Jun 27, 2019
According to Mike Kane, Vice President of Ally Financial Services, these are some of the key questions that auto finance companies must answer to qualify customers for auto loans. Do I extend this loan? What can I do about thin-file applicants or subprime borrowers? How do I price risk accurately? This is not only crucial to identifying creditworthy customers but also to improving auto finance performance in a market plagued by high delinquency rates.
According to the Federal Reserve Bank of New York, around 7 million Americans are 90 days or more behind on their auto loan payments. Auto delinquency rate by subprime borrowers rose from 12.4% in 2015 to 16.3% in the second quarter of 2018. With the auto lending sector seeing rising defaults, what can lenders do to increase their lending volume while tightening their underwriting standards?
The answer is to move from limited metrics such as income proof and FICO scores to an AI-driven credit scoring model that leverages customers’ digital footprint to provide real-time insights into their repayment capabilities.
The Future of Auto Financing: AI-Enabled Credit Scoring
As ride sharing, self-driven cars and autonomous vehicles reshape the way consumers own and use vehicles, progressive auto lenders are looking to redesign the car financing experience by automating underwriting process through real-time scoring mechanisms. Here are three ways in which AI-driven credit scoring can help auto lenders enhance their lending process:
As AI improves nearly every facet of auto finance from data management to customer experience, a Machine Learning (ML) driven credit scoring solution can accurately assess and segregate high-risk applicants from lower risk ones -even in the absence of credit bureau data. This is especially useful in emerging markets where people conduct mostly cash transactions, lack banking records, and do not have assets to use as collaterals. Combining AI with the wealth of alternative data available such as smartphone metadata is helping increase credit access to the underbanked in such markets.
Leading credit card company Discover is in the process of implementing AI-based credit scoring to improve the accuracy of its credit decisions. In a successful trial, the company found that leveraging more data and AI techniques helped significantly reduce default rates without raising portfolio risk.
AI-based digital scorecards help accelerate decision time, reduce processing cost and improve customer experience. The benefits of AI-based underwriting don’t end here. It is also the key to approving more borrowers at lower rates as AI-enabled credit scoring provides more precise information to base credit decisions on. It allows lenders to identify borrowers who are likely to do well in the future, even though they might have a poor credit history, thereby expanding the customer base.
By combining a larger volume of credit with an efficient credit approval process, lenders can reduce costs and pass on the savings in the form of lower overall rates to customers, driving superior loyalty. Possible Finance, a Seattle based lender, charges much lower interest than its rivals by scoring individuals based on transaction history alone, without looking at their credit history.
Companies that use AI-enabled credit scoring to analyze massive amounts of data can speed up the approval process by delivering scores almost instantaneously. An AI-based credit scoring mechanism that leverages smartphone metadata is a great way to predict and analyze customer repayment behaviour, and onboard low risk and high-value customers. With scores delivered in real-time, an AI-based scoring model helps dig deeper into alternative data to make credit decisions in minutes. According to a study by the National Bureau of Economic Research, Fintech lenders are able to process loans using AI on the very same day as opposed to traditional banks that take weeks.
With 45 Million people lacking a credit score in the US alone, capitalizing on powerful AI and ML algorithms is the way forward for auto lending companies looking to empower customers with affordable financing options. Leveraging alternative data such as smartphone metadata not only helps lenders accurately identify risk but also instantaneously adjust the interest rate and loan amount to predicated risk profile. The result: the ability to acquire new segments of customers such as millennials or self-employed individuals who are new to credit - ultimately enhancing the quality of their lives and building a better future
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