One Customer, Many Signals: Connecting Fraud & AML Intelligence

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Financial crime risk is converging around the customer, but fraud and AML controls often are not. Criminals are exploiting the gaps, customers are bearing the losses, and regulators are asking why institutions still manage connected risks in silos. 

A scam that begins as a fraud loss can become an AML exposure, revealing links to organised crime. A customer who onboards cleanly may activate as a mule months later. Yet inside many institutions, the same event is still split across separate teams, systems and reporting lines. The direction of travel is clear: financial crime risk is converging faster than the institutions responsible for managing it. 

This webinar will examine what convergence looks like in practice. It will follow the customer through the risk lifecycle – onboarding, account activation, behavioural change, suspicious activity, investigation and reporting – asking where the first reliable signal appears, which function owns the response, and what happens when fraud and AML teams each hold only part of the file. 

The discussion will also examine how technology is changing the operating model. Institutions are moving from transaction monitoring toward customer monitoring, from static controls toward lifecycle risk intelligence, and from isolated fraud and AML tooling toward shared data, analytics and AI. But convergence also raises harder questions around governance, explainability, accountability and reporting. 

This webinar moves beyond theory to examine what convergence looks like in practice. The session will ask whether financial institutions are ready for a financial crime model built around the customer, rather than the function. 

Key themes: 

• From static onboarding to lifecycle risk: How do you detect a clean account that turns into a mule months later, and where do the first reliable signals appear? 

• The typology with no owner: When a scam is both a fraud loss and an AML event, who owns the response and what gets reported? 

• From transaction monitoring to customer monitoring: Moving beyond isolated alerts to a continuous, behavioural view of customer risk over time. 

• When fraud inherits the AML plumbing: As mule restrictions and intelligence sharing become formal obligations, how must fraud controls and governance evolve? 

• Governing AI across siloes: Can generative and agentic AI be deployed and governed as a shared utility for both fraud and AML, rather than within separate domains?