Predictive and prescriptive data analytics for credit unions – keys to the future growth

It was not long ago credit unions were new to the world of data analytics. Fast forward to today, and credit unions have understood the value of their member data – be it demographic, behavioral or transactional. More and more credit unions are making arrangements to capture this rich data, and many others have already mastered the science of data analytics and putting their data to test. The revelations made by the data are eye-opening for credit unions. They are learning new things about their membership.

With this wide acceptance of data analytics being in the credit union industry, what will distinguish the best performing credit unions from the others and let them get ahead of the competition? The answer would be their ability to race through the analytics maturity curve by adopting more sophisticated and advanced data science tools like predictive and prescriptive analytics. This technology can help credit unions uncover what the future may hold for them accurately and confidently.

Delving Into Past and Present Data is a Necessity

Let’s look closely at the type of data analytics currently used by most credit unions. We will find that they are concentrated in the domain of descriptive and diagnostic analytics, analyzing the past and tracking the present. This means they are successfully deciphering what happened and why it happened. In line with this growing curiosity of credit unions, using business intelligence and visualization tools to keep track of portfolio and member level metrics is gaining popularity. This is viewed as a standard practice at many credit unions, helping its product managers and C-level executives make strategic decisions. Let’s look at some examples closely:


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