Sink or Swim with Data Analysis

CUs can solve problems and create new opportunities with member data.

May 06, 2014
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Credit unions are "swimming in data" that can solve problems, create new opportunties, and improve the bottom line, consultant Karlo Rodriguez told the CUNA Payments Roundtable audience Monday in Las Vegas.
Rodriguez said credit unions' core processing systems contain usable information such as:
  • Historical transaction records;
  • Loan histories;
  • FICO scores;
  • Credit and debit card usage; and
  • Insight on member insurance and investments.
Most financial institutions, however, fail to use the data to identify goals or address problems, Rodriguez said.
"Every credit union has a problem to solve," Rodriguez said. "You might need to make more loans, or you might have a problem with member retention. All of these issues are tied to member data."

Credit unions can use member data to solve problems related to:
  • Risk management;
  • Transactional behavior;
  • Delinquency;
  • Financial product design;
  • Member retention; and
  • Member segmentation.
For example, a credit union with a low penetration of checking accounts can identify characteristics of active account holders and market to similar members.

Or, a credit union seeking to improve member retention can identify signals that point to members most likely to close accounts within a year and reach out to them with more enticing product offers.

Transactional data, Rodriguez said, is a good place for credit unions to start with data alaysis.

"Remember, you have your members' financial history," he said. "It is that data that so many retailers would love to have. Use it to your advantage."

Transactional Data vs. Relationship Data

Mike Bartoo
May 07, 2014 10:45 am
I would respectfully disagree that transactional data is a good place to start. In my opinion, relationship data is a much better starting point. Transactional data tends to require more "mining" of thousands/millions of transactions to identify opportunities or threats. Relationship data, however, involves identifying and profiling your high-value relationships (those profitable relationships with multiple products/services, for example) and leveraging that information to attract/cross-sell similar members. Generally involves a bit less effort and quite a bit higher return.

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