July 31, 2017
Human trafficking occurs in every country in the world, including the U.S. It’s a hugely profitable industry, generating an estimated US$150 billion annually in illegal profits per year. In fact, it’s one of the largest sources of profit for global organized crime, second only to illicit drugs.
Analytics, the mathematical search for insights in data, could help law enforcement combat human trafficking. Human trafficking is essentially a supply chain in which the “supply” (human victims) moves through a network to meet “demand” (for cheap, vulnerable and illegal labor). Traffickers leave a data trail, however faint or broken, despite their efforts to operate off the grid and in the shadows.
There is an opportunity – albeit a challenging one – to use the bits of information we can get on the distribution of victims, traffickers, buyers and exploiters, and disrupt the supply chain wherever and however we can. In our latest study, we have detailed how this might work. We can use data to identify populations most at-risk and target prevention campaigns to those populations. Risk factors for being drawn into trafficking include poverty, unemployment, migration and escape from political conflict or war. Experiences with organized crime and natural disasters can also change to a person’s risk.
Trafficking often begins with fraudulent recruitment methods, such as promises of employment or romance. Data can help identify specific economically depressed areas, where we can deploy awareness campaigns and social service support.
In operations research, scientists apply mathematical methods to answer complex questions about patterns in data and predict future trends or behaviors. Analytical tools similar to those used in transportation, manufacturing and finance can help us decide where to best allocate resources and help locate shelters for victims.
Researchers can help by tracking subtle trends in data at various locations; at access points where we actually come in contact with victims, such as the emergency room; and in the activity of local law enforcement.
In the sex trade, for example, clues may be found in patterns of petty theft, by looking at transactional data from purchases at retail outlets. Victims sometimes steal essential supplies that traffickers may not provide for them such as feminine hygiene products, soap and toothpaste. Trends in the use of cash for transactions normally made with debit or credit cards – hotel bookings, for example – may also raise a red flag.
Traffickers advertise on social media and internet-based sites. Analytics could seek patterns in photos through facial recognition software, comparing images from missing person reports or trafficking ads.
Sex trafficking activity, in particular, leaves traces in the public areas of the internet, mostly in the form of advertisements and escort ads. Advertisers tend to use social networks and dating websites, while more proficient traffickers frequently alter their online presence to try to elude identification.