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Credit Unions Are Way Behind The Curve On Data Analytics: Report

How far behind are credit unions when it comes to data analytics and digital transformation?

According to a recent study from Best Innovation Group and OnApproach, 45 percent of credit unions don’t currently have a strategy in place, and those that do have a strategy still say it will take three to five years to implement.

“The competition is already three to five years in front of most CUs,” declared Kirk Kordeleski, senior managing partner at BIG. “FIs, including the largest CUs in the U.S., are investing heavily in data strategies and analytics and are now seeing increased membership growth, loan originations and digital brand/marketing awareness.”

And, Kordeleski added, those CUs that aren’t making the most of data analytics could be in even bigger trouble if the economy goes south.

“As we go forward there will be a significant performance difference between those that have invested and those that have not,” he continued. “We think any downturn in the economy will highlight the advantage that data-oriented FIs will have over their competitors.”

Approximately 85 credit unions participated in the survey, titled “National Survey Findings: Credit Union Data Analytics and Decisioning Trends.” Questions focused on topics ranging from strategies and timelines to budgets and staffing.

“The majority of CUs don’t have a clear understanding of what digital transformation means and why it is important it is to their future,” explained OnApproach CEO Paul Ablack. “I firmly believe that digital transformation should be the number one strategic priority for credit union leadership.”

The survey revealed that 70 percent of respondents had invested in analytics tools compared to the previous year. CUs allocated budgets most often toward staffing, data warehouses, analytics tools, and dashboard and reporting tools.

The OnApproach/BIG study is believed to be the first nationwide look at data analytics trends within the U.S. credit union movement.


Just over half of the credit unions who responded to the survey revealed they have budgets in place for data analytics, and 45 percent of those CUs plan to spend less than $100,000 on such tools this year. Roughly one-third of respondents will spend more than $200,000, while 15 percent will spend more than $500,000.



“We do not have a benchmark in place today that suggests what percentage of the operating or tech budget should be allocated to data strategies,” said Kordeleski. “We do believe that data analytics should be a high priority and should get the funding it needs in order to be fully operational within 18 months.”

Ablack added that depending on how fast a credit union wants “to move,” a CU with $500 million in assets should budget between $150,000 and $300,000 per year for three years to cover software/hardware, analytic applications and strategy.

“For smaller credit unions, these numbers can be significantly less by using analytics as a CUSO-provided service,” he said.

Devising a strategy

For many credit unions, integrating disparate or legacy systems can potentially impede the growth of a digital transformation strategy. Kordeleski said “how” a CU solves disparate systems and related data analytic issues are “crucial to expeditiously and accurately installing” a data analytics strategy.

“Data analytic strategies are about using all sources of data to get a full picture of member’s business whether it is internal or external to the CU,” he said. “BIG’s view is that an assessment of the strategy, data, and current tool set, culture and business goals is the starting point for a data strategy, integration and implementation plan.”

In Ablack’s estimation, the “holy grail” for a credit union is the ability to have “a single source of truth” for all of its data. To this end, the data has to be streamlined and easily extractable, including transaction level data from each system, such as core, auto and credit card. The data next has to be normalized to a “common industry definition,” which enables analytics as a platform.

To help a credit union begin devising a strategy, Ablack offered a three-step approach:

  1. Create an API to the single source of truth allowing CU managers and analytic application providers (e.g., CECL, Portfolio Analysis) to build and share applications across the user community.
  2. Provide access to an industry “data lake” that allows credit unions to securely pool data with other CUs and other third party data providers (e.g., Lexis, Building Permit, Insurance).
  3. Ensure the industry data lake is open to any analyst or data scientist that works on behalf of the industry to create powerful predictive and machine learning models.

“Credit unions export way too much data out to vendors that provide analytics services, which means that parties outside of the credit union have the potential to have more insights than they credit unions do as they amass large troves of data,” said Ablack. “The analytic vendors have an important role in the industry, but there is a way that they can deliver their value without taking ownership of the CU’s data.”

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