Sarah Tyler

The retail banking industry has entered an era where traditional approaches to risk management, customer onboarding, and fraud detection are being fundamentally reimagined. Over the past three years, I've witnessed firsthand how institutions struggle—and occasionally triumph—when deploying advanced AI capabilities across core banking functions. The gap between pilot projects and production-grade systems has taught our industry invaluable lessons about what actually works when integrating intelligent automation into processes that handle billions in assets and millions of customer relationships daily. What we've learned about Generative AI in Financial Services comes not from vendor presentations or conference keynotes, but from the messy reality of transforming loan origination workflows, reimagining AML investigations, and rebuilding credit scoring models while keeping the lights on. These lessons carry weight precisely because they emerged from actual deployments at institut...

When our mid-sized financial services firm embarked on its first major AI deployment in 2024, we believed our traditional risk frameworks would be sufficient. We had decades of experience managing operational, market, and credit risks. How different could algorithmic risk really be? Within six months, we learned the answer the hard way: profoundly different. A seemingly minor data drift in our credit scoring model led to a cascade of approvals for high-risk applicants, costing us millions before we even detected the problem. That wake-up call transformed how we approach technological innovation and ultimately led to a complete overhaul of our risk governance structure. The experience taught us that AI Risk Management cannot be an afterthought or a simple extension of existing controls. It requires dedicated frameworks, specialized expertise, and continuous vigilance. Over the past two years, through both painful missteps and hard-won victories, our organization has developed an AI ris...