How Banks Can Utilize Machine Learning


Last week, Accenture Consulting published a presentation on how banks can utilize machine learning to draw quicker insights from their data. Machine learning is the specific application of artificial intelligence (AI)  in which computers can learn without being explicitly programmed to do so.

How Does it Work

Machine learning begins with an identified data set that will be used to “train” the computer. If the goal is for the computer to make a judgment based on data, then you would also need historical data that can be matched with correct answers. Using historical data as a training guide, the computer can now be programmed to go through real world data sets searching for similar patterns and making predictions. Here’s the interesting part: as the algorithm goes through more and more data sets, it improves and adjusts itself based on whether or not its predictions became true; it learns on its own.

There are two main kinds of machine learning: supervised and unsupervised. Supervised machine learning is when you know the output variable and are trying to predict future outputs. The example Accenture gave was credit default risk. Presumably, you could program an algorithm to go through historical data on individuals who have defaulted on debt and identify key metrics that could be a marker for default risk. The algorithm could then predict future cases of individuals defaulting and adjust itself based on whether or not those individuals actually defaulted.

Unsupervised machine learning is when the output variable is unknown and the goal is to categorize the data based on a pattern of distribution or structure. For example, you could program an algorithm to go through all your customers purchasing habits to look for patterns and then segment your customers for better product targeting.  This is what advertisers like Google and Facebook probably use when they create targeted ads.

What’s In It For Banks and How Can They Apply It

There are clearly great benefits that any company can gain from machine learning, but there is a huge potential for banks in particular because of the enormous amounts of data available. Banks can use organizational data to reduce operational costs, financial and market data to react quicker and with more agility in response to competitors, customer data to better engage their clients through targeted products, and transactional data to reduce fraud and other risks.

The Accenture presentation gave a few real Photo courtesy Wikimedia Commonsworld examples that banks can apply machine learning to. One example was to reduce credit card fraud. In order to identify credit card fraud, banks traditionally have to manually determine trends which indicate a customer’s card is being used fraudulently, for example, the card is being used in a foreign country. Using machine learning, the bank can instead program an algorithm to look at an individual’s spending habits to search for patterns and when an anomaly appears, the algorithm can flag it as potential fraud. If the fraud is confirmed or rejected, the algorithm learns from it and adjusts itself accordingly.  Another example given was the use of machine learning on trading floors.

Another example given was the use of machine learning on trading floors. Banks can program an algorithm to use historical financial data to automate trades. An algorithm can also use an individual client’s data to assess their changing investment needs and essentially act as a robo-adviser.

Clearly, there is a myriad of applications for machine learning in the banking industry. Given the recent global rise of fintech in general, banks that are quicker to adapt and utilize the most cutting edge technologies should have a distinct advantage over their competitors.

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