GenAI in banking: what fraud teams and IAM leaders need now

TL;DR: 96% of global banks are implementing GenAI, according to Feedzai, while Deloitte forecasts GenAI-related crime could reach $40 billion by 2027 as criminals adopt synthetic content, scam automation and agentic workflows. The governance challenge is no longer whether GenAI works, but whether data, explainability and human review can keep pace with machine-amplified fraud.

Editorial analysis by NHI Mgmt Group, based on content published by Feedzai: “What Is GenAI? How Generative AI Is Changing Everything”.

By the numbers:

• 96% of global banks are already implementing GenAI.
• GenAI-related crime could reach $40 billion by 2027, according to Deloitte forecasts cited by Feedzai.

Key questions

Q: What breaks when GenAI outputs are treated as trusted evidence in fraud operations?

A: When GenAI outputs are treated as trusted evidence, hallucinations, biased summaries and overconfident recommendations can steer investigators toward the wrong conclusions.

Q: Why do endpoint agentic AI tools create more governance risk than chat-only GenAI?

A: Endpoint agentic AI can act inside a user’s session, move data, and trigger downstream actions, which expands the effective privilege boundary.

Q: What are the signs that AI fraud controls are failing?

A: Weak controls usually show up as rising scam volume, more difficult-to-spot fraudulent content, higher account takeover rates, and customers abandoning the brand after suspicious interactions.

Practitioner guidance

• Define governed use cases for GenAI in fraud operations Limit GenAI to use cases with clear review points such as case summarisation, triage support and synthetic scenario generation, then document who approves the output before it affects customer or transaction decisions.
• Set authority boundaries for agentic workflows Specify which systems, records and decisions an AI agent can access, and remove any standing access that allows it to act beyond the intended workflow scope.
• Validate synthetic data against real fraud patterns Compare synthetic examples with known scam and fraud typologies before using them for model tuning, control testing or investigator training.

Bottom line: GenAI in banking is now a fraud and governance issue as much as a productivity issue, because the same capabilities that help investigators can also help attackers scale deception.

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