In the last five years, one of the new disciplines in banking that has emerged is the combination of machine learning and satellite imagery to gather bank intelligence on fraud, credit, and bank marketing. While this sounds daunting, in this article, we will show you some relatively simple applications of both artificial intelligence (A.I.) and satellite (sat) imagery to improve bank performance. In the last several years, bankers have learned that they have access to inexpensive imagery that is often free.
Tag: Machine Learning
In the olden days, if you wanted to market deposits, the head of Retail would come to Marketing and say something like - “We need to raise deposits,” or “We have a new account opening platform that we need to market.” Marketing would then put together some ideas for a print or digital campaign; Retail would sign off on it, and then they would roll it out. Maybe it worked, perhaps it didn’t, but the reality is it likely was successful at some level, and everyone was happy. Unfortunately, this approach is quickly fading and highly inefficient.
We recently taught a banking class in bank technology and there was this pervasive undertone that artificial intelligence was the domain of large banks. Nothing could be farther from the truth. The state of machine learning is such that almost any banker with the curiosity to understand can leverage machine learning for a variety of tasks from marketing, to credit, to risk management.
Last week our underwriting team turned down a bunch of loans. Too many delinquencies, not enough time in business, debt service was too low, loan-to-value (LTV) was too high, and cash flow was not of sufficient quality - all good reasons, all normal conclusions. As we have been chronicling in our blog (HERE for example), we have been experimenting with artificial intelligence.
It was back in 2014 when researchers at DeepMind directed their nascent artificial intelligence application to the game Breakout. Instead of programming DeepMind on how to play the game, the researchers programmed DeepMind to learn about learning to play the game. That is a meta level that isn’t normally programmed but the result of that effort, combined with many others, was in the back of our mind when we turned some artificial intelligent tools towards customer data.