AI Enables Banks to Identify and Prevent Money Laundering While Surpassing Regulatory Demands

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In this special guest feature, David McLaughlin, CEO and Founder of QuantaVerse, discusses how advancements in data science, including artificial intelligence (AI), machine learning and big data, promise to stifle money laundering and change outcomes for victims around the globe. Financial institutions have begun working smarter through the use of AI and machine learning to help banks dramatically improve the efficiency and effectiveness of money laundering investigations. David McLaughlin is CEO and founder of QuantaVerse, an innovator of data science and artificial intelligence (AI) solutions purpose-built for identifying financial crimes. David spent six years as a naval officer, starting in 1986 as an Ensign in the U.S. Navy and attending flight school in Pensacola, FL. He is a graduate from the highly regarded TOPGUN program, and completed a combat tour in the Persian Gulf where he was awarded the Distinguished Flying Cross and two Air Medals for bravery in combat. Prior to founding QuantaVerse, David held senior executive positions with IPR International, NES Financial and SEI.

Today’s financial institutions are grappling with high volumes of customer, transactional and other internal data sets. If intelligently analyzed, these vast stores of data can prove invaluable to institutions by helping them uncover financial crimes risk.

Across the financial services industry, there are substantial efforts to identify and exclude criminals who launder money garnered through various unlawful activities including illegal drug sales, human trafficking, fraud, and even terrorism.  The laundered or “clean” funds are then used to re-fuel the world’s crime economy, supporting the lavish lifestyles and future crimes of those responsible for destroying the lives of millions.

As part of the Bank Secrecy Act (BSA), financial institutions are required to establish and maintain effective anti-money laundering (AML) programs to detect and prevent money laundering activity while assisting U.S. government agencies in carrying out financial crime investigations.  The Financial Crimes Enforcement Network, or FinCEN,  requires financial institutions, in a timely manner, to file suspicious activity reports (SAR) for suspected money laundering transactions. An institution’s failure to do so may subject them to enforcement actions and/or significant fines totaling millions of dollars.

In response to regulatory scrutiny, financial institutions have spent billions of dollars on technology and personnel to impede criminals from laundering their illicit proceeds through the global banking system. According to a report published by WealthInsight, global AML spending will exceed $8 billion in 2017 alone. Despite these increases in compliance spend, the threat of money laundering has not decreased. The United Nations Office on Drugs and Crime (UNODC) estimates that the amount of money laundered globally in one year is two to five percent of the global GDP, or $800 billion to $2 trillion in U.S. dollars.

Many financial institutions find themselves deficient in the domain of information technology and data analytic skills which are necessary to keep up with consistently evolving mandates and expectations, let alone the increasingly sophisticated criminals they are designed to curtail.  Financial institutions have long used rules-based, legacy transaction monitoring systems (TMS) to detect and report transactions indicative of money laundering activity. The problem with TMS is that they are allowing an unacceptable number of suspicious transactions, or false negatives, to go unidentified, which enable sophisticated financial criminals to continue to operate their illicit money laundering schemes. Additionally, as much as 95 percent of alerts generated by TMS are false positives, or transactions that seem suspicious but aren’t, triggering time-consuming and expensive investigations. Therefore, simply increasing the number of AML investigators within banks is not sufficient enough to solve the problem.

However, leveraging new advancements in data science, including artificial intelligence (AI), machine learning and big data management, offers the promise to choke off money laundering while creating operational efficiencies and improving how these crimes are detected. Today’s AI systems are capable of analyzing large volumes of transactional and other data sources, and providing consolidated case files to human investigators for more accurate SAR-filing determinations.

Some AI and machine learning techniques that are employed to identify transactional anomalies worthy of further investigation include:

  • Collaborative filtering: capable of finding transactions with missing, matching and/or odd information
  • Feature matching: utilized to identify transactions below a specific monetary threshold
  • Fuzzy logic: used to find data matches with slight changes to names or addresses
  • Cluster analysis: can detect abnormalities in transactions benefiting a single person or entity
  • Time series analysis: detects transactions benefiting a person or entity over an extended period
  • Focused keyword searches: ability to dynamically monitor, screen and filter transactions based on keywords from high-risk AML, CTF (Counter-Terrorist Financing) and financial crimes typologies
  • Ability to learn from an AI-identified suspicious activity to enhance transaction monitoring and KYC (Know Your Customer) platforms

Modern financial criminals are determined and well-versed at probing financial institutions’ weaknesses and defenses. The application of AI and machine learning into the AML landscape is essential to assisting financial institutions’ efforts in reducing regulatory risk, identifying potential criminality, and improving the investigation process. With trillions of dollars in laundered money funneling through the global banking system, AI technology is an intuitive and logical solution to solving the problems posed by AML risk for financial institutions.


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