The landscape of drug trafficking has drastically changed over the past two decades. Illicit drugs such as cocaine, methamphetamines, designer drugs, controlled prescription drugs, fentanyl and heroin continue to be trafficked throughout the world. Transnational Criminal Organizations (TCO) continue to control distribution networks into the United States; however, terrorist groups are now using drug trafficking as a funding mechanism. As partners in the fight against drug trafficking, financial institutions (FI) and FinTech companies providing innovative artificial intelligence (AI) and machine learning (ML) solutions can have a bigger impact against the money laundering cells used to move the traffickers’ and terrorists’ billions of dollars around the world.
According to the U.S. Drug Enforcement Administration’s (DEA) 2016 National Drug Threat Assessment (NDTA), drug poisoning/overdose deaths are currently at their highest reported level. The NDTA indicated that every year since 2009, drug overdose deaths have surpassed reported firearms, car crashes, suicides, and homicides combined. Amazingly, the NDTA reported that in 2014, approximately 129 people died daily from drug overdoses.
Terrorism groups such as Hezbollah use drug trafficking as a source to fund their and other groups’ global terrorism activities. This is exemplified by the March 2017 arrest and extradition of Kassim Tajideen, who the Department of Justice (DOJ) describes as a prominent financial supporter of the Hezbollah terror organization. Tajideen was charged with evading US Sanctions and money laundering through his managerial role in Hezbollah’s Business Affairs Component (BAC), which is the logistics, procurement and financing arm for Hezbollah. The indictment alleged that after being designated a terrorist and placed on sanctions lists, Tajideen and his associates created a web of vertical shell structures to bypass sanctions detection by U.S. companies and financial institutions. The indictment alleged that Tajideen’s group caused approximately $27 million in wire transfers to be sent to the US in exchange for goods from US vendors. Tajideen’s arrest is a testament to the government’s commitment to follow the money to dismantle TCOs and terrorist organizations.
How can FIs do more to identify and prevent drug and terrorism money laundering? The answer is not to file more Suspicious Activity Reports (SARs) and hire more investigators and analysts. The answer is to file better SARs that identify the highest risk activity. FIs spend an inordinate amount of time filing potentially defensive SARs on correspondent banking, commercial, and retail transactions due to personnel constraints, regulatory deadlines and transaction monitoring alert volumes. If FIs implemented an AI and ML component to enhance their compliance programs, FIs could peel back the onion of money laundering efficiently. Moving away from sole reliance on scenario-driven transaction monitoring, FIs could use AI to identify entity relationships, predict future suspicious activity, and provide better SAR reporting to law enforcement.
FIs are the government’s frontline defenders and reporting mechanism. AI is the emerging as the preferred tool to assist FIs in fulfilling their responsibilities. A properly installed AI solution can enhance existing transaction monitoring, KYC, sanctions name screening, and investigative platforms to provide an insight of what FIs should be reporting. AI can solve the problem and drive efficiency for FIs. The proper identification of drug-related money laundering typologies and red flags such as funnel accounts and shell companies are identifiable today through an AI-enhanced AML program.
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.read more
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.read more
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?read more