GREENVILLE, S.C.–Every credit union has data. But the key in making the most effective use of data lies in understanding which data tells the right story and in having the right people asking the right questions, according to one person.
Tony Hildesheim, COO with Santa Rosa, Calif.-based Redwood Credit Union, told NAFCU’s Strategic Growth Conference here that in a lot of ways credit unions are way behind the curve when it comes to supporting members using data. This whole idea about right data, wrong data, it’s just data. What you do have is getting to the most useful tata, the data that tells a story.”
Getting to the core of the story involves the steps outlined below, he said.
“Predictive analysis is where AI starts playing really well,” Hildesheim stated. “At the end of the day all AI is is statistical analysis on steroids. It’s ‘Here is the likelihood of X, Y, and Z happening.’ The more data you feed it the better that analysis is. When coupled with machine learning…it helps to create the solution.”
When it comes to creating that right solution, Hildesheim said there are two key questions to be asking:
- Are we asking the right questions about the data. “‘Why’ is the first question, and you have to ask that over and over again.”
- Are we being honest with ourselves?
‘Weird Anomalies’
For example, Redwood Credit Union had what he called a number of “weird anomalies” in its data related to member growth. The credit union has long had steady member growth, but then the trend line showed a dip. The reason? Some of the credit union’s IT folks had purged dormant accounts by implementing a solution that escheats for all the states in which RCU has members residing, which is all 50. It had previously only performed escheatment for California.
“Trendlines are valuable because they help you spot an anomaly and lead to why questions. Often, we don’t ask the why questions,” he said.
Similarly, the data show Redwood Credit Union wasn’t lowing efficiencies in account opening, even though it had invested in an easy to use online account opening process. It is still exploring the why behind that trendline.
Changes in policies and procedures can also show up in different ways in the data, he noted.
Not a Risk
Redwood CU, for example changed its policies so that whether a check was deposited via an ATM, mobile app or branch, the holds and limits were the same across all channels.
“A lot of credit unions are afraid to do that. They feel the mobile deposit is a riskier transaction, but there are ways to deal with that,” he said. “You can use tech to identify risk from fraud. Our data and risk analysis show there is no greater risk in mobile deposits. What does that do for the branch? It allows them to focus on opening accounts, where it is a lot easier to help members with questions and to cross-sell members.”
Another data point that requires why questions be asked, said Hildesheim, involves members who use digital channels. He explained Redwood has been working very hard to get as many members using digital as possible, and now more than 80% of the membership have either online, mobile or both types of accounts.
“But the bigger question is how many are active,” said Hildesheim. “It’s about 81% of those. The inactive accounts have started to trail off.”
Where the question becomes “interesting,” said Hildesheim, is driving down deeper into the difference between mobile and online. Mobile has started to outpace than online.
“More checks are being deposited in mobile channel than all other channels combined,” Hildesheim told the meeting. “That’s a huge shift in member behavior that was done purposely. Our mobile only users have climbed and at the end of the year skyrocketed due to three new systems.”
One of those was a new bill pay solution.
Not Just About the Data
“Making something happen is not just about data. It’s about understanding what happened and why,” he said. “Why are people behaving the way they are behaving?”
Hildesheim said one of the challenges in predictive analysis is that everyone makes assumptions. It’s a problem exacerbated when the wrong people are providing the assumptions, he added.
Data can also be drawn from outside the credit union, according to Hildesheim, who cited spiking gas prices as a data point that can drive decision-making.
“Why not do an (electric vehicle)-discounted loan? We are seeing a surge of EVs being produced--although not delivered, by the way. Why not get in front of the trends? The interest is there.”
Those why questions must also extend beyond the credit union’s point of view, as well, he added, explaining the credit union may need to get some mortgages on its books, but members aren’t looking for mortgages.
“That’s not what they need. They need a house. A mortgage is a way of getting to that house in which to raise their kids,” he said. “What if you could instead use data to identify people who are more likely to need a home today?
“We have a lot of information. But at the end of the day our job is to step back, make sense of it and ask a ton of why questions,” Hildesheim said. “We need to ask the questions that matter to understand what is really happening.”
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