How Auto Lenders Can Make Effective Underwriting Decisions

John M. Giamalvo, the head of Subprime Automotive at Trust Science®, compared the pandemic with a “positive perfect storm” for Buy-Here-Pay-Here (BHPH) dealers in the February issue of the BHPH Dealer. The influx of over 10 million immigrants in the last ten years has widened the horizons of auto-lenders. Owing to the pandemic, the usage of consumer mobility options (like Uber and Lyft) has reduced by 55-75% as consumers are hesitant to share vehicles with strangers. This has increased the demand for personal transportation like cars. On the other hand, the fuel prices have gone as high as $3.70 per gallon, squeezing the monthly spending of consumers on their cars. With “work from home” becoming a permanent work model for some industries, fewer people use their cars to commute to work. This has reduced the average miles driven by more than 2200 miles per year, and this number is only expected to fall. With fewer miles, customers will be spending less on their cars’ fuel, thereby saving as much as $75 per month in the cost of car ownership. This means a lower depreciation rate for auto-lenders if the vehicle is returned. Along with this, the new COVID Relief Bill passed by the US government will give $600-1200 per month to qualified recipients. The steady interest rates in the auto-lending ecosystem coupled with the increased ability of customers to pay have cultivated a favorable business environment for auto-lenders. At the same time, auto-lenders need to expand to the much bigger subprime and invisible consumer market, which has been expanding simultaneously. Therefore, auto-lenders need to uncover these “invisibles” by using alternative data sources and other data sets with the help of machine learning (ML)  and artificial intelligence (AI) technologies. Some of the ways in which AI can help auto-lenders to make more effective underwriting decisions and increase their profits:

AI can make the vehicle depreciation curve more accurate

A car loses its value the minute it leaves the showroom. During the first year, the average rate of depreciation is around 20%, after which it stabilizes to 15% per year each consecutive year. The rate of depreciation depends on the model, mileage, and region. The depreciation curve can be used in estimating the loss, i.e., the value that lenders can get at repossession vs. the value of the loan still withstanding. AI technologies can make the depreciation curve more accurate using actual resale data on loans, regions where the cars are used (example: snowy regions can cause more wear and tear), and the vehicle’s primary purpose. AI can build models that can induce automated loans based on depreciation curves and data analytics, thus, retaining financially stable customers. Besides this, AI models can identify customers trading their cars for new dealerships because their loan is greater than the car’s value. Using advanced AI analytics, auto-lenders can identify such customers and offer remedies, like new car purchases or new loans.

AI can reveal creditworthy Invisible Primes™

The use of AI tools can open the auto-lending markets to many thin-file applicants called subprimes or invisibles. Post-2009 recession, there has been a shift in the auto-lending industry, lending more and more to subprime borrowers than ever. However, the lenders have given more debt to borrowers than their ability to pay. This puts such borrowers at the risk of losing their cars if they default on their loans. In such a circumstance, the use of AI in auto-lending decisions is more advantageous than ever. AI tools can effectively reveal “credit invisibles”/“unscorables” and evaluate their true creditworthiness by aligning traditional credit data with vast data from alternative sources like banking. Using AI in their underwriting decisions, auto-lenders can attract creditworthy subprime borrowers and reduce the probability of default.

Trust Science® is an industry leader in its ability to use AI/ML models that grow with your business, harnessing numerous data sources to deliver meaningful, explainable, and fully compliant risk scores, even on those that were conventionally thought of as credit invisibles. We have a unique ability to score millennials who form the largest demographic segment of the auto market. Most of them lack credit history due to generational differences in credit use. Our AI/ML models collect massive amounts of consented consumer data to calculate an accurate credit score. For lending leaders who need to score these financially stressed or underbanked borrowers fairly and ethically, Trust Science® offers a fully compliant, data-driven, AI-powered solution. To learn more about how Trust Science® can help with your underwriting process, visit us at https://www.trustscience.com/