Fueled by the large volumes of data available in today's digital world, machine learning has the power to completely transform the experience financial institutions deliver. Until recently, it was a tool available only to tech giants and international companies on the Fortune 500 list. But it is increasingly within the reach of financial institutions of all sizes, including community banks and credit unions. Brace yourself... you're about to see machine learning and artificial intelligence applications take the banking industry by storm.
Machine learning is an area of computer science that uses large-scale data analytics to create predictive and dynamic models. In essence, you feed the right data to computers with the right algorithms and they have the ability to learn — automatically, on their own. These algorithms iteratively improve over time. That’s the “learning” part of machine learning that allows computers to find hidden insights without being explicitly programmed where to look for them.
The exceptionally large data sets used in machine learning make it possible for computers to recognize both anomalies and patterns lurking within the information, yielding faster and more accurate data-driven decisions.
The process of crunching numbers is made more efficient, reliable and cost effective thanks to major advancements in machine learning. This up and coming technology is driving innovation in every sector — but particularly in banking — and it will only continue to grow in popularity.
Some familiar real-world examples of machine learning include online recommendations in the online retail industry (i.e. Amazon and Walmart), allowing retailers to offer customers personalized recommendations based on previous activity and purchases. Apple uses machine learning in their voice recognition system, Siri, to imitate human interactions. Facebook uses the technology to tag individuals in photos. Google Maps analyzes the speed of traffic on roadways with location data from smartphones. And nearly every major tech company that serves an ad online uses some sort of machine learning to determine which ads to show to whom. In the 2016 U.S. Presidential election, some ads were actually created using machine learning algorithms that figured out which buzzwords, colors, images and buttons got clicked the most.
This is why companies are pouring a huge amount of resources into the technology. Mountains of raw data is now being collected on a daily basis and processed using sophisticated and powerful data analytics tools and machine learning tools to tease out useful insights.
Bonus: The banking industry has access to sprawling archives of historical records and data, which is perfectly suited for artificial intelligence and machine learning applications.
In the banking industry, machine learning can play an integral role in many stages, from approving loans and assessing risks to managing assets.
