Banking

Moving Data Analytics Up The Credit Union Priority Chain

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Most of the insight into credit union spending priorities featured in the PYMNTS/PSCU Credit Union Playbook earlier this year is probably much in line with expectations. Anti-money laundering, data security, mobile/digital payments, fraud-fighting and payment tech rounded out the top five — with each ranking as a lead priority for over 50 percent of credit union executives. Given the average credit union’s focus on member experience, a big focus on security and smooth transactions as innovative priorities is no surprise.

What is surprising, however, is how relatively anemic a showing data analytics makes in terms of executive enthusiasm, with just over a third (35.7 percent) of credit union executives naming it as an innovative top priority. It was a result that PYMNTS’ Karen Webster and PSCU SVP of Data and Analytics Jeff Carelli agreed was quite puzzling on first glance, given the near-universal recognition of the importance of data analytics tools.

 

But Carelli said the question’s binary structure is generating a somewhat misleading answer. Financial institutions, he noted, don’t pursue things like data analytics as an end in themselves — it is a tool that is almost always going to be used in service of a broader objective.

And in some of those objectives — particularly in fraud detection and prevention — those uses are happening, iterating and thriving.

But the bigger-picture problem that stat points to is in the broadness of application — and the reality that in many credit union organizations tools are siloed into particular use cases like security instead of being leveraged more broadly across the organization. And that is a limitation in thinking, Carelli says, that can be costly — and needs to be rectified going forward if credit unions want to maintain a competitive presence in consumers’ lives.

“If credit unions don’t have a focus on data and analytics, all they can do is follow the moves of their competitors and the industry at large,” he said. “They aren’t seeing the insights into what they are doing and why — and that means they can’t get ahead of them to offer customers the things they want, or the things they don’t even know they need yet.”

Where Analytics Are Working

That fraud and compliance are leading concerns for the credit unions PSCU works with, Carelli said, is not that surprising. This is particularly true for the smaller ones, both because they are the most vulnerable and because building the fraud-detection tools necessary to take on sophisticated fraudsters often constitutes a more expensive and technology-advanced undertaking than they can hazard on their own. They are also the most likely to actually suffer catastrophic losses beyond their ability to recover in the event of a massive and successful attack.

That is why it is critical that those credit unions work with a service organization like PSCU to provide access to a set of security tools they otherwise couldn’t pursue on their own.

And those tools, particularly in PSCU’s case, draw often on the analytics infrastructure within fraud departments.

“The analytics infrastructure we build,” he said, is designed “by our fraud team to build rules, check in on the linked analysis across channels about merchant types and card types, to help fend off fraud. And a major issue is how quickly you can react in terms of writing rules and using machine learning and predictive analysis to modify rules as close to real time as possible as you are seeing trends that human eyes on their own could not spot.”

Fraud fighting and, increasingly, compliance areas like anti-money laundering (AML) and know your customer (KYC) regulations, are increasingly connecting into analytics frameworks wired to look for patterns across data holistically. That — broadly from a security and risk perspective — means organizations can get a much better view in context of when operations are normal, and when something is happening that should not be happening.

And, Carelli noted, in the world of security and risk, analytics are absolutely the supporting core missing because there is a wide understanding that data and analytics are really a foundation that underlies all of it.

The educational and operational challenge going forward is helping credit unions build that foundation under more of their organizations.

The Work Left To Do 

Credit unions have a lot to focus on at any given moment — from keeping members happy and protecting security to innovating payments forms and designing digital experiences — and all of it is critical to keeping up with the pace of a rapidly innovating market. The trick in presenting data analytics to departments, Carelli said, is not to force anything on them, or drag them into something they don’t want to do. It is to help them see this isn’t a new thing to do — it is a new way to do what they already have to do.

“This doesn’t have to be one more thing to think about and wrap their heads around or one more thing to worry about,” he said. “It is a foundation that can help you achieve all your other goals and make it easier to do so.”

That foundation, he said, can strengthen all kinds of areas. There are the places everyone’s minds jump to, he noted, like marketing or building consumer products and offerings. There are also the less flashy areas like back-office automation that frees humans from labor-intensive tasks.

And, he pointed out, the solutions also will tailor to the needs of the actual credit unions — and their actual members. What data analytics does best, he said, is aggregate and distribute data so credit unions can get a clearer snapshot of who they serve, what those people do — and how their attempts to offer up solutions to those customers compares to other credit unions in their size range.

What they do with that snapshot, he said, will vary depending on what they see in it. But it is better to know, and to strategize from knowledge, as opposed to seeing what everyone else does and making a guess as to whether to follow along or not.

“What credit unions do first is meet the needs of their members — and go the extra mile to anticipate them and meet them before they ever say them,” he said. “For us the challenge is how to bring as much data to our members in as many channels as possible so they can actually do that.”

 

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