Can Analytics Curb the Human Trafficking Menace?

The International Labour Organization points that human trafficking is a growing menace, with an estimated 40.3 million people enslaved worldwide. Over the years, these human traffickers have raked in $150 billion as profits from forced employment in domestic, agricultural, industrial and prostitution.

The US State Department estimates that as many as 800,000 people are trafficked across international borders every year with about half of those being children. These individuals, about 80 percent female, are forced into prostitution or other involuntary servitude.

As many as 17,500 people are trafficked into the US each year, with California and Texas serving as hotbeds of activity. Industrialized countries account for nearly half of the illegal money made each year through human trafficking. Human Trafficking is not just the problem of one industrialised country, but one that is prevalent world-wide.

Government organizations, law enforcement agencies, healthcare providers and private companies around the world continue to fight this scourge. Despite these efforts, though, human trafficking has remained the third-largest criminal industry in the world, trailing only illegal drugs and arms trafficking.

How Can Text Analytics Aid to Solve the Menace?

Analytics, machine learning, and artificial intelligence have already shown their merit in helping organizations address social issues. Analytics has been used to combat the opioid epidemic, improve high school graduation rates, fight fraud, and many other areas of concern.

Text analytics has already been a success factor when it comes to tracking human trafficking. As its name implies, text analytics is the process of taking data from written documents and turning it into larger insights.

For example, the State Department publishes more than 200 reports each year about human trafficking in various countries. Organizations can use text analytics to look through these reports to find patterns. Earlier, organisations relied on reading manual reports to find insights. Not only is this process time-consuming, but it relies entirely on human analysts who may miss some possible connections or insights.

By combining text analytics with machine learning and artificial intelligence, as well as insight and overview by analysts, organizations can gain deeper understanding of the subject. They can use visualization technologies to better find connections or to more clearly understand trends and use that information to help make decisions.

Technology Extends a Supportive Hand

To curb this organised crime, law enforcement agencies, non-profits, and financial institutions are working to prevent human trafficking via the Traffik Analysis Hub, which aims to understand the human trafficking nexus by understanding the flow of money.

The Traffik Analysis Hub deploys IBM i2 software which leverages machine learning and augmented intelligence to recognize terms and incidents related to exchanges between the human trafficking groups.

The Traffik Analysis Hub is hosted by IBM’s cloud services and is accessible to coalition members. The coalition members include anti-slavery group STOP THE TRAFFIK, IBM, telecommunications company Liberty Global, British banks Barclays and Lloyds, Europol, financial services company Western Union, and the University College London.

Looking Forward

The goal for organizations involved in the fight against human trafficking should be to develop an enterprise framework that supports analytics. This framework would:

• Improve the accuracy of their current programs using strategic data and visualizations. This will provide stakeholders with a more complete picture of what is happening, improving strategic decisions.

• Improve specific responses to certain situations. For example, law enforcement officials in the field could have better guidelines for shared characteristics of human trafficking cases.

• Increase the number of data sources without the need to add additional analytical operations.

• Envision a system where state and local law enforcement can share information with national security agencies, hospitals, departments of motor vehicles or any other organization that can link individuals to draw connections and anomalies.

Concluding Remarks

Human trafficking remains one of the world’s largest humanitarian problems. The organizations trying to stop it need all tools available to fight this growing organised crime.

Like most issues today, human trafficking is made up of many components. There is no silver bullet to magically make the problem disappear. It takes a multi-pronged approach and requires those involved to be able to truly look at the problem, but also be flexible to changing conditions to alter approaches as needed.

That is where advanced analytics, machine learning, and artificial intelligence can begin to make a real difference.