Computer Models as Autocrats
In recent times, though, a new form of centralized control has taken root–one that is the work not of old-fashioned autocrats, committees, or rule books but of statistical models and algorithms. These mechanistic decision-making technologies have value under certain circumstances, but when misused or overused they can be every bit as dysfunctional as a Muscovite politburo. Consider what has just happened in the financial sector: A host of lending officers used to make boots-on-the-ground, case-by-case examinations of borrowers’ creditworthiness. Unfortunately, those individuals were replaced by a small number of very similar statistical models created by financial wizards and disseminated by Wall Street firms, rating agencies, and government-sponsored mortgage lenders. This centralization and robotization of credit flourished as banks were freed from many regulatory limits on their activities and regulators embraced top-down, mechanistic capital requirements. The result was an epic financial crisis and the near-collapse of the global economy. Finance suffered from a judgment deficit, and all of us are paying the price.
This is from an article based on a forthcoming book. As I understand it, the thesis is that substituting computer models for human judgment reduces diversity and thus has some of the same costs as substituting central planning for decentralized trial and error.
In my view, it is romantic to think that mortgage lending was decentralized before there was credit scoring. Mortgage lending standards could not be diverse as long as Freddie and Fannie were such dominant players. At Freddie Mac, we were never going to knowingly take the credit risk on a mortgage that was not underwritten to our standards. (We might put a loan that did not comply with our standards into our securities, but only if the loan originator agreed to take the losses if the loan defaulted.) So the question was never one of decentralized judgment vs. centralized standards. It was a question of centralized standards articulated as underwriting guidelines or centralized standards articulated as credit score limits.
Of course, loan originators were always trying to push the envelope on the standards, and that produced some diversity that was not intended on the part of Freddie and Fannie. Trying to get rid of documentation requirements was one envelope-pushing tactic, which Freddie and Fannie shut down in 1991.
Then, under different leadership a dozen years later, Freddie and Fannie opened the door to low-docs. Thus, the advent of NINJA loans–No Income, no Job, no Assets, which did not mean that borrowers were not required to have such attributes. It only meant getting rid of the requirement that borrowers produce copies of tax returns, letters from employers, or bank statements to allow lenders to verify the income, job, and assets being reported on the loan application. In 1991, we thought that eliminating documentation requirements was an invitation to fraud. A dozen years later, the new geniuses at Freddie and Fannie looked at it as a simplification of the lending process. That point of view only cost taxpayers a couple hundred billion dollars and counting.
If the credit scoring approach had a weakness, it was that it led to overconfidence, perhaps leading the mortgage industry to believe that they could get away with NINJA loans. But to the extent that the mortgage market suffered from a monoculture, that reflected the market structure in which two firms dominated. In fact, that dominance was challenged by the growth of subprime lending, which initially took place outside of Freddie and Fannie, who managed to recapture their overwhelming market share at exactly the wrong time in 2006 and 2007.
Bhide’s thesis that computer models create a monoculture is interesting, and I will want to read the book, but I am skeptical about the application to mortgage lending. Keep in mind that I suffered through a lot of corporate soap opera twenty years ago arguing for credit scoring as a better tool than rules of thumb, and so I may not be unbiased.