AI adoption across financial crime and compliance functions is accelerating, moving rapidly from experimentation to operational necessity.
Hawk partnered with Chartis to survey 250 banks, payment firms and FinTechs to assess how far institutions have progressed on their AI maturity journey. The research highlights how AI has shifted from a future ambition to a foundational layer within anti-money laundering (AML), fraud prevention and broader compliance operations, with clear implications for investment, operating models and governance. Read the banking report here and the payments/FinTech report here.
For banks, AI is no longer viewed as an optional enhancement. Nearly nine in ten institutions now actively encourage AI adoption, and 70% are already using AI in some form within financial crime and compliance.
Adoption maturity varies, with close to half of banks still piloting AI tools, while 16% have operational deployments and 6% report AI embedded at a strategic, enterprise-wide level. Fraud prevention currently leads adoption, with 10% deploying AI at scale, followed by AML transaction monitoring, where pilots and operational use are quickly becoming the norm.
Investment patterns suggest this momentum will continue. More than 80% of banks plan to increase AI spending by over 25% in the next one to two years, signalling growing confidence in its value.
Cost savings have emerged as a more significant benefit than many banks initially expected. While only a quarter anticipated cost reduction as a primary driver, more than 70% now report tangible savings. Almost half have saved over $1m in the past year alone, with a majority forecasting annual savings exceeding $5m by 2026. ,
Attention is now shifting to generative and agentic AI, which banking leaders see as the next major leap forward. Optimism is tempered by practical concerns, particularly around regulatory scrutiny, auditability and governance. Meeting regulatory expectations is the leading challenge, while over half of respondents worry about excessive reliance on automation and the erosion of human expertise. Many also anticipate increased operational complexity and higher implementation costs as these technologies mature.
Among payment firms and FinTechs, AI adoption is being driven even more directly by commercial pressures. Fraud prevention is the leading use case, with nearly three-quarters of firms either piloting or operating AI-driven solutions. AML and screening follow closely, reflecting the need to balance regulatory compliance with fast-moving, high-volume transaction environments.
Cost benefits are already material. Almost three-quarters of payment firms report savings from AI in AML operations, with many expecting annual savings above $5m as deployments expand. Investment plans mirror this confidence, with strong interest in generative and agentic AI as firms look to scale efficiently without proportionate increases in headcount.
Overall, the findings point to a clear inflection point. Banks, payment firms and FinTechs are moving beyond isolated pilots towards structured, scalable AI rollouts. Success in the next phase will depend on building internal expertise, integrating AI into core systems and maintaining explainability and governance as adoption deepens.
For more insights, read the report here.
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