Revealing New Players in a Narcotics Distribution Chain

March 2019

How can an AI-driven analysis of billions of data points enhance and expedite a narcotics investigation?

A routine traffic stop in a Midwestern state led to the discovery of 18 pounds of heroin. The driver refused to speak to the police.  A large police department was left with a dilemma – chasing down all the possible directions this investigation could lead would require significant time and effort.  How could they efficiently understand where the drugs came from and who was behind the operation?

Working the case the old-fashioned way

Despite the lack of cooperation, working the case by traditional methods, police coaxed the driver into revealing who he was delivering the package to and the drug’s point of origin. After several hours of investigation, they discovered that the information he provided was false, and hours had been spent chasing the wrong trail. They also discovered that the intended recipient of the package is one of several dozen living in the city who share the same name. A second round of interrogation yielded a new supplier name which was then investigated separately and was eventually verified.  The issue of recipient was then revisited.  Eventually the driver was convicted but no further arrests were made.

But for each time a narcotics distribution channel is disrupted, one or more new channels spring up to take their place. There will surely be a new shipment, a new driver. From a drug supplier’s perspective, interdiction by police is a nuisance, a cost of doing business – a cost which in any case is often absorbed by the customers.  

Advanced technology which augments the investigative process can change the equation

Let’s look again at the case of our heroin-toting driver. What if, instead of spending hours, days and weeks tracking down leads legitimate or false and then convicting a minor player, law enforcement officials could, in near real-time, distill millions of possibilities to reveal the most promising leads in relation to any case – automatically, and based on complex AI-driven algorithms crunching real-time data at scale?

What if, in addition to this network, law enforcement could unearth individuals who were not part of the drug trafficking network in a classic sense, but nonetheless facilitated the trafficking, even if they lived in a range of jurisdictions or even countries? These mediators may have purposely kept a low profile and had no discernable association with the suspects, but they would nonetheless be automatically highlighted by a technology capable of connecting the dots.

In this case, the driver’s lack of cooperation and attempts at misdirection would be irrelevant. Instead of relying on shaky testimony, law enforcement could quickly analyze the suspect’s claims using a platform that provides immediate, automated deep insights derived from publicly available real-time data. In today’s world, as Manhattan DA Cyrus Vance Jr. noted, “The Internet is the crime scene of the 21st century”. Crimes are not just committed, but also mirrored online; With the right technology, expert investigators could be armed with insights they could not have obtained otherwise, and empowered with the most advanced capabilities to conduct multi-dimensional investigations.

With this ability at their fingertips, investigators can think big and grasp the larger picture. Instead of making one arrest, they can connect the dots, truly understand who the key players are, and even go after the biggest culprits – the heads of criminal organizations.

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