commercial-banking-4-25-17.pngYears ago, I had the good fortune to work for a bank with pristine credit quality. This squeaky-clean portfolio was fiercely protected by Ed, one of those classic, old-school credit guys. Ed had minimal formal credit training, and the bank had no sophisticated modeling or algorithms for monitoring risk. Instead, we relied on Ed’s gut instincts.

Ed had a way of sniffing out bad deals, quickly spotting flaws that our analysts had missed after hours of work. He couldn’t always put his finger on why a deal was bad, but Ed had learned to trust himself when something felt “off.” We passed on a lot of deals based on those feelings, and our competitors gladly jumped on them. A lot of them ended up defaulting.

Obviously, Ed wasn’t some kind of Nostradamus of banking. Instead, he was spotting patterns and correlations, even if he was doing it subconsciously. He knew he’d seen similar situations before, and they had ended badly. Most banks used to be run this way. It was one of those approaches that worked well—until it didn’t.

When Ed’s Not Enough
Why? Because some banks didn’t have as good a version of Ed. And some banks outgrew their Ed, and got big enough that they couldn’t give the personal smell test to every deal. Much of the industry simply ran out of Eds who had cut their teeth in the bad times. A lot of banks were using an Ed who had never seen a true credit correction.

It also turns out that humans are actually pretty bad at spotting and acting on patterns; the lizard brain leads us astray far more often than we realize. It was true even for us; Ed kept our portfolio safe, but he did so at a huge opportunity cost. The growth we eked out was slow and painful, and being a stickler on quality meant we passed on a lot of profitable business.

The surprising thing isn’t that banks still handle credit risk this way; the surprise is how many other kinds of decisions use the same approach. Most banks have an Ed for credit, pricing, investments, security, and every other significant function they handle. And almost all of them are, when you get right down to it, flying by the seat of their pants.

Bankers have spent decades building ever more sophisticated tools for measuring, monitoring, and pricing risk, but eventually, in every meaningful transaction, a human makes the final decision. Like my old colleague, Ed, they base their choices on how many deals like this they have seen, and what the outcomes of those deals were.

These bankers are limited by two things. First, how many experiences do they have that fit the exact same criteria? Usually it numbers in the dozens or low hundreds, and it’s not enough to be statistically significant. Second, are they pulling off the Herculean task of avoiding all the cruel tricks our minds play on us? The lizard brain—that part of the brain that reacts based on instinct—is a powerful foe to overcome.

Artificial Intelligence’s Time Has Come
This shortcoming, in a nutshell, is why artificial intelligence (AI) and machine learning have become the latest craze in technology. Digital assistants like Siri, Cortana and Alexa are popping up in new places every day, and they are actually learning as we interact with them. Applications are performing automated tasks for us. Our photo software is learning to recognize family members, our calendars get automatically updated by things that land in our email, and heck, even our cars are learning to drive.

The proliferation of the cloud and the ever-falling costs of both data storage and computing power mean that now is a real thing that is commercially viable for all kinds of exciting applications. And that includes commercial banking.

Banking & AI = Peanut Butter & Jelly
In fact, we think banking might just be the perfect use case for AI. All those human decisions, influenced up to now by gut feel and scattered data, can be augmented by machines. AI can combine those disparate data sources and glean new insights that have been beyond the grasp of humans. Those insights can then be presented to humans with real context, so decisions are better, faster and more informed.

The result will be banks that are more profitable, have less risk, and can provide customized service to their customers exactly how they need it, when they need it most.


Dallas Wells