Financial institutions are struggling to spot the tell-tale signs of human trafficking due to resource constraints and the nature of the anti-money laundering (AML) compliance environment, according to experts.
Today marks National Human Trafficking Awareness Day in the US. Also known as “modern slavery,” it refers to a range of heinous crimes, including forced begging, debt bondage, domestic servitude and sex trafficking.
The most recent stats from the International Labour Organization (ILO) claim over 40 million people around the world are currently trapped in modern slavery. According to the UN, the share of children among these victims has tripled over the past 15 years, while the share of boys has increased five-fold.
Banks should play an important part in the fight to detect such activities by spotting suspect money flows, according to Nicola Eschenburg, FinCrime Testing Service venture lead at BAE Systems Applied Intelligence.
“Virtually all crime is conducted for profit, and that profit can’t be recognized if the money can’t be laundered and spent,” she told Infosecurity.
However, the nuances of human trafficking activity can often be missed by compliance staff, as they can appear innocent to the untrained eye.
“For example: a young woman staying in multiple low-cost hotels around the country, mostly eating late at night at fast food joints and visiting pharmacies several times a week. That’s indicative of sexual trafficking, but you need a number of the elements plus some profiling to be able to draw the bigger picture,” Eschenburg explained.
Part of the challenge is compounded by the nature of AML compliance. There are 22 “predicate offenses” outlined by the EU linked to money laundering, of which human trafficking is just one.
Compliance staffers must monitor all of them, each of which has multiple typologies or behaviors associated with them.
“This means that most teams by nature have to have generalists rather than specialists in certain types of crime, which in turn means subtle nuances or indicators can get missed,” argued Eschenburg.
“Compounding this is the sheer volume of activity that needs to be monitored and analyzed – it becomes a bit of a treadmill of working through alerts which doesn’t leave much if any free time for conducting open-source research, speaking to law enforcement about what criminal behaviors look like on the front line, reading court transcripts and honing and refining detection techniques as the output.”
BAE’s AML report out earlier this year claimed that 76% of compliance officers believe AML compliance has become little more than a box-ticking exercise, and 62% said criminal activity is getting harder to spot. Half (50%) of money laundering goes undetected, it estimated.
Brian Ferro, director of AML at Feedzai, told Infosecurity that better technology is only one part of the solution and that stronger public-private partnerships were essential.
“To date, there hasn’t been a lot of collaboration between regulators, banks and law enforcement to put together best practices and updates to regulations. One of the biggest complaints from the banks is the lack of feedback when filing regulatory reports with law enforcement. While the information provided to law enforcement could aid in an ongoing investigation, completing their investigation could take several months if not years. So while not compromising their ongoing investigations, there needs to be regular feedback from law enforcement agencies to the banks so that the bank investigators know what information or activity is useful, new or emerging trends and what to look for in their own work,” he explained.
“On the regulatory side, the information from law enforcement could benefit banks in looking for new types of criminal activities. However, banks are hesitant to change their surveillance policies for fear of being penalized by the same regulators for not identifying previously unknown patterns of suspicious behavior. There has to be a partnership with regulators to encourage the use of new technologies, and at the same time allows the banks to build better detection models without fear of repercussions.