One thing I'd add is that explainability becomes just as important as accuracy in fintech. Even if an AI model is highly accurate, teams still need to justify why a recommendation or decision was made for audits, compliance, and user trust.

We've seen this in a few fintech AI projects at IT Path Solutions as well. The biggest wins usually come from combining deterministic business rules with AI instead of letting the model make every decision on its own. AI handles pattern recognition, while critical financial actions still go through rule-based validation and human oversight.

That hybrid approach has been much more reliable than chasing fully autonomous AI workflows.