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What is FRAML?

Last updated: 23 October 2026

What is FRAML?

FRAML is the convergence of fraud prevention and anti-money laundering (AML) into a single, connected approach. Fraud and money laundering are closely linked, because fraud generates illicit proceeds that must then be laundered, and the same customers, accounts and networks often appear in both. FRAML joins the data, detection and investigation work of the two disciplines, so institutions see the full picture of financial crime risk, not two partial ones.

Why are fraud and AML converging?

Traditionally, fraud and AML teams operate separately, with different systems, data, case workflows and reporting lines. Criminals do not respect that boundary. Common crossover threats include:

  • Money mules. Accounts used to receive and move the proceeds of scams and fraud. See how to enhance money mule detection.
  • Authorized push payment (APP) scams. Fraud losses that quickly become laundering activity.
  • Account takeover and synthetic identities. Used to open or hijack accounts that later move illicit funds.
  • Scam proceeds moving through payment rails. Fraud events that should trigger suspicious activity reporting.

When fraud and AML do not share information, a signal seen by one team may never reach the other, and institutions can miss laundering linked to a fraud they have already identified.

What is the difference between fraud and AML?

Comparison Fraud AML
Core concern Preventing losses from deception and unauthorized activity. Stopping illicit funds being moved or disguised.
Typical focus Real-time prevention and customer protection. Detection, investigation and regulatory reporting.
Who is affected first The customer or the institution. The financial system and regulators.
Typical teams Fraud operations. Financial crime compliance.

What are the benefits of a FRAML approach?

  • A single view of the customer. Shared data and entity relationships show how a customer, account or network appears across fraud and AML.
  • Better detection. Signals from one domain improve models and rules in the other.
  • Fewer duplicate investigations. A unified case management process avoids two teams working the same case.
  • Faster, more consistent decisions. Teams share evidence, rationale and outcomes.
  • Lower operating cost. Consolidated technology and workflows reduce overlap.

How does FRAML work in practice?

Firms approach FRAML in three stages, which they can adopt gradually.

  1. Share data. Bring transaction, customer, device and behavioral data into one environment.
  2. Connect detection. Apply shared risk signals and entity resolution to link related customers, accounts and counterparties across fraud and AML.
  3. Unify investigation. Use a common case workflow, so fraud findings flow into AML decisions and reporting.

Full organizational convergence is not required. Many institutions begin by sharing data and alerts between teams, then work toward combined detection and case handling.

How does AI support FRAML?

AI and machine learning can find patterns across fraud and AML data that rules alone miss, and agentic AI can assemble evidence and prioritize alerts across both domains. See how SymphonyAI payment fraud detection and transaction monitoring work together, or watch our webinar on breaking down silos in financial crime.

FAQs

Is FRAML a product or a strategy?
It is a strategy. Vendors package it as technology, but FRAML describes how fraud and AML teams, data and processes work together.

Does FRAML mean merging the fraud and AML teams?
Not necessarily. Many firms keep separate teams and connect them through shared data, shared alerts and joint governance.

Is fraud a predicate offense for money laundering?
Yes. In most jurisdictions fraud is a predicate offense, which means the proceeds of fraud that are then moved or disguised constitute money laundering. See money laundering.

What are the main challenges of FRAML?
Data held in separate systems, different regulatory obligations, and different team incentives. Fraud teams are usually measured on loss prevention, while AML teams are measured on compliance outcomes.

For more, see anti-money laundering (AML) and payment fraud.