The ACAMS Annual AML & Anti-Financial Crime Conference in Hollywood, Florida, is a high-profile event in the anti-money laundering sector, serving as an annual platform for tech companies to demonstrate their solutions to FinTechs and traditional financial services.
At this year’s conference, Resistant AI, a previous double winner of the PwC Hackathon, showcased its application of artificial intelligence in combating financial crime.
The team at Resistant AI had over 2.3 million synthetic customer and transaction data points to analyse within a three-day period. Using their extensive experience gained from partnerships with leading payments firms and neobanks, they identified instances of fraud, money laundering, sanctions evasion, and other suspicious activities. What distinguished Resistant AI’s approach was the ability of their tool to put these instances into context.
Understanding the extensive network of money movements and account relationships is a challenging task, especially with millions of data points and a race against time. The team had to incorporate a wealth of additional information such as IP address data, contact numbers, physical addresses, and VPN information provided for the Hackathon. The ability to visualise and make sense of this complicated interconnected network, fondly named “The Tribble”, lies in the power of AI-powered forensic analysis.
Resistant AI’s approach is not a grand unified model focusing on specific AML tactics. Instead, it uses layers of simpler models that each highlight statistically unusual behaviours across different dimensions. This approach, which they call the ‘ensemble approach’, allows their tool to act as a highly accurate yet flexible countermeasure against uncommon activities.
The ensemble approach entails the application of various detectors that most effectively revealed hidden patterns and suspicious behaviours in the Hackathon dataset. These included anomaly clustering, detection of anomalous amounts, intelligent sanctions evasion screening, and more sophisticated analysis using large language models (LLMs) for adverse media screening, predicting transaction descriptions, and graph analysis of suspicious typologies.
This method enabled the AI to cut through the chaos of The Tribble and expose numerous fraud clusters displaying a range of suspicious behaviour. They included money mules, mirror trading, smurfing, anomalous high-value transactions, entity consolidation at a high-risk institution, suspicious transaction details, and sanctions/PEP evasion.
The team at Resistant AI showcased the power of AI at the conference by illuminating a case of suspicious activity involving a receptionist named Maggie Barelty. Their AI forensics detectors identified a series of anomalous high-risk actions that led to a detailed investigation of the account.
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