Hawk, an AI-powered financial crime detection company, has partnered with AML Intelligence to release a new eBook examining how leading financial institutions are approaching the governance of AI models used in anti-financial crime operations.
The publication draws on conversations with five senior practitioners at ING, Rabobank, Apple Bank, and Credit Suisse, exploring the real-world challenges and strategies that define effective AI model management.
It arrives at a time when the adoption of machine learning, generative AI, and agentic AI across anti-money laundering and fraud prevention functions is accelerating rapidly, with 91% of financial institutions now actively encouraging the use of AI for financial crime and compliance purposes.
However, the path to effective deployment remains far from straightforward. Joint research conducted by Hawk and Chartis revealed that 83% of AFC professionals have struggled to interpret or place trust in AI model outputs, while 70% have encountered issues with model performance, underscoring the scale of the governance challenge facing the industry.
Titled ‘Inside AI Model Management: Lessons from Anti-Financial Crime Leaders’, the eBook spans a wide range of critical subjects. These include the reasons model development frequently falls short, the foundational importance of problem definition and data quality, and how AI-driven and traditional rule-based approaches can inform one another.
The publication also examines how organisations track and respond to model drift and human drift, the significance of maintaining a human in the loop, and why explainability must be operationally meaningful rather than theoretical. It further addresses the distinct governance considerations that arise when working with generative and agentic AI, as opposed to conventional machine learning.
A central conclusion of the eBook is that the institutions most capable of leading on AI are not necessarily those deploying the most advanced models. Instead, competitive advantage lies in building governance structures that are resilient, auditable, and agile enough to keep pace with an evolving risk landscape.
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