The concept of FRAML, the integration of Fraud and Anti-Money Laundering (AML), is radically altering the outlook of risk management and compliance in the financial sector. A recent webinar, led by the Electronic Transactions Association (ETA) and sponsored by Hawk AI, engaged a panel of Fraud and AML professionals to dissect the benefits and hurdles of FRAML.
Chaired by Caroline Hometh from RPY, with contributions from experts such as Hawk AI’s Wolfgang Berner, NAB’s Christopher Mascaro, and Armen Khachadourian from Global Vision Group, the webinar offered a comprehensive exploration of FRAML’s potential and challenges.
FRAML represents a novel approach that unifies fraud detection and AML measures. It is based on the principle that fraud often precedes money laundering, and hence it aims to pre-emptively thwart these illicit activities. By adopting FRAML, financial organisations can enhance their risk management, unravel intertwined criminal activities, and mitigate the potential financial, reputational, and legal repercussions associated with fraudulent transactions and money laundering.
“When you’re looking at things holistically, you can do a lot more,” said Christopher Mascaro. “If you know how the funds are initially obtained, you can better track them throughout the system.”
Historically, Fraud and AML functions within financial organisations operated independently, each with its own set of responsibilities and goals. Nevertheless, a symbiotic relationship between the two can facilitate a broader understanding of fraud and financial crime. Streamlining the investigative process and melding the expertise of both functions can aid in identifying patterns and trends indicative of potential fraud or money laundering schemes.
Armen Khachadourian commented that fraud requires immediate response, while AML focuses on compliance and retrospective actions. It is essential to bridge the information gap between these two entities rather than letting them operate in isolation.
Regulators have been ramping up pressure on financial organisations to amplify their efforts against financial crime, resulting in escalating penalties for inadequate monitoring of transactions. With the digitalisation of transactions, fraud methods have evolved to exploit the digital realm, as Khachadourian highlighted with the rise of fraud through prepaid cards. Thus, breaking down the information silos and promoting collaboration between fraud and AML teams is crucial to counter these sophisticated threats.
Furthermore, AI and machine learning technology can assist both Fraud and AML teams in detecting suspicious activities, reducing false positives, and focusing on genuine cases. The distinguishing factor between using this technology for Fraud and AML is the timing, as fraud detection occurs in real-time while AML is retrospective.
As Mascaro noted, that AI won’t replace human input in Fraud or AML. However, as a ‘virtual teammate,’ AI can review transaction data in volumes that are impossible for humans. Combining human and technological resources can yield immense benefits for Fraud and AML teams alike.
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