A playbook for banks, insurers, and fintechs looking to leverage AI safely and effectively
AI in financial services has moved past the hype, but implementation still stalls where it matters most: data quality, internal capabilities, and practical governance. To understand what’s working in the real world, I spoke with three leaders building the next generation of no-code and low-code AI tools: Christian Buckner, Head of Data and AI at. What emerged was a clear playbook for banks, insurers, and fintechs looking to leverage AI safely and effectively.
Devavrat Shah agreed. “We have a ton of data sources and workflow tools, but the AI tooling that connects them is still missing,” he said. “What we need are specialized, purpose-built models that live within the enterprise and understand the specific tasks they’re built to support.”Harvard Calls Out ‘Illegal Retaliatory Step’ As Trump Bars International Students’ EntryWe talked at length about trust, not in theory but in practice.
He emphasized that tools like KNIME let users build automated workflows without knowing how to code, while still requiring them to understand the logic behind each model. “Data literacy is key. You don’t need to know how a method is implemented, but you need to know what it does,” he said. These role-based agents are more efficient, cheaper to run, and far less risky. They can live inside a firm’s firewall, interact directly with structured internal data, and avoid the data leakage concerns associated with using external APIs.
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