BY DAVID ROSS
I will never forget the time I received an escalation call from a member whose card had been declined for fraud prevention while checking into a hotel in Paris. She was furious about what transpired, and her main argument was that we should have known it was really her using the card. Why? Because two months prior, she had purchased airline tickets on that same card. And the airline in question? It was not United, American or Delta – it was Air France. She exclaimed, “Is it really that unusual for someone to buy an Air France ticket and then two months later try to check into a hotel in Paris? You knew that it was me!” It was difficult to disagree with her, as it should have been easy to predict the airline and hotel transactions were related. That was her expectation as a member 10 years ago in 2008 when that conversation took place.
My team and I have seen member expectations continue to grow in the decade since. After years spent partnering with credit unions across the country, my team has found the best approach to knowing and serving members better is through the use of Predictive Analytics.
Predictive Analytics, as the name suggests, can help credit unions determine the probability that something is going to occur in the future. This is very different from traditional reporting, which details what has already transpired in the past, and is often referred to as Descriptive Analytics. To be clear, a credit union needs solid Descriptive Analytics as a foundation before progressing on to the predictive space. It is crucial to understand items such as how a portfolio has performed year over year, where members are transacting, how often a card is used, what interchange was received, how much interest income was generated, and what the total outstanding balance is. A credit union should regularly review reports and dashboards to evaluate KPIs, trending and other key metrics around the aforementioned items. If not available internally, a credit union’s card processor might have this information available. Digesting it is important not just to measure how well the portfolio has been managed, but also to identify potential areas of opportunity. For instance, a graph showing a steeper trend line for fraud losses versus peer groups could mean that a more conservative risk approach should be considered. But while valuable, such an analysis is not predictive in nature – it remains based on Descriptive Analytics.
Predictive Analytics employs a mathematical approach to forecast outcomes