A regular practice of auto loan portfolio risk assessment allows lenders to continually evaluate and adjust their credit policies in response to market dynamics. To do this you’ll need analytics and a loan origination solution that lets you quickly make modifications to credit policies and decisioning processes. With the recent downturn in auto loan originations expected to continue, auto loan portfolio risk assessment is an essential practice if you expect to remain competitive and profitable.
Analytics is the Key to Auto Loan Portfolio Risk Assessment
A loan origination system with integrated analytics capabilities allows you to discover correlations between borrower attributes, loan terms, and loan performance. Based on this information, you can then modify credit policies and practices to mitigate risk factors. Begin the assessment by applying analytics to deals in your portfolio that are in default, with the goal of identifying factors that are strongly predictive of charge-offs. There’s an infinite number of approaches, but we’re going to suggest two initial categories of analysis: Borrower attributes and deal structures.
Assess Borrower Risk
Analyze borrower attributes of defaults in your portfolio with the goal of identifying risk factors strongly associated with defaults. This is a good exercise in evaluating the scorecard criteria you initially used for loan decisioning. The number of borrower attributes you have available will affect the types of analysis you’ll be able to do. The table below lists several examples of analytical questions used to assess various categories of borrower risk and potential steps for mitigating those risks.
|Analytic Assessment||Mitigating Action|
|Which attributes are strong indicators of default—bureau scores, average number of months in file, payment histories, types of debt, or any other significant attribute you may include in loan decisioning?||Recalibrate your scoring model, modify decisioning rules, and tighten policies.|
|Is fraud a factor in first pay defaults (FPD) and early payment defaults (EPD) (within the first six months)?||Incorporate fraud services which verify information provided by applicant and/or dealer before moving to the underwriting phase.|
|Do applications from certain dealers equate to low probability of default and others to high probability?||Ignore high-risk dealers; Push for more book deals with reputable dealers.|
|Is there a correlation between defaults and overrides made by any particular underwriter?||Review the underwriter’s experience and decision skills. Evaluate compensation plan. Implement decision rules, such as requiring an additional level of approval in the workflow for overrides.|
|Are there any signs of growing risk linked to specific attributes such as geography that warrant closer attention, like the closure of a major employer?||Implement a plan to monitor that loan segment, develop alternative payment options, and proactively contact borrowers to assess risk of default.|
|Are any deals made exclusively on the basis of good to exceptional scores suddenly showing risk?||Pair bureau data with alternative credit data to make better quality lending decisions based on detailed, accurate, and current assessments of creditworthiness.|
These, and hundreds of other variations, based upon the attribute data you acquire, can be run against the various tiers or segments in your portfolio to identify borrower attributes that correlate with risk of default. Based on your analyses, you can then apply that insight in two ways:
First, you may want to modify or fine-tune your loan decisioning to decline applicants with similar risks. If your loan origination system has decision rules capabilities, much of the decisioning can be automated. In that way, not only are you reducing risk, you’re also achieving a more efficient loan origination process.
Second, the analyses can provide a predictive model for portfolio performance. As an example, if analysis indicates millennials with student debt greater than $60,000 or single loans or more than $25,000 tend to default around the 30th month, you could then apply this model to borrowers who meet these criteria and who have not yet reached the 30th month payment to forecast potential charge-offs.
Assess Deal Structures
You can also conduct auto loan portfolio risk assessment by analyzing deal structures. This time you’re looking for factors that correlate with a high probability of default. Here are some example of questions to ask as part of your analytical assessment, and some sample strategies for reducing or eliminating those risks.
|Analytic Assessment||Mitigating Action|
|Which factor has the strongest correlation with defaults for subprime borrowers: Interest rate, loan, term, down payment, valuation, make, model, or another deal element?||Tighten credit policies to avoid applicants below certain credit scores. Adjust deal structures to neutralize the factor’s influence on defaults.|
|What is the average exposure at time of default for deals based on interest rate, loan, term, down payment, valuation, make, model, or other significant factors?||Develop multiple alternative payment plans to postpone, eliminate (ideal), or at least reduce default exposure and proactively consult and work with the borrower to adopt alternative payment plans.|
Again, analytics are the key to identifying factors that strongly correlate with defaults. You can also create forecast models based on your analysis. In the example above, an average exposure at time-of-default model could be applied to estimate potential charge-offs, based on one or more of the deal factors.
Auto Loan Portfolio Risk Assessment Should be a Regular Practice
Auto loan portfolio risk assessment should be a regular practice for any lender who strives to remain competitive and profitable. The wealth of applicant data available to evaluate creditworthiness and the integrated analytics capabilities of modern loan origination systems lets lenders conduct continuous portfolio assessments. You can now make data-driven decisions to respond to any market condition, while improving your credit policies and decisioning processes.
defi SOLUTIONS analytics and loan origination experts welcome the opportunity to discuss how we can help reduce risk exposure in your portfolio. We are professional lending process problem solvers. Take the first step toward reducing the risk of delinquency by contacting our team today or registering for a demo of defi Analytics and defi LOS.
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