Let’s be honest—relying solely on FICO scores today is like judging someone’s cooking skills by whether they own a microwave. Sure, it gives you a data point, but you’re missing the whole picture. That ‘thin-file’ freelancer paying rent via Venmo for 5 years? They might be a better risk than the guy with a 720 score who’s maxed out three credit cards. The data exists now to see that, if we’re willing to look beyond the usual suspects.

Here’s where teams get starry-eyed—they want to throw social media scrapes, Fitbit data, even Netflix habits into the model. But in our stress tests, knowing someone pays their phone bill on time predicts repayment 8x better than their Instagram posts. Start with the boring stuff: bank transactions, rent payments, utility histories. Those are your bread and butter before getting fancy.

Remember March 2020? Banks using static models panicked and froze lending across the board. Meanwhile, the smart ones had AI sniffing out that the laid-off bartender picking up Amazon warehouse work was actually becoming more creditworthy. Models that adapt aren’t just nice-to-have—they’re business continuity plans.

The sweet spot? AI handles the pattern recognition, humans handle the exceptions. Like when the model flags a sudden spending drop—your team can check if it’s financial distress (bad) or someone just finished chemo (very good reason to extend terms). Machines find the needles, humans understand the haystack.

Most banks make the same mistake—they either go all-in on AI overnight (chaos ensues) or run endless pilots that never graduate. The sweet spot? Pick one lending product (e.g., personal loans under $10k), prove the model works there, then expand. I’ve seen teams get stuck for years debating ‘perfect’ when ‘better now’ would’ve already helped thousands of good borrowers.