it has instituted its new Current Expected Credit Loss (CECL) guidance.

The new requirements call for increased loan loss reserves and will require changes to portfolio management and lending practices. It will also require a significant amount of data capture, analysis and modeling to meet the implementation deadline of December 15, 2019.

In implementing changes appropriately, auto finance companies may realize a competitive advantage through CECL compliance. By leveraging granular data and building loan-level analytic models, auto lenders could have a better understanding of risks and improve return on capital.

What is CECL?Beginning with the fiscal year starting after December 15, 2019, lenders will have to determine Allowances for Loan and Lease Losses (ALLL) based on CECL models. As a result of CECL, automotive lenders will need to carry larger levels of capital in their reserves, prompting a change in their lending practices, as well as product and portfolio management. This update means lenders will need reserves set aside to account for future losses over the life of the loan. The lenders must consider information that is more forward-looking than is permitted under current GAAP.

This change will affect financial institutions to make significant increases to ALLL reserves. To be clear, the

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cumulative credit losses recognized by charge-off are the same for a loan whether the incurred loss model or the new CECL model is used. It is the timing of when losses are recognized that will differ as CECL requires expected lifetime losses to be calculated upon origination. The goal of CECL is to reduce volative reserve levels and a decline in cyclicality in lender portfolios and finance strategies.

With CECL’s requirement that lenders perform life-of-loan loss forecasting, as soon as a lender says yes to a loan, it must begin reserving for potential losses on that loan. This means each lender must have a much better understanding of the borrower’s financial condition, as well as accurate historical and residual collateral insight, when they make the loan.

Auto lenders have the opportunity to create improved loss models by having the most accurate and up to date credit and collateral data on their portfolios.

Probability of Default & Loss Given DefaultFASB does not specify any particular approach for CECL measurement and allows lenders to apply methods that reasonably reflect their expectations of the credit loss estimates.

However, it’s important to have capabilities that allows lenders to be flexible in choosing their methodology. To have such flexibility, detailed loan-level data is needed for more robust and forward-looking calculations. Capturing this data historically and on an ongoing basis is a prerequisite for accurate estimates and for evaluating methodologies.

A common CECL modeling framework may include lifetime PD, EAD and LGD models to create lifetime expected credit loss estimates. The method is also used by many risk management systems and within the Basel frameworks. This requires estimation of three components:

1. Probability of Default (PD): over the life of the loan2. Exposure at Default (EAD): loan balance at default3. Loss Given Default (LGD): loss amount at default, as percent of EAD

To develop a model driven by the PD method, a lender must evaluate and identify attributes that demonstrate the strong predictive power of the model through continual back-testing. Such models are based on explanatory variables such as credit scores, loan term, payment history, delinquent behavior, loan-to-value ratio etc.

Estimating probability of default and loss given default has always been important for lenders, CECL will make this an even more critical step for lenders with the necessity of accurate collateral values. Collateral values and Loan-to-Value (LTV) have a significant impact on both probability of default and loss given default.

Expected Loss PD EAD LGD= x x

3 Analytic-Driven Data Helps Auto Lenders Mitigate Risk & Become CECL Compliant

LGD estimates over the life of the loan could differ significantly by loan, depending on origination LTV, loan term, interest rate, vehicle depreciation forecast and economic conditions.

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5,000

10,000

15,000

20,000

25,000

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Origination LTV=120, Loan Term=60, Depreciation Forecast=15%

Remaining Balance

Depreciating Vehicle ValueLGD

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5,000

10,000

15,000

20,000

25,000

30,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Origination LTV=140, Loan Term=84, Depreciation Forecast=24%

Remaining BalanceDepreciating Vehicle Value

LGD

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5,000

10,000

15,000

20,000

25,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Origination LTV=120, Loan Term=84, Depreciation Forecast=15%

Remaining BalanceDepreciating Vehicle ValueLGD

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When a lender is looking to evaluate the amount of risk tolerance in a long-term loan, there are four things to consider: How much initial loan-to-value they want to establish; the rate of annual depreciation of the collateral; credit profile of the customer, whether subprime or prime; and determine if the Risk-Adjusted Return on Capital is sufficient.

Collateral data can help bring the right decision into focus. Data can help determine the loan-to-value ratio during origination, historical depreciation patterns and the projected residual forecast based on the desired time frame for the loan term. With CECL, leveraging the absolute most accurate data is key to identify the lifetime loss-given default for loans at origination and any month during the loan term.

Data ReadinessTo be comprehensive, CECL will require historical exposure data covering an entire credit cycle. Such data enables lenders to form robust modeling of how assets will perform over their full contractual lives, across a variety of potential economic scenarios.

One of the challenge lenders face for CECL modeling is the amount of data required to develop a reliable, forward-looking model. To forecast expected losses, auto finance institutions will need to capture detailed historical loan-level data from both internal and external sources. A critical data element needed is the Collateral Value for historical time periods, and the values at regular ongoing frequency (prefereably, monthly) during the life of the loan as well as collateral value projections (i.e., residuals) during the projected life of existing loans.

Whether you are utilizing pools of loans method or a granular methodology, it’s best to collect data at a granular loan-level basis. If historical credit scores and collateral values have not been retained, they can be easily acquired from available sources to perform modeling on historical data and apply to the portfolio going forward.

