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 dollars per loan. The effect is substantial: at the time, the average loan principal was around 9,000 dollars. 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%.
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%, 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 dollar increase in profit per high-risk loan.
We estimate a similar increase in profitability for lower-risk loans, but the mechanism is different. Required down payments and close rates changed little for lower-risk applicants. Instead, consistent with the model, we observe that car quality and average loan sizes increased substantially. Default rates did not change much, and hence the larger loans had a substantial profit impact due to the high interest rates charged in this setting. For lower-risk loans, the increased “size” of each investment is largely responsible for the dollar increase in profit. Hence, the two channels through which credit scoring theoretically increases profitability in the model both appear to be operative and substantial in the data.
Essentially, we see a microcosm of what happened in the larger economy over the past few decades: People with steady payment histories and low levels of outstanding debt relative to their available credit got better loan terms, and were therefore able to borrow more money (because their interest rates went down). They got bigger, nicer cars, and auto lenders became more profitable.
The financially marginal, on the other hand, found that their financial lives got harder still. Their poor credit histories meant that they could no longer get loans, or they could get them only at painfully high rates of interest. They would have had to drive less car -- or, possibly, only whatever they could afford to pay cash for.
Credit has long been thought of as a democratizing force. It enabled ordinary Americans to buy houses, cars and other amenities that had previously only been available to those with substantial capital. But over the last few decades, that process has been reversed. Financial irresponsibility is, of course, one of the things that drives a bad credit score. But so does unstable, low-skilled employment and a thin margin of financial error between you and the basics of American middle-class life. So what we’re seeing is a redistribution of benefits not just from the financially irresponsible to the financially responsible, but also from the labor market’s “have nots” to its “haves.”
Those on the left see this problem and call for the reinstitution of usury laws to cap the amount that those with low credit scores can be asked to pay. And, of course, that would keep those 25-percent-interest auto loans from bleeding the family budget dry. But it would not put the Big Data genie back in the bottle; loan companies would still know that these people are bad risks. They would substitute even higher down-payment requirements -- or outright denial of the loans -- for the higher interest rates they’re now charging.
Knowledge is power, as they say. But that power is not necessarily equally distributed.
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