The NY Times this morning wrote about three new companies that I have never heard of. Each of them seeks to make loans to people who they believe will pay them back. These potential lenders rely on crunching "big data" to determine if an individual is "worthy" of a loan. A direct quote:
"None of the new start-ups are consumer banks in the full-service sense of taking deposits. Instead, they are focused on transforming the economics of underwriting and the experience of consumer borrowing — and hope to make more loans available at lower cost for millions of Americans.
Earnest uses the new tools to make personal loans. Affirm, another start-up, offers alternatives to credit cards for online purchases. And another, ZestFinance, has focused on the relative niche market of payday loans.
They all envision consumer finance fueled by abundant information and clever software — the tools of data science, or big data — as opposed to the traditional math of creditworthiness, which relies mainly on a person’s credit history.
The new technology, proponents say, can open the door to far more accurate assessments of creditworthiness. Better risk analysis, they say, will broaden the lending market and reduce the cost of borrowing."
Here is the key piece of the article that caught my eye;
"The data scientists focus on finding reliable correlations in the data rather than trying to determine why, for instance, proper capitalization may be a hint of creditworthiness."
What I like about these new firms is that they are using all available data to impute a person's permanent income. If someone is young and graduated from an Ivy League school but doesn't have any savings, this person may still be a "safe bet" to loan money to. At the end of the article, an example is given of a woman who majored in Computer Science at Barnard and why she borrowed $850 from one of these firms to pay for a mattress rather than exceeding her credit card limits and paying 17% on the balance. This is what I meant by the blog post's title.
BUT, this new set of firms should be aware that by basing on the whole business on the belief that past correlations are predictive of future correlations, they are subject to the "LUCAS CRITIQUE".
Intuitively, these firms will make $ if their data crunching allows them to identify low risk targets and make loans to these individuals. Or if they charge higher interest rates to those they identify to be riskier. If the low risk potential borrowers turn out to be high risk borrowers , then these firms will go broke.
The Lucas Critique focuses on times when there has been a large shock to the macro economy. Dynamic optimization theory predicts that consumers (and hence borrowers) will change their consumption and savings patterns because of the shock. If these firms do not update their models to incorporate these behavioral changes then these lenders are at risk of losing money because the macro shock has changed the "rules of the game".
This suggests that a potential weakness for these lenders is that they are engaged in naive reduced form prediction models of default when they should probably be hiring structural econometricians as consultants to help them make better "out of sample" predictions.
To see precisely what I mean, read paragraph 2 of this technical paper and keep reading!