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Demystifying Machine Learning for Banking

A report parsing machine learning for banking

People have been saying it for a while. “AI has arrived!” Well, AI turned 67 this year. It’s not new. And yet, we are now witnessing a special moment. AI is graduatng from the laboratory to the wide market.

Before, AI belonged to the halls of academic research and the R&D bunkers of multbillion-dollar corporatons. Now, AI is afordable and atainable, signaling a radically transformed landscape that will change the way we live, work, and pay. Today’s arrival of AI adopton marks the birth of something that’s been gestatng for decades.

But what makes this moment so special? In a word: convergence. Certain distnct technological strands have been in development over the last 15 years, and at this moment, they are coming together in a perfect storm.

First came the era of mobile, which placed powerful computers and sensors in our pockets as we walked through our days. Next came big data, because the multplicaton of computers and sensors meant mountain afer mountain of informaton. Sometme around 2010, our world produced informaton surpassing one zetabyte. To picture the “big” in “big data,” think of it this way: If your cup of cofee could hold a gigabyte, you’d need The Great Wall of China to store a zetabyte. And by 2020, that number will increase 44 tmes over, from one zetabyte of data produced globally to 44 zetabytes.

This advent of big data happened to coincide with the advent of beter computng, beter algorithms, and new AIfocused organizatons, allowing us to take this data and turn it into something even beter: meaning.

This brings us to machine learning. Later in this guide, we’ll look at how machine learning works and how it can work for you. But frst, let’s take a closer look at all the ingredients that got us here

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