CECL’s Effect on Loan StrategiesLoss modeling should be based on the inputs most highly correlated to expected losses. For example, our research has shown that the performance of auto loans is highly correlated to credit score, LTV and loan term. As such, an auto loan with a credit score of 740, 60-month term and an LTV of 100% would have a low probability of default and low loss given default, if any. Whereas, an auto loan with a credit score of 600, 84-month term and an LTV of 120% would have a much higher probability of default and high loss given default.

Now that lenders will have to adjust and project for losses, lenders may look to tighten terms more. While this may not have a significant impact on prime customers, it may have an impact on Subprime applicants. This raises pressure on the risk manager on pricing decisions, and it may make used vehicles even more in demand for Subprime consumers particularly.

Type of Data NeededAt time of Origination Credit Score

TermInterest RateLoan AmountMacroeconomic DataCollateral Value

Portfolio Lifecycle Credit Score(preferably on monthly basis) Delinquency Status

PaymentsLoan BalanceMacroeconomic DataCollateral Value

At Time of Default Loan BalanceSelling Cost of AssetDefaul AmountCollateral Value

5 Analytic-Driven Data Helps Auto Lenders Mitigate Risk & Become CECL Compliant

Profitability certainly becomes in focus more specifically for lenders, which is why access to accurate historical and residual forecasts are critical here. Lenders may not add as much profit into the bottom line of a loan with CECL in place. However, with access to accurate projections of collateral based on proper historical data, lenders will have more confidence in projecting, managing and mitigating risk levels.

Taking a Closer Look at Economic ScenariosInstitutions subject to stress testing under Comprehensive Capital Analysis and Review (CCAR) or Dodd-Frank Act Stress Test (DFAST) could leverage their stress-test modeling processes for CECL as both processes rely on loss estimates that are conditioned on an economic outlook. However, stress tests are generally run annually, whereas CECL will require more frequent (monthly or quarterly) runs.

To accurately predict loan performance, a lender must model how its loans will perform in different economic conditions. It thus must have data that includes a significant downturn in the economy. Macroeconomic forecasts should be forward-looking, not based solely on the past data. A forecast for CECL is not the same as a CCAR or DFAST scenario, as it should represent the lender’s best estimate of macroeconomic expectations. After arriving at a reasonable and supportable macroeconomic forecast, projections of collateral value under those economic conditions need to be considered.

With the need to have accurate projections of collateral and loss forecasting, economic scenarios and conditions become even more important to the risk manager. New resources such as economic scenario-based residuals are now available to lenders and financial institutions with a portfolio of auto loans. These data resources map regulatory prescribed scenarios, enabling risk and portfolio managers the ability to analyze how vehicle values will respond under different macroeconomic CCAR and DFAST scenarios as outlined by the Federal Reserve. In addition, collateral value projections under a custom economic scenario considered by the lender for CECL are also available. These analytical-driven collateral value projections are built with extensive analysis of historical data taking into account impact from macro-economic variables, fuel prices, vehicle incentives, supply and demand in the used and new markets, and other criteria.

A key component of loss in auto loans is the severity of loss which is driven by the ability to predict residual values. Leveraging this new suite of collateral data with economic scenarios at a vehicle level will greatly enhance a lender’s ability to properly assess risk, and aid in evaluating capital adequacy.

SummaryCECL is being implemented to further insulate financial institutions from economic fatigue because of potential losses in loan portfolios. Lenders will need to keep additional reserves on hand to satisfy new CECL compliance, and to do so they will need access to accurate, analytic-driven collateral insight that will help lenders manage and mitigate risk efficiently and effectively.

When CECL takes effect in 2020, lenders will be required to calculate expected loss over the life of each loan and recognize the expected loss upon origination. Compliance will demand producing supportable forecasts, conditioned on the current environment, using detailed historical data.

The newly issued CECL accounting rules are expected to have a significant impact on auto finance companies by substantially changingn how credit losses are accounted for and estimated. Having the right level of loan-level data and accurate collateral data are important to gain a competitive edge.

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About Black Book Driven by the industry’s most sophisticated analytics resources available, Black Book valuation and residual modeling is the most accurate and actionable, based largely on data, analytics and insight that allow lenders, dealers, remarketers and other automotive professionals to base their inventory and portfolio decisions. The company leverages analytics, statistics, economic stress testing and editorial input to derive a forecasting model that helps drive better and more accurate decision making.

With highly accurate vehicle depreciation data available, combined with advanced analytical modeling and input from seasoned editors with a rich network of automotive expertise, Black Book data-driven innovation and resources are used help the entire automotive ecosystem maximize profit potential and limit exposure to risk and losses. Today’s analytical data helps Black Book understand exactly how each model and trim level depreciates based on historical patterns, also considering certain economic scenarios as well as global economic events. It is also known when manufacturers are redesigning, refreshing, or launching new models within segments. Combined with all this input, a highly qualified editorial team adds the right context to the data, based on input from a variety of trusted sources that include industry observers, dealers, remarketers, lenders, and a host of other authority figures who can help add the right perspective to what the data and projections are pointing toward.

Black Book is best known in the automotive industry for providing timely, independent, and accurate vehicle pricing information, and it is available to industry-qualified users through online subscription products, mobile applications, and licensing agreements. Since 1955, Black Book has

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