Resistant AI has been named ‘Digital Crime Fighter of the Year' for winning the inaugural hackathon challenge at the recent ACAMS Hollywood conference in Florida last week. The hackathon participants were given a large and complex synthetic dataset with more than a million rows that included customer information, wires, credit card and ATM activity and tasked to uncover potential human trafficking activity.

The Resistant AI solution won by quickly assessing data and uncovering anomalous patterns to detect behaviors indicative of human trafficking offences. While its almost impossible to measure the scope and scale of human trafficking, according to the International Labour Organisation (ILO), there are over 40 million victims of modern slavery worldwide. The prevalence of human trafficking was highlighted further in recent months, as reports circulate of traffickers targeting vulnerable women and children fleeing the crisis in Ukraine.

The hackathon, sponsored by PwC, represents an urgent need to use intelligence in the fight against financial crimes that reach far and wide around the world. Judges for the event were Jim Candelmo, chief bank secrecy act and AML sanctions officer at PNC; Melissa Strait, chief compliance officer at Coinbase; and Scott Nathan, head of AML transaction monitoring at Citi.

"The teams participating were nothing short of incredible," said Vikas Agarwa, partner at PwC, Financial Crime Unit Leader. "Each group demonstrated multiple unique approaches and attacks -- each of them focused on how, through purpose and innovation, they quickly identified some or all elements of our hidden human trafficking data."

"Resistant AI analyzes the hidden relationships between identities and transactions to draw a better decision boundary between legitimate and criminal activities," said Martin Rehak, CEO of Resistant AI. "Our team took a data-driven approach to the hackathon exercise, using the anomaly detection tools in our AI-powered identity forensics solution."

Resistant AI's advanced analytics solution helped the hackathon team more effectively pinpoint anomalous and suspicious behaviors and also uncover previously undetected patterns. Through a variety of machine learning techniques, the team produced results that uncovered the chain of human trafficking activity.

The challenge is that many organizations are still relying on traditional scenarios or rule-based systems to find and root out suspicious behavior. While these systems act as a useful baseline, they only look for scenarios that are known as suspicious, and miss the unknown patterns of behavior. Even when new patterns are detected, there is a delay in defining rules that will detect those patterns in the future. The volumes of alerts and false positive rates of traditional systems also make it extremely difficult for investigators to identify true risk amid the noise.

"Congrats to the winner, Resistant AI, that showcased the power of innovation with tangible, data driven approaches to uncovering unusual, high-risk behaviors indicative of human trafficking across large complex data sets," said Scott Nathan, head of AML Transaction Monitoring at Citi. "Gone are the days of multi-year, linear ‘transaction monitoring' system implementations. Today it's all about ‘customer interactions' across multi-dimensional data constructs (internal and external), agile development, and the confidence to fail-fast while processing petabytes of data at scale."