Bias in auto lending occurs when an automotive dealer and lender make decisions about a loan and deny credit or impose “non-standard” terms for reasons other than the borrower’s creditworthiness.
Vehicle shoppers can encounter bias, such as receiving favorable loan terms because they appear “reliable” or not receiving loans simply because they live in the “wrong” area.
A proper decision process eliminates all considerations of race, religion, marital status, gender/gender identity, age or disability. Bias leads to less than optimum decision-making and it can affect both the dealer/lender and the consumer adversely.
Bias Remains a Significant Problem
According to the Federal Reserve Bank of Chicago, there exists strong evidence of racial and ethnic discrimination when auto financing is arranged through auto dealers. Auto loans obtained through indirect channels for Black, Hispanic and Asian borrowers have higher interest rates than those for non-Hispanic White borrowers. This disparity in rates results in Black, Hispanic and Asian borrowers often paying hundreds – or sometimes even thousands – of extra dollars in loan payments relative to their white counterparts. This can result in Black borrowers paying nearly $1,400 in additional interest over the lifetime of an average auto loan. https://www.chicagofed.org/publications/profitwise-news-and-views/2023/discrimination-auto-loan-market
Bias is not just about personal characteristics, though. Lending also can be biased toward regular wage earners versus the self-employed and gig economy workforce. On “paper,” some occupations look better than others. Much of that bias arises from the documents provided with the loan application and how those documents are processed. Improving the quality of the documents’ data is key to removing bias.
Where AI Can Help
The key to reducing the effect of bias is to understand the consumer AI process. There are three major elements:
- The expansion of data available for decision-making.
- The models that detect relationships in data.
- The automation of decision-making based on model predictions of loan profitability.
“Big Data” makes it possible to collect much more information of types not available before. Traditional approval processes depend on the information on loan applications, credit bureau scores and previously collected information. However, those sources are limited. Loan applications can contain fraudulent data. Credit bureaus build credit scores, ignoring significant facts.
Now, however, a tsunami of data is available to assess loan applicants – information from payment systems, social networks, web presence and more. The question is, what is the “right” data for decision-making?
The availability of a broader spectrum of data cuts both ways. More data types support better decision-making by identifying correlations between creditworthiness and other factors. On the other hand, there’s a risk that some of those factors – such as gender or name (which might hint at ethnicity) – ought not to be considered in the lending process.
What Type of Data Needs to Be Uncovered
There’s plenty of supplemental data to improve the loan approval process, but the starting point is the data provided by the applicant. The use of AI and ML allows us to understand the documents and extract and classify the underlying data into a deal jacket, greatly reducing the need for manual intervention and improving speed and accuracy. This provides a better customer experience and, for auto loans, gets the dealer paid faster.
The second element in Lending AI is Machine Learning (ML). In ML, a model is created using a sample data set (training data). The model can then make usable predictions from a new dataset with characteristics similar to those of the training data.
The essential question answered is, “What attributes of the subject best predict an accurate outcome?” Or, for consumer lending, “How do we know how much we should lend to whom, at what interest rate, and what is the risk of default?”
AI in consumer lending can be a “win-win.” It can radically reduce bias and eliminate risk for borrowers and lenders alike. It can improve lending profitability and reduce the risk of loss. It can enhance borrowers’ ability to get appropriate loans and manageable terms and reduce the risk of default.
Jessica Gonzalez (pictured, above left) is director of lending strategies for InformedIQ.com, an AI start-up company serving the financial services industry with a sophisticated Software-as-a-Service platform.
