The search for the ‘fat middle’
Kemba’s automated indirect decisioning engine reduces the work load of human underwriters while improving the member experience.
At Kemba Credit Union ($871.1M, West Chester, OH) necessity is the mother of invention.
When the Ohio credit union’s one underwriter for indirect auto loan applications left in 2015, the organization’s leaders saw the opportunity for Kemba to adopt a more robust automated decisioning strategy, says Mark Seymour, vice president of lending and risk.
At that time, Kemba’s lending department was running on two separate LOS systems: indirect on one, consumer on the other. The consumer system featured some automated decisioning ability, the indirect system did not .
“It made no sense,” Seymour says. Borrowers today can go online for a quick loan decision and funding, so Kemba started using its consumer LOS for indirect lending in the hope that automation would improve the member experience as well as control overhead expenses.
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