Building Robust Anti-Fraud & Scam Capabilities at the National Level
FNA Papers Series
By Florian Loecker, Amanah Ramadiah, Dr Kimmo Soramaki & Will Towning
The rapid global adoption of instant payment systems has inadvertently catalyzed a surge in sophisticated financial crimes, notably Authorized Push Payment (APP) fraud. As fraudsters exploit the speed of these systems, they employ complex, cross-bank transaction networks and multiple generations of mule accounts to quickly launder stolen funds. Traditional fraud detection mechanisms, which rely on standard statistical rules and siloed bank-level data, are fundamentally limited because they fail to capture the broader network dimension of illicit activities. To overcome this structural blind spot, this paper proposes the establishment of robust, national-level anti-fraud capabilities based on centralized intelligence and network analytics.
The proposed framework outlines three foundational components for modernizing fraud prevention:
Data Sharing & Centralization: To construct central data hubs that collate cross-bank payment flows and reported fraud incidents, enabling complete network visibility while preserving data privacy.
Case Management & Investigation: To deploy automated tools that trace complex money trails in real time, grouping connected cases to accelerate investigations, improve fund recovery rates, and proactively uncover new mule accounts.
Fraud Detection & APIs: To utilize machine learning algorithms enriched with graph features for highly accurate anomaly detection. These insights are delivered to financial institutions via real-time APIs, empowering them to intercept fraudulent transfers before funds are irretrievably lost.
Ultimately, by transitioning from isolated, institutional defenses to a collaborative, network-wide data architecture, national authorities and financial institutions can successfully disrupt organized scam networks and restore public confidence in digital payment systems.
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