Three AI models turn a credit union's dormant data into smarter decisions
Smarter targeting, sharper loan decisions, and earlier warning signs on member risk, all built from data the credit union already had.
The Opportunity
A national credit union wanted to make better use of the data it already had. Marketing wasn't reaching the members most likely to respond, loan decisions leaned on just a few static factors, and there was no way to spot early signs of member financial distress.
It needed AI models that could turn existing data into better day to day decisions.
The Transformation
Three AI models were built on the credit union's own historical data: one to recommend share certificates, one to score loans, and one to flag member risk early.
Each one plugs directly into a decision the credit union already makes.
Smarter Loan Scoring
A loan scoring model learns from past loans to flag good and bad risk earlier. It replaces a score based on a few static factors with one built on real loan outcomes, giving the credit union a more consistent way to approve loans and spot risk sooner.
Share Certificate Targeting
A prediction model learns from 200K+ past share certificate records which members are likely to invest. Instead of marketing to everyone, the credit union focuses on the members most likely to say yes.
Early Warning on Member Risk
A cashflow model studies member transaction patterns to spot early signs of financial trouble and group members by behavior. It gives the credit union a heads up on who may need support, and who is growing.
Lower cost per approved loan
Less marketing spend needed for the same result
Reduction in delinquency to charge off progression
Increase in cross sell