Financial institutions racing to adopt artificial intelligence risk replicating other organisations’ mistakes rather than solving their own problems. That was one of the central warnings to emerge from a recent webinar hosted by RegTech firm Hawk in partnership with ACAMS.
The panel brought together senior voices from compliance, risk advisory, and financial crime prevention, including ING global head of financial crime compliance for investment banking Adrianna Fabijanska, Wintrust Financial Corporation VP of compliance technology product management Michael Morrison, and Grant Thornton (US) partner in risk advisory services Kyle Daddio, moderated by Hawk senior product marketing manager Erica Brackman.
A recurring theme throughout the discussion was that AI without a clearly defined purpose is destined to underperform. Michael Morrison emphasized, “Good AI isn’t just accurate, it’s operationally embedded and defensible. This starts at the point of selecting the right AI model by establishing what problems you’re trying to solve with it.”
Fabijanska agreed, highlighting the critical role of data quality. She warned that poor data governance leads directly to poor AI outcomes, urging organisations to invest in structuring their data and understanding its lineage before deployment—rather than after encountering a wave of unexplained false positives.
Grant Thornton’s Kyle Daddio cautioned against a growing “copycat” mentality in the industry, where firms rush to replicate a competitor’s AI implementation without assessing its suitability for their own risk profile. He remarked, “What really ends up happening is you’re doing what was good for somebody else, not what’s good for your organization.”
Daddio advised firms to establish long-term goals, involve the board early, and resist the pressure to adopt AI reactively. Erica Brackman added that vendor selection is crucial, noting that while every provider claims to reduce false positives, the real value lies in whether the solution is tailored to an organisation’s specific risks and systems.
Rather than viewing governance as a hindrance to innovation, the panel argued it is essential to sustaining AI programmes. Morrison outlined the core components of a defensible framework: a clear purpose statement, documented data lineage, defined performance metrics, and rigorous change management tracking to capture model updates over time.
Fabijanska highlighted a specific organisational risk that is often overlooked. She stated, “Just as much as the person who designed the model knows how it works, if an analyst can’t explain why they’re making the decision they are—or if an examiner comes and asks a question and there’s only one person who can answer it—the AI you’ve designed is flawed.” She insisted that building broad internal literacy across teams is what truly prepares an organisation to meet regulatory expectations.
Morrison suggested that firms start with narrow, lower-complexity use cases to surface problems early and build credibility with auditors before expanding their efforts. This cautious, incremental approach, he argued, is more likely to earn regulatory confidence than diving straight into large-scale implementation.
Hawk concluded that implementing these principles necessitates technology capable of automating the heavy lifting—from documentation and change tracking to explainable alert outputs—allowing compliance teams to manage the model lifecycle without relying solely on data science expertise.
For more insights from the discussion, read the full report here. As the financial sector continues its embrace of AI, the lessons learned from this panel may prove invaluable in shaping a more thoughtful and effective approach to technology in finance.
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