It’s a familiar setup in many insurers: the AML team sits in compliance, and the fraud team sits in claims, underwriting, or operations. Each runs its own systems, follows different workflows, and reports to separate chains of command.
They may collaborate on the occasional case, but for the most part, they operate independently.
After all, AML is about money laundering and fraud is about internal or customer deception. Well, not anymore.
In this round of the “Compliance myth-buster series: Insurance edition”, we tackle the false comfort of siloed operations and explain why separating AML and fraud is costing insurers in more ways than one.
Traditionally, AML and fraud have been treated as separate domains. AML focuses on regulatory compliance, transaction monitoring, and suspicious activity reporting. Fraud teams zero in on claims abuse, impersonation, or internal misconduct.
Each has different objectives, tools, and mandates. And in large, federated organizations, that separation is further reinforced by geography, business line, or tech stack.
But this division is more based on organizational legacy than operational logic.
Financial crime doesn’t care how you structure your org chart. In fact, criminals actively exploit the gaps between teams.
Many money laundering schemes start as fraud:
These don’t just defraud the insurer; they also serve as placement and layering mechanisms to clean illicit funds.
Without shared intelligence and coordinated investigation, each team sees only part of the picture and the full crime goes undetected.
Here’s how disconnected operations create risk:
When AML and fraud don’t talk to each other, the criminal wins.
Leading insurers are now shifting toward unified financial crime intelligence connecting data, insights, and expertise across AML, fraud, and sanctions teams.
Here’s what that looks like in practice:
This isn’t about adopting a single tool. It’s about aligning teams, data, and objectives to see the full picture of financial crime risk.
Insurers can no longer afford to treat AML and fraud as disconnected functions. Bringing them together through shared intelligence and coordinated oversight unlocks a more comprehensive view of risk, reduces duplication, and strengthens defenses against financial crime.
That concludes the Compliance myth-buster series: Insurance edition. Miss some articles? Be sure to give them a read by clicking on the links in 'Related Resources' below.
Compliance myth-busters: Insurance edition: Myth #1: AML insurance - still low risk?
Compliance myth-busters: Insurance edition: Myth #2: False positives are inevitable in insurance AML
Compliance myth-busters: Insurance edition: Myth #3: Rules are enough for AML
Compliance myth-busters: Insurance edition. Myth #4: If it’s not regulated, it’s not a risk.
Redefining Risk: The Insurance Industry’s New Reality
Webinar: The domino effect: How breaking down silos amplifies financial crime prevention
Webinar: Regulators, risk & reinsurers: AML’s New Frontier
Data Sheet: Compliance for Insurance
Learn more about AML compliance designed for insurance
Download our white paper “Elevating compliance in insurance: A risk-driven, AI-powered approach to AML and sanctions screening” and discover how organizations are strengthening compliance across all product lines.
Historically, AML focused on regulatory compliance and suspicious activity reporting, while fraud teams concentrated on claims abuse and policy manipulation. Different mandates, reporting lines, and technology stacks reinforced the separation, even though the underlying risks increasingly overlap.
Many laundering schemes begin as fraud. Staged accidents, exaggerated claims, fake identities, and policy cancellations can all serve as mechanisms to move or disguise illicit funds. Without shared intelligence, each team sees only part of the pattern, allowing threats to slip through.
Siloed operations lead to duplicated investigations, missed network links across policies or entities, inconsistent thresholds, and gaps in regulatory reporting when fraud-related activity is not escalated as potential money laundering. This delays detection and increases overall financial crime exposure.
Rather than merging tools, leading insurers focus on aligning data, intelligence, and governance across teams. This includes shared typology insights, coordinated escalation pathways, unified risk understanding, and more frequent cross-team analysis to identify patterns that single domains can’t see alone.
Insurers can map overlapping typologies, create joint escalation protocols, establish shared reviews, leverage AI analytics to surface cross-domain patterns, and educate leadership on the value of integrated financial crime intelligence. These steps build a more complete and defensible view of risk.
Thierry Fortin is a seasoned financial technology professional with over 25 years of experience in banking, consulting, and enterprise software implementation. Currently based in Paris, he serves as a key member of the SymphonyAI Financial Services sales team, where he supports the delivery and adoption of advanced AI-driven financial crime solutions. Prior to joining SymphonyAI, Thierry spent over a decade at BAE Systems Digital Intelligence, where he held roles including Senior Business Consultant and Solutions Consultant, working closely with leading financial institutions to implement risk and compliance technologies across Europe. His earlier roles include project positions at Société Générale and Crédit Foncier de France, where he specialized in risk management systems, securitization projects, and client risk detection solutions. He also brings international experience from his time in the U.S. with Nordstrom, where he managed logistics systems and led audit-related tech initiatives. With a deep technical background in systems development, business analysis, and project management, Thierry brings a unique combination of hands-on experience and strategic insight to every engagement. He is passionate about driving innovation in financial services, particularly in the fight against financial crime.
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