HOW TO USE AUTO LENDING RISK MANAGEMENT STRATEGICALLY

A comprehensive auto lending risk management strategy addresses every phase of the lending cycle from applications through servicing. Regular analysis of loan applications and portfolio performance with a focus on dealers, underwriters, credit policies, and lending regulations is critical to effective risk management. A modern loan origination solution with integrated analytics gives lenders powerful tools to do exactly that.

Let’s look at some of the risk factors inherent in lending processes and portfolios. We’ll see how analytics can help by identifying those risks, and by letting you adjust relevant policies and processes for strategic auto lending risk management.

Don’t Let Fraud Damage Your Portfolio

Auto loan fraud originations in 2018 are estimated to exceed $6.0 billion. Cars are financed in the US, then shipped overseas and sold for multiple times the purchase price, making this a very lucrative endeavor. Often the first payment is never made. More frequently, payments stop within the first 12 months. As many as 70% of early payment defaults are associated with some type of borrower misrepresentation in the loan application.

Auto Loan Fraud Originations by Year

Source: frankonfraud.com: Has Auto Loan Fraud Replaced Car Theft In The US?

Misrepresentation or falsification of employment and income, and synthetic identity are popular methods of theft. But just as digital technology has greatly reduced outright car theft, fintech is preventing auto theft by fraud. Machine learning or artificial intelligence (AI) applied to analyze millions of auto loan applications is able to precisely identify loan applications that contain misrepresentations.

Dealers, intent on sales, not applicant quality, can also play a hand in fraud. Analysis of some portfolios indicates that as few as 3% of dealers are responsible for 100% of fraudulent applications.

Your auto lending risk management strategy needs to account for the increasing prevalence of theft by auto finance. Fraud recognition eliminates risk before it damages your portfolio. Every auto lender should include this modern fintech capability for better auto lending risk management.

Fraud recognition eliminates risk before it damages your portfolio.

Analytics Provides a Focused Understanding of Portfolio Performance

We’ve previously written about the value of loan origination solutions that include integrated analytics. Rich analytics capabilities give lenders summary and granular insight into lending process efficiency and portfolio performance. Analytics can identify potential and actual sources of risk throughout your lending practice. There’s really no limit to the variations of analysis you can do regarding your loan portfolio. Let’s focus on four areas: Dealer performance, underwriter experience, regulatory compliance, and portfolio performance. For each, we pose several questions that can be answered by analytics, with the goal of making adjustments in policies or processes to reduce or eliminate the risks.

Dealer Performance

• Are any dealers responsible for an unusual number of delinquencies or defaults? What does the long-term dealer history show? Is the trend consistent, or was there a recent jump that might be explained by a new finance manager focused on boosting sales? A strong correlation demands investigation and modification of any automated processes that may be involved in loan approvals from these dealers.
• Does dealer segmentation analysis show better or worse loan performance among dealers? Are there any particular attributes or terms that correlate with performance? Which dealers merit additional attention and investment to increase lending opportunity? Which dealers should be dropped?

Underwriter Experience, Skills, and Workflow

• Are delinquencies and defaults tied to overrides by particular underwriters? Are there any common attributes among the suspect loans, such as geography, vehicle type, terms, or stipulations? Is underwriter experience a factor in the these questionable lending decisions? Are any incentives biasing decisions? Does your underwriting workflow have appropriate checks and reviews to prevent such overrides?
• Are there any bottlenecks or manual processes in the workflow that lead to variations or inconsistencies in underwriter decisioning? Could any of the manual steps be replaced by decision rules? Would better training or an added review step improve the quality and consistency of decisions?
• Are delinquencies and defaults associated with insufficient credit data? Could alternative credit data be used to obtain a more accurate profile of applicant financial strength?

Regulatory Compliance

• Are there any overrides that do not comply with state usury rules?
• Do all loans subject to the Military Lending Act comply with the maximum interest rate allowed?   
• Have all adverse actions notifications been sent on time and include proper reasons?  
• Do you maintain a secure digital copy of notifications in the event of an audit?

Portfolio Performance

• Is risk balanced across your portfolio, or are there concentrations of loans with terms longer than 60 months, LTV ratios above 100%, or unusually low-interest rates?
• Does analysis of any portfolio segment in light of the current economic climate indicate need to tighten policies?
• Are formulas such as vehicle value, debt-to-income (DTI), payment-to-income (PTI), and loan-to-value (LTV) consistently calculated across all phases of the lending cycle, or do inconsistencies distort comparisons when analyzing portfolio segments?

Strategic Auto Lending Risk Management Requires Analytics

Modern loan origination software that incorporates analytic capabilities gives lenders the ability to identify and mitigate risk throughout the lending lifecycle. Analytics allows you to continually take the pulse of your lending practice and determine where and when adjustments need to made. In today’s hyper-competitive lending market, analytics is a requirement for strategic auto lending risk management.