Applying Moneyball to Auto Finance
There has been a tremendous evolution in sports during the past decade or so where players are being judged on new statistics — and past performance is being valued less — while using analytics to try and predict future performance is being used much more. The trend, known as “Moneyball,” is named after a popular book and movie chronicling how the general manager of the Oakland A’s embracing this mindset led to his team’s ability to continue to succeed despite having one of the lowest payrolls in professional baseball.
Other sports have adopted the concepts of Moneyball, and now it appears that the business world is taking notice, too.
Certainly, the auto lending sector, and the consumer finance industry, rely very heavily on past performance. The very decision of whether a prospective customer is to become an actual customer is based, in large part, on how well that prospective customer has done in making loan payments. Credit scores take into account borrowers’ past performance on other debts to help give lenders an idea of whether they will be able to make payments on a new loan or lease.
Bill collectors look at past performance to determine what type of collection strategy to use on past-due debtors. Marketing departments look at the effectiveness of old campaigns to determine how well a new marketing venture is likely to fare. Even employees are evaluated based on how well they have done in the past. Look at the tagline for the “Moneyball” movie poster: What are you really worth?
Granted, past performance is likely to bear some resemblance to what will likely happen in the future, especially when it comes to loan performance. But shouldn’t a loan application take into account that the borrower just got promoted or a new job or just had a baby?
Borrowers live on a curve, not on a straight line. Using a mirror to see behind them may have worked in the past (no pun intended) in guessing whether they would be able to make their payments in the future. But there is a wealth of analytics and processing power that can be put to use to do a better job of knowing whether the loan that borrowers are applying for today is going to be the one that causes them to start missing payments.
