Fraud and money laundering are ever more intertwined as finance becomes increasingly digital amid the rise of online platforms and the integration of click to pay and instant payment services. This creates challenges for financial service companies seeking to protect themselves and their customers from risk.
Merchants’ losses from online payment fraud will exceed $362 billion globally from 2023 to 2028, with the annual loss climbing to $91 billion in 2028, according to estimates from digital tech analyst firm Juniper Research. At the same time, regulatory initiatives including the EU’s Instant Payments Regulation (IPR) are supporting the shift to digital finance. The IPR requires funds from credit transfers to be available within just 10 seconds of a payment being made. Such rapid movement of payments gives financial institutions little time to detect potential financial crime.
Fraud prevention strategies that were designed for longer payment settlement cycles to identify risks and assist with regulatory compliance are likely to fall short in this rapidly evolving digital era. The proceeds of fraud often flow through complex money laundering schemes that might involve tactics like money mules, false identities, and layered transactions.
To adapt to the evolving payments landscape and keep pace with criminals, a unified approach has been developed that brings both fraud and anti-money laundering regulation and detection under one roof – the FRAML (fraud and anti-money laundering) framework.
By establishing unified FRAML frameworks, financial institutions may improve the way they tackle the threats and also create opportunities to reduce expenses, improve efficiency, and make it easier to comply with EU regulations.
Today, fraud can range from cybercrime and insider threats to complex cross-border scams, while money laundering disguises the illicit origins of funds and may fuel further illegal activities, such as drug trafficking and corruption. Yet many organizations still maintain separate teams, systems, and data to manage fraud and anti-money laundering (AML). This can limit visibility, affect response times, and create inefficiencies. FRAML frameworks are designed to integrate both fraud and AML capabilities for a faster, more unified assessment of customer-related risk. At the heart of FRAML lies master data management. This involves unifying records for key data such as information on customers, accounts, and transactions, which may come from many sources, both internal and external, including company records, financial histories, transaction data, media reports and sanctions lists.
When data is aggregated and harmonized through a FRAML framework, teams may be better positioned to recognize complex behavioral patterns and interrelated risks. Dynamic integration of know your customer (KYC) controls, payment, behavioral, and external data feeds can also support machine learning models programmed to identify potential anomalies and suspicious activity at speed. Integrated records may bolster customer screening and reviews, helping to identify potential risks.
Efficiency may also improve as cross-platform data management can reduce gaps and redundancies. This could potentially lower costs and help strengthen anti-fraud and AML functions.
FRAML frameworks may break down information silos by enabling structured and unstructured data — from transaction logs to market feeds — to be accessed, standardized, and analyzed across systems. This supports more granular behavioral intelligence and the application of advanced analytics to potentially uncover cross-channel patterns and emerging crime typologies.
Establishing a shared dataset across fraud and AML functions — covering both onboarding and ongoing monitoring — may offer a number of advantages:
1. Centralized data can be more efficient for collaboration and comprehensive analysis.
2. Uniting teams and filing reports on suspicious activity through a centralized system may improve both the quality and consistency of reporting.
3. Collecting robust data during onboarding may help identify and prevent fraudulent applications before accounts are opened.
4. Identifying risky corporate behaviors can indicate the possibility of shell companies being used for fraud, money laundering, and sanctions evasion.
For EU-based financial institutions, and other obligated entities, FRAML reflects a strategic response to the convergence of fraud and AML risks.
To strengthen FRAML data governance, institutions could consider focusing on five areas:
1. Monitoring data quality and bias through rigorous audits and performance checks.
2. Maintaining detailed documentation and audit trails of all data processing and decisioning steps.
3. Supporting transparency and explainability by providing rationales for alerts and risk scores.
4. Establishing human oversight with defined roles and escalation protocols.
5. Implementing robust security and privacy controls — including encryption, access management, and privacy impact assessments.
Institutions that embed these principles into their FRAML frameworks may be better positioned to support compliance obligations while improving operational resilience without impeding innovation.
By Chor Teh, Senior Director, Financial Crime Compliance, Industry Practice Leads at Moody’s
