{"id":4158,"date":"2026-01-02T13:53:17","date_gmt":"2026-01-02T13:53:17","guid":{"rendered":"https:\/\/fintech.global\/globalregtechsummitapac\/?p=4158"},"modified":"2026-01-02T13:53:17","modified_gmt":"2026-01-02T13:53:17","slug":"is-the-line-between-fraud-and-aml-disappearing","status":"publish","type":"post","link":"https:\/\/fintech.global\/globalregtechsummitapac\/is-the-line-between-fraud-and-aml-disappearing\/","title":{"rendered":"Is the line between fraud and AML disappearing?"},"content":{"rendered":"

Fraud prevention and AML have operated as parallel disciplines \u2014 adjacent, but structurally separate, for a long time. Fraud teams focused on stopping unauthorised transactions in real time, while AML functions concentrated on detecting patterns of illicit behaviour after the fact.<\/strong><\/p>\n

Today, that division is becoming harder to defend. As financial crime grows faster, more interconnected and more digital, the signals that indicate fraud increasingly overlap with those that suggest money laundering, forcing institutions to rethink whether these are still two distinct problems.<\/p>\n

At a data and behavioural level, the convergence is already well underway. The same indicators \u2014 unusual transaction flows, synthetic identities, mule networks, rapid fund movements across accounts \u2014 sit at the heart of both fraud and AML risk. Criminals no longer specialise neatly; fraud schemes are routinely used to generate proceeds that must then be laundered, while laundering networks rely on fraudulent onboarding and account abuse to function.<\/p>\n

As a result, siloed systems miss context. A transaction that looks like low-value fraud noise in one system may form part of a larger laundering pattern when viewed across channels and time.<\/p>\n

A key question being asked on this, is why are financial insitutions increasingly merging their fraud and AML teams and systems?<\/p>\n

According to Ryan Swann, CRO at\u00a0RiskSmart<\/a>, a reason for this centers on the fact that \u2018criminals don\u2019t respect organisational charts\u2019, adding: \u201cThe same data signals that flag fraud often point to money laundering too; it\u2019s logical to connect the dots.\u201d<\/p>\n

When contemplating how unified platforms detect complex financial crime patterns that separate tools may miss, Swann cites taking a more full, holistic view.<\/p>\n

He said, \u201cIt\u2019s about seeing the whole chessboard, not just one corner. Unified platforms reveal patterns that only emerge when you combine perspectives.\u201d<\/p>\n

For Swann, governance and trust are some of the crucial challenges that comes with integrating data, models and governance across functions that were previously siloed.<\/p>\n

\u201cGetting teams to share data is as much a cultural shift as a technical one. You need clarity on who owns what and who\u2019s accountable,\u201d he said.<\/p>\n

Could this convergence lead to a more holistic \u2014 or more complicated \u2014 approach to financial crime prevention? \u201cBoth,\u201d said Swann. \u201cHolistic doesn\u2019t mean simple. But done well, convergence gives organisations the intelligence edge criminals don\u2019t expect.\u201d<\/p>\n

Attacking the processes\u00a0<\/strong><\/p>\n

According to Tim Khamzin, CEO and founder of\u00a0Vivox AI<\/a>, the line\u00a0between fraud and AML is disappearing because, in practice, fraudsters are increasingly attacking the very processes designed to stop financial crime, particularly KYC and KYB.<\/p>\n

He said, \u201cAML officers are effectively being asked to catch fraud, because fraudsters exploit onboarding and due-diligence workflows just as much as they exploit payments. Both teams rely on the same data sources and conduct almost identical investigative steps, so maintaining separate toolsets inevitably leaves blind spots. Unified platforms address this by connecting patterns that siloed systems can\u2019t.\u201d<\/p>\n

In the words of Khamzin, when fraud signals, AML red flags and onboarding inconsistencies sit in one reasoning layer, institutions can detect complex behaviours that cross regulatory categories. For example,\u00a0synthetic identities passing KYC but triggering downstream fraud patterns, or mule-network behaviours masked as legitimate corporate onboarding.<\/p>\n

\u201cThe real challenge is not technical but organisational: merging data standards, risk models, alerts, case management and governance across historically distinct teams,\u201d said Khamzin.<\/p>\n

\u201cIt requires alignment on definitions of risk, escalation paths, and evidential standards that regulators will accept. But the reward is a more holistic view of financial crime. Instead of fragmented signals, institutions get a unified narrative of intent, behaviour and risk. For many, this convergence is no longer optional, it is becoming the only effective way to protect the integrity of onboarding and prevent criminals from exploiting gaps between fraud and AML controls.\u201d<\/p>\n

Read the daily RegTech news<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Fraud prevention and AML have operated as parallel disciplines \u2014 adjacent, but structurally separate, for a long time. 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