It costs $7,000 to buy a human being in the United States.
Approximately 17,000 children, women and men are sold and bought every year in America and around the world as lifelong slaves used for labor and sexual exploitation.
January is Human Trafficking Awareness Month, prompting focus on a range of initiatives to combat this global scourge. Largely overlooked, however, is a strategy that could have enormous impact, quickly, at relatively low cost. That strategy is to modernize the technology used to deter and punish these crimes by finding their money trail and shutting down illicit pathways that allow transnational criminal organizations to fund their activities.
Global trade in humans is lucrative, generating an estimated $150 billion a year. The money is useless, however, unless traffickers can conceal its source by laundering it through the banking system. Forty years ago, U.S. and global regulators began building a massive set of anti-money laundering, or AML, regulations to combat terrorist financing, narcotics smuggling, weapons trafficking and human trafficking.
It’s not working. The United Nations Office on Drugs and Crime estimates that financial crimes now yield about $1.6 trillion annually, of which only 1% is caught. Unsurprisingly, such high profits and low risks feed an upward spiral of crime.
Furthermore, catching this 1% costs banks an estimated $50 billion a year. This means that banks incur astronomical costs and risks for poor results. It also means that adding resources to the current system cannot scale it up enough to turn the tide — expanding it by 99% would cost the GDP of the United Kingdom each year.
Fortunately, there is a better way. Finance is undergoing digitization, and so is financial regulation through so-called “regtech” — new-generation technology for redesigning both industry compliance processes and the regulations themselves. For anti-money laundering, the needed technology already exists but most isn’t yet in use because both banking systems and the legal and regulatory framework inevitably lag behind.
Two kinds of change are needed.
First, both banks and law enforcement need to modernize how they use the voluminous data the system already captures but doesn’t leverage. Money laundering always leaves digital footprints, but old tools can’t see most of them, especially as criminal organizations invent more sophisticated methods to hide their tracks.
A core problem is that outdated screening techniques generate massive volumes of false alerts, leaving the system awash in low-value information that neither industry nor law enforcement can effectively search, analyze and prioritize. The ratio of alerts raised to reports deemed useful by law enforcement is about one thousand to one, which means huge amounts of time is wasted by all parties on unnecessary data-gathering, investigation, report-writing and report-reading. Law enforcement officials today are often forced to compare information by sitting around tables piled with stacks of paper and yellow highlighters, while criminal rings move humans beings and money all around the globe.
Former Department of Homeland Security Supervisory Special Agent Robert Whitaker says one perverse effect of having to churn through so much irrelevant information is that the system sometimes skews toward addressing the crimes that are easiest to find, noting that it can be “very difficult to target and catch the largest and most sophisticated transnational criminal organizations, which often operate with little to no risk.”
This is a solvable problem. Digitized data can be analyzed using the kinds of machine learning and behavior modeling already employed every day in arenas like targeted online advertising. Computers can spot likely laundering behavior patterns and reveal connections between seemingly unrelated people, locations, accounts and devices. They can display these patterns on maps and as timelines and visual diagrams that human analysts can instantly recognize — and they can do this in seconds. Early test applications of these approaches have already cut the industry average false positive ratio in half and increased productivity of analyst teams by a factor of ten. Regtech could actually break the traditional zero-sum-game view of regulation by enabling a new generation of compliance that reduces costs and risk simultaneously.
Both government and industry are beginning to explore new applications of technology. A second needed change, however, may require government action — namely, permitting more fluid sharing of laundering information. While the system allows some collaboration between banks and with law enforcement about suspected launderers, most information is siloed and moves slowly through linear reporting channels. This means it trails far behind both the rapid flow of laundered funds and fast-evolving concealment methods as criminals invent new ways around the industry’s defenses. Moving beyond manual reporting to system-to-system communication would enable rapid blocking of new kinds of attacks by both industry and the government, making life much harder for criminals.
Human trafficking is not the only crime that should be fought in this way. The same methods can also help address other intractable problems, including terrorism and the opioid crisis.
Current regulatory systems were designed for the past, an analog era of paper-based processes and limited, costly data and computing power. Today a new “digitally-native” model can leverage the cheap, abundant data and computing power of the digital age.
That shift should start with attacking money laundering, which is already data-rich and which causes so much suffering every day. Reform can sharply cut costs and compliance risks for banks and, most importantly, can wring the profit out of the crimes. Traffickers won’t buy and sell human beings if they can’t make money and will probably get caught. We need to fight these modern, tech-savvy criminals with modern tools.