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Machine learning (Image: Shutterstock).

For many lending executives, machine learning is top of mind these days. ML has the potential to build supercharged credit models that can result in higher lending volumes and lower default rates by using high-level math to make billions of calculations, finding unseen correlations among hundreds of disparate pieces of borrower information.

It’s no surprise, then, that a Fannie Mae survey of 184 lenders last year found almost 60% expect to be using ML by the end of next year. The survey found most credit unions are taking tentative steps toward ML, with their efforts about equally divided among three areas: Rolling out ML on a full or trial basis, actively planning ML usage or taking a wait-and-see approach over the next year. In my over a decade of experience deploying AI and ML in finance, the biggest delay credit unions will face implementing ML isn’t technological – the tech is simpler than people think. The real challenge is getting buy-in from the people in your institution who can make or break the project.

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