Harnessing the power of AI to improve recovery for survivors of human trafficking

Worldwide, about 40 million people are victims of human trafficking each year. Kept captive on a farm, forced to work with no pay and little to eat, living in fear of abuse, all while raising an infant—this and other stories of modern-day slavery endure around the globe.

Anti-slavery organizations help rescued women, men, and children overcome trauma and restore their lives. Case workers can help them navigate their legal, financial, and mental-and physical-health challenges, but as many as 40 percent drop out of the programs, disappear, or are recaptured by traffickers.

We partnered with an aid organization that had already helped thousands of survivors restore their lives and had an ambitious vision to scale the number of people it services by 20 times. To achieve this goal, the organization needed deeper insights, which required new systems and case-worker training in data collection and data entry. Our experts worked hand in hand with the organization to harness data and implement sophisticated analytics and machine learning to improve its aftercare program and recovery services.

Our team collaborated with the organization to extract new insights from its more than 10,000 anonymized-survivor support records. The work included digitally analyzing more than 250,000 services offered to the survivors and some 100,000 paragraphs of case-worker notes. McKinsey experts used a combination of machine learning, natural language processing, and journey analytics mapping to understand the drivers of survivors’ recovery and ways to raise the likelihood that they could quickly regain their lives. In addition to interviews and joint working sessions with case workers, the McKinsey team and the aid organization organized a 24-hour insight-gathering session with dozens of data scientists around the world to develop innovative approaches and draw further insights from the data.

Our analysis suggested that having case workers meet in person with survivors and initiate support within 30 days improved the chances of successful outcomes by more than 50 percent. The team also found that survivors’ accounts of the recovery process, documented through natural language processing, could help determine specific events associated with positive or negative sentiments. Furthermore, the research identified risk factors that could indicate a higher likelihood to drop out or take longer to recover, such as age, risk of losing housing, or risk of harm by perpetrators. The survivors’ mental health proved to be one of the most critical factors in determining the successful completion of the aftercare program.

As a result of the work, we helped the aid organization to enhance its survivor aftercare delivery and program, one that will help survivors recover faster and give them the tools to remain healthy and self-sufficient. We also helped to build a foundation for how data and advanced analytics insights could help the organization scale which services will best support survivors’ needs.

The analysis identified specific areas to improve aftercare services – such as making a 30-day contact window standard, using early indicators to flag survivors who may need additional support, monitoring survivor sentiment in case notes, and increasing the focus on mental well-being support services such as training, home visits, and trauma counseling. These insights are currently being implemented in a redesign of the aftercare support service program.