Human trafficking is devastating for victims, but typically low-risk for the criminals, whose activities are largely hidden from view. To disrupt human trafficking, law enforcement is partnering with NGOs, financial institutions and forward-thinking technology providers (like QuantaVerse) that offer new artificial intelligence and machine learning solutions.
Per the Trafficking Victims Protection Act, human trafficking is defined as:
- Sex trafficking in which a commercial sex act is induced by force, fraud, or coercion, or in which the person induced to perform such an act has not attained 18 years of age; or
- The recruitment, harboring, transportation, provision, or obtaining of a person for labor or services, through the use of force, fraud, or coercion for the purpose of subjection to involuntary servitude, peonage, debt bondage, or slavery.
Human trafficking is a multi-billion-dollar industry that destroys families and communities affecting tens of millions worldwide. Yet in 2017 there were fewer than 10,000 worldwide convictions of human traffickers according to the U.S. Department of State’s 2017 Trafficking in Persons Report. As criminals have become more sophisticated, it’s imperative that law enforcement and financial institutions adopt new and evolving technologies such as AI to help identify suspicious transactions indicative of human trafficking red flags.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?