How does AI Revolutionize the Auto Lending Industry in 2024?

America sold 3.12 million cars in 2023. And, dealers expect an even better number this year thanks to rising vehicular demand. Post the 2020 pandemic, the auto industry suffered major blows in the form of rising labor & material costs and lower consumer demand. However, the situation is improving.

According to the Automotive Industry Portal, Markline, U.S. auto sales witnessed approximately 5% increase in March. A total of 1,455,030 units of vehicles were sold in March. It is a 15.5% rise from February 2024. If you look at the numbers from a year ago, there is an increase of 5.1%.

There was a time before credit bureaus when the auto lending industry depended on the lenders' experiences and preferences. However, FICO and other credit reporting agencies streamlined the lending process, allowed for more growth, and reduced risk for lenders.

Another revolution is set to take over the American automotive industry in 2024. Machine learning has the potential to transform auto lending like never before. Auto lenders and subprime car financing companies will use AI to win more loyal customers, detect fraud, and reduce their risk appetite. Many AI startups are helping lenders revolutionize their lending process and creating complex algorithms to process hundreds of variables before approving the loan application.

1. More Advanced Algorithm for Loan Review Process

Auto lenders use logistic regression models that analyze many variables to determine the creditworthiness of their applicants. Usually, they will consider the applicant's credit history, open lines of credit, current income and employer, ability to make a down payment, and many other indicators that help them ascertain the risk of loan approval.

With machine learning, a lender can process several different applicants quickly, and that too, by considering thousands of variables in a short period. It will also consider many data sources that traditional models may tend to overlook and thus, reduce biases in the auto lending process.

2. Reduced Risk

The lender will learn more about the applicant with a wider data set. For example, AI can help lenders know whether the applicant has experienced bankruptcy. It can also help predict whether their current spending habits and credit situation can lead to bankruptcy.

An advanced machine learning model can also consider an applicant's residential history and determine any unpaid rent or utility bills. It can also look into open court cases and help understand how the outcome of the cases affects the applicant’s financial standing in the community.

3. Eliminating Bias

AI in the auto lending process can eliminate bias that often stems from the personal preferences of the lender. For example, a lender may use a "non-standard" factor such as the area of residence to ascertain the borrower's creditworthiness. Lenders may categorize applicants as favorable and unfavorable simply because they belong to a specific age group or ethnicity.

AI can eliminate personal biases and present a more practical and reasonable picture to the lenders, thus, ensuring better profitability for the auto financing company. Machine learning models can provide an expanded data set for a more comprehensive decision-making process.

A person with a good credit score can get a traditional auto loan. Banks, credit unions, and online auto financing companies are eager to provide credit to borrowers with a steady history of payments. But, what about car buyers with zero credit history? And, how can bad credit borrowers seek a loan to buy a new car?

It is a fact that credit scores are an excellent indicator for learning more about an applicant. However, it is difficult for lenders to decide for applicants who do not have an established credit history. It is a problem for young car buyers who do not own credit cards but are used to making their purchases with debit cards and checking accounts. As a result, younger loan applicants receive dreadful outcomes on traditional credit models.

Traditional lending models often categorize zero credit car buyers and bad credit borrowers as "unfavorable". However, AI and machine learning can help lenders ascertain their creditworthiness by considering a large data set.

Lenders can use AI to detect underserved markets and target applicants ignored by traditional models. Thus, ensuring more business for the lenders and improved car buying capacity for the borrowers.

In conclusion, machine learning can allow lenders to expand their market and even lead to economies of scale, thus reducing auto loan rates. Lenders can offer more tailored financing solutions to car buyers and a more personalized experience for their target market.

:- Posted by Admin on 21st April, 2024