FRAML: The convergence of fraud and money laundering

FRAML is an umbrella term for fraud and anti-money laundering. It reflects the growing focus on how the two are converging and the benefits of an integrated approach to detecting money laundering and fraud for fighting financial crime.

Money laundering and fraud exist in a shared ecosystem where criminals leverage financial institutions to steal identities, access data, and launder illicit funds. From embezzlement and insider trading to credit card fraud and identity theft, the success of these financial crimes often hinges on being able to launder the proceeds through the legal financial system. 

Our State of Financial Crime 2022 survey showed that fraud is one of the top three predicate offenses of concern for firms globally. A recent survey by KPMG also found that 67% of senior risk executives experienced fraud in the past 12 months and that 38% expect the risk of fraud to increase in the next year.

As concern about fraud accelerates, firms are also placing a greater emphasis on the nexus between money laundering and fraud. Specifically, focusing not just on how the illicit money was acquired, but also on how criminals are seeking to hide its origins in the financial system. 

This shift requires many teams to restructure away from siloed fraud and anti-money laundering teams and towards an integrated approach. That’s why, in a recent session on FRAML, the Association of Certified Anti-Money Laundering Specialists (ACAMS) referred to the combined functions as “today’s compliance power couple,” citing their ability to increase operational efficiency and optimize risk management as reasons behind its growth in popularity. 

What does FRAML mean?

By aligning fraud detection with AML operations, FRAML solutions automate data sharing and insights between functions that often operate separately – fraud is usually considered within the realm of security, while financial institutions (FIs) treat AML as a matter of compliance. 

Regulators have encouraged a collaborative approach. The Financial Crimes Enforcement Network (FinCEN) issued an advisory highlighting an expectation that FIs promote “communication and collaboration among internal AML, business, fraud prevention, and cybersecurity units.”

Anti-fraud and AML teams often track the same individuals and types of criminality. By combining the two functions and analyzing the intersection between money laundering and fraud, FIs can reduce false positives, increase the speed of customer onboarding, and better identify risk early on in the customer lifecycle.

FRAML and artificial intelligence (AI)

Powered by artificial intelligence (AI), FRAML solutions combine and analyze vast amounts of data from transactions, consumers, and external sources to create accurate, holistic risk profiles. 

Not only does this automation enable FIs to create a more transparent and informed picture of who their customers are and the potential risks they pose, but the high-quality, orchestrated insights it generates can also help FIs shift from merely detecting criminal activity to predicting and preventing it. 

How can a FRAML strategy optimize risk management?

With AI, FIs can also implement self-learning behavioral analytics to detect anomalies, such as deviations in customer behavior and transactions that are not physically possible based on location. 

Building a collaborative approach to a FRAML solution

While a FRAML approach is recommended across industries, invariably the risks and typologies firms are concerned about will often be industry-specific. As a result, firms need to ensure any external RegTech firms they partner with have high-quality data that aligns with their business model. Some firms will also have to handle more complex payment chains, screening both business (B2B) and consumer (B2C payments).

Success story: RealPage detects fraud in real-time

Leading global provider of property management software and ComplyAdvantage customer, RealPage, experienced these benefits first hand. The flexibility to build custom scenarios helped RealPage implement unique rule sets to reflect its requirement to follow individuals across different state jurisdictions and identify connected individuals. The real-time results seen by RealPage was the true mark of success as its fraud alert activity could be reviewed immediately and allow team members to act as necessary to prevent significant losses for its clients.