Over the past five years I have attended dozens of presentation on data analysis. The call to action from those presentations have been remarkably similar: “You should be performing analytics.”
That call to action falls short, for obvious reasons, when directed toward people already performing analytics. But it also falls short when directed at people who have yet to dive into their data.
Stepping into the shoes of a credit union executive or employee who isn’t currently analyzing data, I would speculate the reason for the lack of action is simple: They don’t know where to start.
Do we look at loan data? Do we look at member data? Do we look at transactional data? Should we be looking at data points indicative of higher risk, greater profitability or growth opportunities?
If you pose those questions to someone waist-deep in data, they would tell you “yes” to all. However, such an approach presents a daunting task for a credit union dipping its toes into the sea of available data for the first time.
A better response might be “It depends. What do you want to learn?”
If you knew everything about your credit union, members, and loans, then performing analytics would be a waste of time.
If not, I would suggest taking a step back and thinking about what questions you have that can be answered using data and how those answers can benefit your credit union.
With the improving economy and collateral markets, common themes throughout the industry have used data to:
• Increase yield by offering products, specifically secured products, to higher-risk borrowers, and;
• Identify growth opportunities, including refinancing auto loans held at other institutions and identifying qualified credit cardholders for credit line increases.
Before starting any program geared toward taking on additional risk, you should evaluate your loan portfolio, looking at FICO scores and collateral values to answer the question, “What does our risk concentration look like?”
After you have quantified your ability to take on risk, you can answer the question, “Do our higher-risk secured loan portfolio segments perform more profitably, net of charge-offs, than our lower-risk segments?” by evaluating originating FICO scores, interest rates, and historical charge offs.
Answering the question, “Do we have members that could benefit from refinancing an auto loan with a different institution?” would involve identifying members who have recently taken out high-rate auto loans with other institutions.
Whether working with a third party or performing your analytics in house, having your goals in sight will give you the opportunity to focus on the data and data points that matter.
This will and allow you to streamline your data compilation, reduce re-work, and enter the world of analytics as painlessly as possible.