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.
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Jurisdiction Derivation, Powered by AI, Helps Financial Institutions Reduce Risk and Their Number of AML Investigations
Financial institutions are held accountable by regulators to ensure they are taking a risk-based approach in their AML/BSA compliance operations. As such, institutions must consider AML risk based on certain types of customers and transactions, including risky jurisdictions impacted by political or economic unrest.
The AI-powered QuantaVerse Automated Volume and Value (V&V) Transaction Analysis solution provides risk managers with better insights into variances in account activity that might indicate risks of financial crimes, or that suggest an account is being used for something other than its stated purpose. Analysis of this nature is a growing regulatory burden driven by the expectation that FIs understand the risk profile of clients as well as their clients’ clients.