Financial institutions across Europe are facing a convergence of threats that require a new approach to risk management. Fraud and money laundering are now deeply intertwined. This shift is driven by factors such as the shift to online marketplaces, the rise of instant payments, AI-enabled scams, and increasingly complex financial crime typologies.
In response, a unified framework is emerging in the form of fraud and anti-money laundering (FRAML) frameworks.
FRAML can provide a framework that helps financial institutions unify anti-fraud and AML operations with greater efficiency to help support compliance efforts and improve responses to emerging threats.
According to Juniper Research, estimates are that “global online payment fraud losses will exceed $343 billion cumulatively between now [2024] and 2027, with Europe accounting for about 26% of global fraud by value.” The case for convergence across these areas of financial crime continues to grow stronger.
An article published by Payments Industry Intelligence claims that real-time and instant payments are expected to represent nearly 28% of global electronic payments by 2027. And in Europe, as of 2023, it was estimated that instant payments represented around “15% of total real-time payments in Europe”, with adoption accelerating due to European Union (EU) regulations in 2025 mandating instant euro transfers.
While regulatory responses such as the Instant Payments Regulation and PSD3 with enhanced reimbursement obligations aim to mitigate risks associated with faster payments across Europe, the scale and speed at which fraud can be executed and money laundered make the case for an integrated approach to address threats.
AI-driven scams — including deepfakes and impersonation attacks — are compounding the challenge for financial services. These emerging and evolving threats may not be addressed by traditional fraud defenses, exposing systemic vulnerabilities.
Fraud is a predicate offense to money laundering. The proceeds of fraud often flow through complex laundering schemes that might involve money mules, synthetic identities, and layered transactions. Yet many institutions maintain separate teams, systems, and data strategies for managing fraud and AML. This separation potentially limits visibility, impacts response times, and presents operational inefficiencies.
FRAML frameworks may help address these limitations by integrating fraud and AML capabilities into a single strategic model. Institutions can then work to develop a view of customer risk that has the potential to improve detection, while simplifying regulatory compliance.
At the heart of FRAML is master data management. This approach involves the creation of unified records across key data domains such as customers, accounts, and transactions. It helps support cross-risk data interoperability, which can be a cornerstone for effective insight.
When data is aggregated and harmonized, teams may be better positioned to recognize complex behavioral patterns and interrelated risks. Dynamic integration of know your customer (KYC), payment, behavioral, and external data feeds can also support machine learning models that are programmed to identify potential anomalies and suspicious activity at speed. Integrated records can bolster customer screening and reviews, helping institutions surface potential risks such as money mule activity or use of synthetic identities.
Operational efficiency may also improve as cross-platform data management reduces gaps and redundancies. This has the potential to lower costs and strengthen the resilience of compliance programs.
Data interoperability can also play a critical role in breaking down information silos. It means that structured and unstructured data — from transaction logs to app events to market feeds — can be accessed, standardized, and analyzed across systems. This capability supports more granular behavioral intelligence and the application of advanced analytics to potentially uncover cross-channel patterns and emerging crime typologies.
Control frameworks increasingly require agile scenario testing and dynamic dashboarding. These tools can help give decision-makers a more unified view of risks and performance metrics. Data interoperability can also help support alignment with evolving regulations, like FATF, GDPR, and the EU AML package.
Establishing a shared dataset across fraud and AML functions — covering both onboarding and ongoing monitoring — may offer significant advantages for Financial Intelligence Units (FIUs).
For EU-based financial institutions, and other obligated entities, FRAML reflects a strategic response to the convergence of fraud and AML risks. This convergence is amplified by digital transformation, changing consumer behavior, and AI-driven crime.
To strengthen FRAML data governance, institutions could consider focusing on 5 areas:
Institutions who embed these principles into their FRAML frameworks may be better positioned to support compliance obligations while improving operational resilience without impeding innovation.
The convergence of fraud and AML through a FRAML framework reflects a practical response to new technology, data availability, and evolving threats. As financial crime becomes more complex and AI-driven, institutions may need to evolve control frameworks to keep pace, and FRAML could serve as a reference point for this evolution.
Unified monitoring can mean that one suspicious event triggers both fraud and AML responses, with joint suspicious activity reporting and audit support.
FRAML systems can be designed to view transaction data holistically, which may help in detecting issues such as mule networks, synthetic identities, and illegal cross-border flows.
And resource optimization follows as institutions are given the tools to help reduce duplicative data and technology, minimize alert fatigue, and foster improved collaboration between risk teams.
For more information on Moody’s platform for unified risk management, please get in touch with the team, we would love to hear from you.