Use of AI in Merchant Underwriting

The payments industry has never stood still.  And nowhere is that more evident than in how we underwrite merchants. For decades, underwriting relied on static snapshots: a credit report, a business profile, a few bank statements. But static risk models are no match for today’s fast-moving commerce and evolving fraud tactics.

Enter AI-powered underwriting. Machine learning models can now assess risk in real time, drawing on dynamic data.  Everything from transaction behavior to online footprint and even subtle indicators of legitimacy or deception. It’s not just about onboarding anymore. Continuous monitoring is becoming table stakes to spot emerging risks before they turn into losses.

Yet as we embrace AI, we also need to grapple with important questions: How do we ensure these models remain explainable? Are we confident they won’t inadvertently reinforce bias or exclude worthy merchants?  Are there trends we ought to be looking for, before fraud occurs?

At the intersection of innovation and accountability lies the next frontier in underwriting. The firms that succeed will combine AI’s predictive power with human judgment and transparent governance.

If you’re exploring how to evolve your underwriting programming just curious where this is all heading, I’d love to connect and compare notes. The future is already here; it’s time to get ready.