AI adoption in Financial Services is wide but shallow: Finance is one of the most data rich industries out there—as such, it's one of the most data and AI mature. While AI has seen great success across fraud, risk, and customer experience—there's still a lot of depth to be achieved.
A great blocker to AI Adoption is trust: It's incredibly important to get ahead of the trust problem in AI in financial services. AI has incredible value to provide banks and customers—but minimizing harfmful impacts and providing transparency to AI-based decisions is key.
To scale trust, focus on education: It's paramount that everyone within financial organizations understands the value, and limitations of AI. This will empower humans in and out of the loop to put customers' best interest in mind.
It is not building trust which is a difficult task for financial services firms. It is the fact that they have a huge amount of existing trust level to defend, right? And losing that trust is both very easy and catastrophic to them. I mean, you could see how hard it could be when the previous financial crisis struck. If people lose trust in financial services, then that's the end, right? I mean, other than your doctors, your bank or insurer probably knows more about you than almost everyone else.
It's important to talk both about the core data related skills, but also about how to increase the data quotient of the rest of the organization. It's not enough to have a pool of data specialists or analytics specialists. You need the entire organization's talent, AI or data quotient to increase, otherwise you can't get the full value.