AI in Auto Finance: Lessons from Lending
Businesses across every industry today are turning to machine learning and artificial intelligence to make better sense of the massive data at their fingertips. Yet discussions at the recent Auto Finance Summit revealed a surprising fact: auto lenders are struggling to use these modern techniques and technologies to balance speed of making credit decisions with risk of credit defaults. In short, while AI promises to provide better, faster, and more accurate decisions, few of these lenders are pulling the trigger on AI implemention.
But a 2019 Harvard Business Review Analytic Services report reveals auto lenders are not alone in this hesitation. Their survey showed that while 68 percent of executives believe AI will become a competitive differentiator for them in the next year, only 15 percent actually have AI-powered analytics in place. One explanation for this seeming contradiction lies in where companies are focusing AI investments, namely on developing new products and services rather than revamping their existing decisioning processes. Another is that, while these same executives rank improving employee productivity as their second most beneficial use case for AI, assessing the creditworthiness of customers ranked last. What many of them fail to realize is that applying AI to assessing creditworthiness can help them achieve the employee productivity they seek.
Take TBB, for example, a lender whose recent application of AI proved this to be true. Like many auto lenders, TBB’s manual processing of loan applications made underwriting a costly, slow, and rigid process. And, just like auto lenders, the company wanted to increase the capacity of existing staff while improving the speed, accuracy, and consistency of its credit decisions. By transforming its manual processes into machine learning models, rules, and workflows, TBB now automatically declines applications with very high credit risk and automatically approves those with very low risk. This means that the TBB team can now focus on applications that require further verification, essentially doubling the output of its staff.
“This new process enables our risk managers to easily test, implement, and fine-tune different loan amounts and rates to help achieve our business objectives,” says Andrew Schmidt, Head of Analytics & Data at TBB. “Applying AI to our credit decisioning has not only boosted the accuracy and consistency of those decisions, it has made our staff far more efficient.”
Enova Decisions works with both captive and indirect auto lenders to apply AI to their manual credit processes, helping them to overcome their challenges with fraud and credit decisioning and realize the kind of benefits TBB achieved. Contact us today to learn how.