Business Excellence Awards
Please Join PBN to Celebrate the 2014 Business Excellence Award Winners on Novem ...
The other day, I got to wondering something: What is the effect of automated payments on credit scores? Automated payments, I reasoned, reduce late payments among the people who are basically responsible budgeters but terrible at remembering to mail their bills on time every month. Those people should see their credit scores increase as they rack up fewer late payments to creditors.
Alas, the Internet seems to be silent on this point, or at least my Google-Fu was not good enough to discover any research that could shed light on my theory. But I did stumble across an interesting paper put out by Rand Corp. last year on the impact that credit scores have on auto lending.
Even though I lived through it, I find it a bit hard to realize how new the credit-scoring revolution actually is. Credit scoring has been around for a while – the Fair Isaac Corp. was founded in the late ’50s – but it wasn’t until the information technology revolution of the 1990s that companies got enough data storage and computing power to start slicing and dicing their loan portfolios by credit score. The auto-financing company Rand studied used uniform pricing and traditional interviews for loan issuance as late as 2000.
Here’s what happened when it shifted to a more sophisticated credit-scoring model: higher interest rates and down-payment rates for risky borrowers, better rates for those with better scores.
We find that the adoption of credit scoring, and the changes it enabled in lending increased profits by roughly $1,000 per loan. The effect is substantial: at the time, the average loan principal was about $9,000. We also analyze an alternative measure of profitability, the profit (or “net revenue”) per loan applicant.
After the adoption of credit scoring, loan originations fell, but the profit per applicant still increased, from $751 to $1,070, or by roughly 42 percent. Consistent with the theoretical model, we identify two distinct channels through which better information improved loan profitability. First, credit scoring allowed the lender to set different down-payment requirements for different applicants. High-risk applicants saw their required down payment increase by more than 25 percent, creating a higher hurdle to obtain financing. Close rates for this group fell notably, and also default rates, consistent with the idea that higher-risk borrowers were screened out by the higher down-payment requirement. Translating this into dollar terms, we find that improved loan repayment was largely responsible for what we measure to be about a $1,200 increase in profit per high-risk loan. We estimate a similar increase in profitability for lower-risk loans, but the mechanism is different.