AI Forces Compliance Teams to Rethink Alert Strategy

Financial crime compliance programs are a game of alerts. Or at least they used to be in the pre-AI era, when compliance teams operated on a linear equation in which more transactions generated more alerts, and more alerts required more analysts.

But financial services is firmly in its post-AI era now. The emergence of agentic artificial intelligence systems capable of reasoning, investigating and executing workflows autonomously is creating what may be the most significant shift in compliance operations since the rise of the internet.

“Most firms think AI is an efficiency upgrade, they think they will run the same processes with fewer people. We think that framing is wrong,” Madhu Nadig, co-founder and CTO at Flagright, told PYMNTS.

Rather than helping organizations process larger alert queues, today’s agentic AI systems are beginning to reshape the very structure of financial compliance work itself.

“The more context a system of record plus a system of action can have, the more use cases we can serve and the deeper the use cases,” Nadig explained.

Agentic AI’s New Operating System for Financial Crime Compliance

The emergence of agentic AI arrives at a moment when regulators and enterprises are becoming more comfortable with AI adoption. When Flagright launched as what Nadig describes as the first AI-native solution in its category in 2023, the market was characterized by equal parts enthusiasm and skepticism. Concerns around hallucinations, explainability and auditability dominated conversations.

“In the pre-AI era, you would tune these rules and there’d be alerts generated, and you hire people based on the alert queue. Your work throughput was depending on how many people you had,” Nadig explained, emphasizing that agentic AI is beginning to break that paradigm by moving beyond simple “flag detection” and toward contextual reasoning.

That shift comes at a critical moment. Financial institutions face increasingly sophisticated fraud schemes, expanding regulatory obligations, and attack vectors amplified by AI itself. The result is a growing realization that compliance architectures, built around static rules and manual review, may no longer be sufficient.

“The firms that will win,” Nadig said, “will rethink both how they apply AI to compliance technology and how they build a team around it.”

See also: AI Moves Compliance From Detection to Investigation

And as financial institutions rearchitect their compliance stacks for the AI era of financial crime, Flagright on June 17 raised $12.5 million in a Series A funding round to help them with agentic systems that provide explainable AI uses cases across investigations, alert intelligence, rule optimization, decision support and more.

“We see the human role shifting from just processing alerts to more of an orchestrator, supervising the systems that do the processing and being there for escalations and more heavy-context work,” Nadig explained. “The alert itself will become less central because the AI system can reason across behavior, context and history.”

Still, he stressed that firms “cannot treat AI as this magical black box that will just go ahead and solve your problems.”

After all, regulated industries are demanding governance frameworks that can explain how decisions are made, demonstrate adherence to policies, and withstand regulatory scrutiny. For AI providers, that means building systems that amplify existing compliance programs rather than replacing them.

The End of Compliance by Alert Queue

Traditional compliance and financial crime systems were designed to capture decisions, not reasoning. As a result, organizations attempting to layer AI capabilities onto older platforms often encounter limitations around data management, explainability and contextual analysis.

“The core challenge of legacy compliance stacks is they’re built on rigid case structures and deterministic rules,” Nadig said.

The challenge of rigidity is that modern financial crime investigations rarely occur in isolation. Relevant information may reside in customer relationship management systems, payment platforms, third-party data providers, or internal operational tools. That reality is driving a new emphasis on interoperability and data connectivity.

“The more context a system of record plus action can have, the more use cases we can serve and the deeper the use cases,” Nadig said.

The goal for financial institutions and FinTech platforms defending against AI-powered financial crime today is not autonomous decision-making detached from governance, but scalable execution of established compliance practices. They are seeking platforms that encode standard operating procedures, risk policies, and institutional controls directly into AI workflows.

The emerging alternative is an AI-native architecture in which the system of record and the system of action converge.

“Here’s a system that you can upload your standard operating procedures to. You can train the model based on what you already do, what your risk policies already allow, and we constrain these models and agents to that specific context,” Nadig said.

As he sees it, the next generation of compliance organizations will not simply be smaller versions of today’s teams. They will be fundamentally different organizations, built around AI-native systems that change not only how work gets done, but what work humans are responsible for in the first place.

“We think of the platform as the AI-native operating system to run all these operations,” Nadig said. “The AI is actually part of the core infrastructure, not an addition to it.”

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