Data analytics better explains trends in data and supports policy formulation. Human trafficking is an international crime that thrives in the shadows. By taking advantage of big data and analytics¹, data scientists and researchers understand the problem and thereby help law enforcement and victims of child trafficking. Child trafficking is a hugely profitable industry, generating billions of dollars annually in illegal profits. At the same time, it is one of the largest sources of profit for global organized crime, second only to illicit drugs.
The mathematical search for insights in data, could help law enforcement combat human trafficking. Child trafficking is essentially a supply chain in which the supply moves through a network to meet demand. Traffickers leave a data trail despite their efforts to operate off the grid and in the shadows.
There is an opportunity despite a challenging one to use information on the distribution of victims, traffickers and disrupt the supply chain. Trafficking often begins with fraudulent recruitment methods, such as promises of employment or romance. Data² can help identify specific economically depressed areas, where we can deploy awareness campaigns and social service support.
Scientists apply mathematical methods to answer complex questions about patterns in #data and predict future 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.
Trafficking networks are dynamic. Traffickers³ are likely to frequently change distribution and transportation routes to avoid detection, leaving law enforcement and analysts with incomplete information as they attempt to identify and dismantle trafficking networks.
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 media⁴ and internet-based sites. #Analytics could seek patterns in photos through facial recognition software, comparing images from missing person reports or trafficking ads.
Machine Learning and Data Analytics in Action
Machine learning can be used to detect online trafficking activity. Recent advances in matrix completion could even help clean up falsified information or make predictions about missing data.
Traffickers are also known to take advantage of increased demand for commercial sexual exploitation⁵ during major events, including conventions and large sporting events. Analyses that look at both location and timing of online ads could help law enforcement detect and possibly interdict transportation of victims to the event. They could also suggest when and where policymakers should focus intervention efforts.
Data allows us to identify trafficking hotspots and patterns, which are then shared with stakeholders that have been flagged as having links to trafficking-related activities. For example, if our findings identify a particular airline route with a high rate of child trafficking victims, the airline can respond along that specific route right away, and immediate measures can be taken to engage law enforcement before the victim reaches the designated destination.
Data has shown that handbags and smartphones are often ‘gifted’ to those being recruited in the hopes of winning trust. By using this information and setting up geo-targeted campaigns, as well as setting up a search tool to identify when those keywords are used in digital communications⁶, it is easier for local and international law enforcement to identify who is engaging in this kind of interactions.
Of course, as with anything that involves information gathering and potential privacy breaches, there will be much opposition particularly from those who are invested in shielding their private activities and digital interactions. For businesses, in particular, reputation management will be a major concern, and for individuals, the right to personal privacy. Together with the power that comes with access to such data comes great responsibility, and it is hoped by all that access to this type of #technology⁷ continues to be leveraged by those fighting for change for those too vulnerable to be able to do so themselves.
Banks using Analytics and Tracking Offenders
Banks can be alerted when suspicious activity is flagged up by the data. On their own, financial institutions struggle to identify and disrupt trafficking-related transactions because their data models⁸ cannot distinguish money-laundering transactions from trafficking ones. Fortunately, together with data sharing, this all becomes possible. Financial data can be combined with existing NGO and #opensourcedata to identify specific signs of #childtrafficking and the risk level of particular transactions and accounts.
With these results, banks can now validate and improve their #machinelearning modelsand educate staff to better identify trafficking-related transactions. They can also train front line staff to identify trafficking brokers, which can ultimately freeze financial flows of all the intermediaries involved in the trafficking supply chain.
Child Trafficking Organizations using Data
The first organization is Polaris, the Washington D.C. non-profit group that runs the National Human Trafficking Resource Center Hotline. The Polaris analysts use software from to actually conduct the network analyses. Palantir software is used by many of the top law enforcement organizations around the world, and is regarded as extremely powerful. It also has strict formatting requirements for input data.
The company utilizes public data sources, such as business licenses recorded at the state and local levels, to find the beneficial owners of illicit massage businesses. Commercial sex websites also provide valuable data, such as phone numbers and email addresses, to feed into the network.
The second organization using analytics to combat child trafficking is Stop the Traffik, built on the human-trafficking prevention model that relies on data-sharing between numerous sources, particularly NGOs and financial institutions, alongside crowdsourced and open-sourced data. Stop the Traffik utilizes applications on their platform, which allow people to submit suspicious activity anonymously and securely.
Recommendations for preventing Child Trafficking
The government and the private sector should use data-driven approaches that first assess vulnerabilities for human trafficking at all levels and then implement research-informed prevention strategies. Here are some recommendations that assist in the fight against child trafficking:
-Encourage human trafficking task forces to place greater emphasis on primary prevention.
-Facilitate primary prevention efforts by supporting community stakeholders’ collaborative use of data and corresponding approaches for addressing known risk factors.
-Promote rigorous evaluation of existing prevention programs through research grants and evaluation requirements for programmatic grant funding.
Data Analytics to Uncover Criminal Networks
The scourge of human trafficking exists in every major city in the United States, where it ruins the lives of victims while enriching the traffickers. Local police often are ill equipped to deal with sophisticated trafficking schemes, resulting in solicitation charges being filed against the victims while the real perpetrators go free. Thanks to the power of big data analytics, law enforcement has new tools to build criminal cases against the traffickers themselves.
The problem is even bigger on the global stage, with about 4.5 million people trapped by traffickers in 2012, according to the International Labor Organization. In many cases, women or girls are lured from their home country to Europe or the US with the promise of good jobs, only to be trapped into sex slavery. This is precisely why we need to harness data for good because through data sharing, we can work to eradicate one of the most longstanding, evil and unjust social issues of our time.