Europe’s financial sector is grappling with a perfect storm of interconnected risks. Fraud and money laundering are no longer discrete criminal threats — they now reinforce one another, accelerated by the expansion of online marketplaces, instant payments, and increasingly sophisticated AI-driven deception.
As crime typologies evolve, institutions are under pressure to reorganise their risk models and adopt new operating frameworks that reflect this reality, said Moody’s.
This shift has given rise to a fresh approach: the convergence of fraud and anti-money laundering under a unified operational system known as FRAML. Rather than treating these two domains separately, FRAML champions joint analysis, shared infrastructure, and deeper collaboration — with the aim of aligning compliance with real-world criminal behaviour.
The rationale behind convergence is growing stronger. Juniper Research estimates 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.” With losses of this magnitude and tactics rapidly evolving, the fragmented model used across many banks now appears increasingly outdated.
Across the payments landscape, speed is amplifying risk. Predictions cited by Payments Industry Intelligence suggest that almost 28% of global electronic payments will be processed in real time by 2027. Meanwhile, instant payments represented around “15% of total real-time payments in Europe” in 2023, with adoption accelerating due to 2025 European Union rules mandating instant euro transfers. While regulations such as the Instant Payments Regulation and PSD3 strengthen consumer protections, the accelerated velocity of illicit movement reinforces the case for integrated oversight.
AI has further blurred traditional lines between fraud and money laundering. Deepfakes, coordinated impersonation, and automated scams exploit gaps in control frameworks still largely designed for manual review and siloed investigation. Traditional rule-based defences struggle to keep pace with dynamic crime.
FRAML offers a strategic alternative. Because fraud often precedes money laundering, treating them as one domain allows for faster triage, consolidated analysis, and improved investigative outcomes. Yet many institutions still operate with segregated teams, data sets, and systems — often resulting in blind spots, slower intervention, duplicated effort, and inconsistent regulatory reporting.
Central to FRAML is the creation of unified data infrastructure. Master data management helps financial institutions construct interconnected profiles spanning customers, accounts, payments, behaviours, and external risk indicators. Once harmonised, these datasets enable machine learning engines and analytics models capable of spotting anomalies at speed — from mule activity to synthetic identities. Cross-platform data management may also reduce cost and help compliance teams build more resilient frameworks.
Interoperability is a vital enabler. Making structured and unstructured data usable across teams — from logs to behavioural insights — supports deeper detection models and enables control frameworks to evolve. Tools such as dynamic dashboarding, scenario testing, and behavioural intelligence feed decision-makers with a single risk view aligned with standards like FATF, GDPR and the EU AML package.
Financial Intelligence Units stand to benefit from FRAML, too. Centralised datasets covering onboarding and ongoing monitoring can streamline investigation quality, simplify suspicious activity reporting, and strengthen collaboration. Better data visibility can reveal shell structures, prevent onboarding fraud, and ensure suspicious conduct is flagged more consistently.
With convergence accelerating, data governance has become more vital. Across the EU and beyond, institutions embedding FRAML must consider monitoring data quality, documenting audit trails, enhancing transparency, ensuring human oversight, and reinforcing cybersecurity safeguards. These principles help balance innovation with regulatory accountability.
Ultimately, FRAML symbolises a broader shift. As AI-driven crime outpaces legacy control structures, the industry’s future may lie in unified monitoring where one alert triggers both fraud and AML scrutiny. Sharing tools, data, and intelligence could reveal networks that previously went undetected, improve audit outcomes, reduce duplicative workflows, and ease alert fatigue.
For many institutions, FRAML is not simply a policy ambition — it may become a practical necessity in a world where digital criminals operate with speed and sophistication.
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