Lending: When Technology Makes the Call

From social media to the use of all caps, every individual action can affect credit worthiness

It’s easy to get bogged down in the endless debate over the mingling of banking technology. We all acknowledge that as new tools emerge for both institutions and their customers, the fundamental dynamic between the two sides will continue to evolve. Resisting the tide is to live in denial—things will change, and the better our industry can handle it, the better things will be for everyone.

Still, some issues have the potential to stand out and deserve greater consideration. Here’s one: given the mountains of data now available on each individual, let alone each transaction, the process of deciding whether or not to make a particular loan will inevitably become more automated. The banking technology more than the banker will actually make the call. And again, making a philosophical argument against such practices is to live in denial, not to mention a waste of time. But bowing to the inevitable doesn’t seem too smart either—we need to manage the transformation in such a way as to make it benefit all parties.

So what’s the best way to move forward?

As a recent article in The New York Times points out, the issue is taking on greater urgency than ever before because of the flood of new technologies designed for exactly this purpose. For now, the focus is on new and smaller players using complex algorithms to determine worthy recipients, but let’s not make the mistake of assuming that it will stay a niche market.

In fact, there’s enormous potential here. Estimates vary, but it’s clear that tens of millions of consumers have little or no credit history, and many of them fall outside the purview of traditional lending institutions. Some survive by borrowing from the shadow economy, loan sharks with dubious, unsavory or even predatory practices, and no one thinks that’s a good idea. Meanwhile, helping those without conventional credit scores get an education, start a business, make necessary purchases or just pay their bills could be hugely beneficial to the economy at large.

That’s why it can be refreshing to see startups use technology to make instant decisions about loans. However, their practices are so at odds with tradition that it’s almost surreal. Earnest, which bills its call center personnel as ‘happiness engineers,’ runs predictive analytics on each applicant to identify credit-worthiness.  The company’s merit system is based on looking forward rather than back.  Affirm, which makes no bones about the fact that it’s out to reinvent the financial services industry, asks for just a little information and makes an underwriting decision in less than two minutes.

On the flip side, banks and credit unions don’t survive in a vacuum—they’re answerable to shareholders and regulators, and they’re building on a foundation of centuries-old tradition. Implementing technologies such as those used by startups will mean revamping almost all of their operating practices.

So what exactly are these new players doing to identify worthy borrowers? The technology analyzes just about every kind of information, from buying habits and social media activities to the use of capital letters to fill out forms. And since the human factor in largely absent, there’s a real question whether regulations governing anti-discriminatory practices even apply.

To be sure, the technologies are running analytics based on algorithms developed by teams of developers and banking professionals, and they’re tweaked constantly as the practices evolve. We’ve still in the embryonic phase of this entire discipline, and it will obviously get better.

The question is how and when, and not just if, more traditional institutions adopt these tools and change their practices accordingly. The influence will potentially go both ways—larger and older firms will become more adventurous while the startups face more regulations and become more conservative. Those that get to the center fastest could really win big.

Written by Jack Dougal

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