Financial institutions have long treated fraud and anti-money laundering (AML) as separate disciplines, run on separate timelines by separate teams. Fraud detection demands split-second decisions, while AML assessment unfolds over months or years, weighing customer behaviour, entity relationships and geographic exposure.
Yet the same customer, transaction or network can generate signals relevant to both, and treating them in isolation risks missing the bigger picture, said ZIGRAM.
ZIGRAM recently discussed the importance of building an adaptive AML risk scoring model for fraud & AML.
A unified risk scoring model attempts to solve this by drawing together transactional, behavioural, customer, entity, geographic, network and historical data into a single, contextual assessment.
Crucially, unification alone isn’t enough. An effective model must also adapt as risk evolves, distinguish genuine threats from isolated anomalies, explain its reasoning, and translate scores into clear operational action.
Static models, built on fixed rules and thresholds, struggle here. A customer rated medium-risk at onboarding may later shift into higher transaction volumes, new jurisdictions or unfamiliar counterparties, leaving the original assessment technically valid but practically outdated. Adaptive scoring closes this gap by allowing new information to update a customer’s risk profile as it changes, without descending into uncontrolled, unexplainable adjustment.
Building such a model requires selecting the right variables: transaction velocity, geographic exposure (including benchmarks like the Basel AML Index), customer and entity profiles, behavioural patterns, network relationships and historical alerts. None of these carry meaning in isolation; a spike in transaction velocity may be inconsequential alone, but far more significant alongside new geographic exposure and flagged counterparties.
Machine learning is increasingly used to tune these interactions, identifying complex patterns, reducing false positives and supporting recalibration as threats evolve. However, this cannot come at the expense of human oversight. Model governance, including validation, drift detection, version control and periodic recalibration, must remain intact so that adaptability doesn’t compromise defensibility with regulators and auditors.
Explainability is central to this. A risk score of 87 out of 100 means little without context on what drove it, whether that’s elevated geographic exposure, behavioural deviation or historical alerts. For investigators, understanding why a score changed is often more valuable than the score itself.
ZIGRAM’s AI/ML-powered analytics engine is one example supporting this shift, enabling institutions to analyse interconnected fraud and AML signals within a shared FRAML architecture, rather than isolated, static profiles.
Read the full ZIGRAM post here.
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