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.
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.
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.
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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