Score the unscored!

June 17, 2013

I went to two conferences in the past couple of weeks: the Underbanked Financial Services Forum, in Miami, and the Clinton Global Initiative’s America conference, in Chicago. At the former, I was introduced to a company called Cognical, which pitched itself as a tool which will allow lenders to lend money to a much broader group of people than they currently accept.

Cognical was set up, at least in part, to address the Catch-22 built in to the US lending system: you can’t get credit if you don’t have a credit score, and you can’t get a credit score until you’ve been extended some credit. The result is a system where many borrowers, especially immigrants and the poor, find themselves forced to pay through the nose to loan sharks, pawn shops, payday lenders, installment lenders, and other institutions which range from the consumer-unfriendly to the downright predatory.

It’s hard to wade through the jargon on the Cognical website. Exempli gratia: “Leveraging our experience with machine learning algorithms and unstructured big data, we assess and transform application variables in specific ways to expand the data and its predictive value.” But the general idea is pretty simple: rather than just look at credit score, lenders can use Cognical to mine enormous quantities of data, in the hope and expectation that buried in there somewhere the firm will be able to find patterns which can predict whether or not any given individual is going to repay a loan.

Cognical claims spectacular results, but I’m not completely sold: with big data comes a massive rise in spurious correlations, and in any case it’s not like the borrowers in question are leaving a massive data trail behind them in the first place. On top of that, even if Cognical is doing amazing work, all of it will be for naught unless and until someone happens to apply for a loan from one of Cognical’s clients.

As a result, what you really want, it seems to me, is two things. First is a dataset which is obviously germane in terms of throwing light on an individual’s ability to pay her obligations in a timely manner. and second is a way to get that dataset into the hands of the institutions which really matter, when it comes to this particular game: the three big credit bureaus, as well as FICO.

Wonderfully, that is exactly what a bunch of us, including Sasha Orloff of LendUp, ended up talking about at the CGI conference the following week. At the moment, it’s only an idea, but I’m keeping my fingers crossed that the idea is so good, and so obvious, that it’s going to end up being put into effect at some point.

What’s the dataset? It’s not a bunch of Facebook likes and Twitter favorites, the kind of inchoate data only Max Levchin could love. Rather, it’s a big and simple obligation: rent payments. It’s pretty obvious that if you’re good at making your rent payments on time every month, you’re also more likely to pay other obligations in a timely manner. And so if rent payments were reported to the ratings agencies, that would give them valuable information about the group currently known as “thin files” — people about whom there’s too little data to make a determination as to creditworthiness.

The place to start is HUD, along with the enormous housing agencies in cities like New York and Chicago. As public agencies, they have every reason to want to improve the lives of the people they’re renting apartments to — and one easy way of doing that would be by improving those people’s access to affordable credit. (And there would be a financial benefit for the agencies, too: if tenants knew that their rent payments would be a part of their credit score, they might be more inclined to pay on time.)

The ratings bureaus are weirdly low-tech: they don’t exactly have convenient APIs which allow anybody to upload payment datasets. Instead, they use a clunky old thing called Metro 2, which was designed back when people were mainly worried about Y2K issues. Still, it’s possible to write a program which converts any structured data into Metro 2 and then uploads it to the ratings bureaus — Orloff has done it, and he says he would be happy to donate the code to any open service or housing agency which wanted to report rental information.

Of course, once the rental information was uploaded to Experian and Equifax and TransUnion, they would have to actually do something with it — as would FICO. That might take a while. But Experian says that it’s already incorporating rental data into its own scores, and certainly landlords would be very interested in a FICO score which included such data. The demand is out there, and if the information is just dropped into the companies’ laps, it would be hard for them to simply ignore it.

The goal here is, simply, to score the unscored. Such people might not have high FICO scores, at the beginning, especially if their history of rental payments is spotty. But score beats no score, and once you have one, you can start working to improve it. What’s more, people who have been diligently paying rent on time for years, and who have sensibly be avoiding debt, might actually end up with high FICO scores rather than none at all.

It doesn’t need to end with public housing agencies, of course. The government could set up a central system where all landlords could, if they wanted to, upload the rental information of their tenants. And once such a system was set up, it could conceivably be extended beyond rent payments, to include things like utility bills as well. The idea being that when a company like FICO attempts to ascertain creditworthiness, all information has to have some value. And the more information that can be obtained about people who haven’t formally borrowed much money in the past, the more likely it is that they’ll be able to get a score. Which is a very useful thing indeed, in today’s economy.


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