Banks have long treated fraud and money laundering as separate disciplines, even though the same customer, account or transaction can carry signals relevant to both.
Fraud teams work in seconds, hunting for anomalies in real time, while AML teams take a longer view, weighing behaviour, entity relationships and geographic exposure over months. Keeping these two systems apart means institutions risk losing the fuller picture financial crime actually presents, said ZIGRAM.
ZIGRAM recently discussed the importance of building an adaptive AML risk scoring model for fraud & AML.
A unified risk scoring model tries to close that gap by combining transactional, behavioural, customer, entity, geographic, network and historical signals into a single, more contextual assessment. But simply merging data isn’t enough. An effective model must also adapt as risk evolves, separate genuine threats from one-off anomalies, and explain its own reasoning clearly enough for auditors and regulators to trust it.
Static models, built on fixed rules and weightings, struggle here. A customer rated medium risk at onboarding might later shift into new jurisdictions or start transacting at far higher volumes, yet their original score can remain technically valid despite no longer reflecting reality. Adaptive scoring addresses this by allowing new information to update a risk profile in a controlled, explainable way, rather than waiting for the next periodic review.
Reconciling fraud’s short-term urgency with AML’s long-term perspective is central to making unification work. A sudden spike in cross-border payments might look like fraud in isolation, but becomes far more significant when set against a customer’s established profile, counterparties and alert history. This is where a broader FRAML approach, connecting fraud and AML intelligence, gives institutions a more complete read on risk that might otherwise stay fragmented.
Selecting the right variables matters more than accumulating them. Transaction velocity, geographic exposure, customer and entity profiles, behavioural patterns, network relationships and historical alerts all provide meaningful context when interpreted together rather than in isolation. Machine learning can help surface relationships between these factors that manual rules might miss, while also reducing false positives, though this must operate within a governed framework rather than replacing human oversight.
Explainability remains non-negotiable. A score alone tells investigators little; understanding which factors drove a change from medium to high risk is what makes it actionable. ZIGRAM’s AI/ML-powered analytics engine supports this kind of unified scoring, letting institutions analyse interconnected fraud and AML signals within a shared risk context, without sacrificing the distinct operational needs of either function.
Read the full ZIGRAM post here.
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