AI Spotlight: FICO’s Machine Learning Tool Facilitates Anti-Money Laundering Detection

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Walking into Bernie Madoff’s home in 2005, you would not have found piles of money under a mattress, behind a sofa or in his garage. At the time, Madoff had been running an elaborate Ponzi scheme through the wealth management arm of his business that reached $65 million by the time of his arrest in 2008, deliberately hiding the money intricately within the financial system. Serving as Madoff’s primary bank for over two decades, JP Morgan was one of the culprits of Madoff’s fraudulent actions and money-laundering tactics. In their innocent incompetence to identify clear red flags about Madoff’s returns and file a Suspicious Activity Report (SAR), JP Morgan’s was fined $1.7 billion in 2014. JP Morgan’s fine highlights the broader problem that many global banks had been facing, which was ignoring the warning signings of fraud and money laundering. Increasingly in today’s age, terrorist organizations and dangerous criminals finance their operations by laundering money in global financial institutions, presenting a huge public policy problem for regulators and policymakers.

In our artificial intelligence (AI) spotlight this week, we highlight FICO’s AML Threat Score tool, which uses AI to help financial compliance analysts detect money laundering or terrorist financing activities. This tool demonstrates AI’s transformative benefits in anti-money laundering (AML) and fraud detection. In doing so, FICO’s machine learning tool also facilitates stronger criminal justice enforcement and enhances national security by identifying the financing activities of terrorist groups and dangerous criminals. Moreover, the tool not only required human input and knowledge for its development, but also requires human interpretation to determine whether the problem identified by the tool presents a case for money laundering and need for intervention.

Innovating AML tools has increasingly become a priority for banks and financial institutions. Regulations to detect and report suspicious activity through SARs have become more strictly enforced. Additionally, with the rise of enormous piles of data, it is very difficult for analysts to sift through the abundance of information. As more cases become flagged for suspicious activity, so too do the number of false positives within the outputted data. Another problem is that, in most instances, money laundering cases deal with multiple interactions or accounts while traditional AML tools flag individual cases, making it incredibly cumbersome for compliance analysts to connect individual interactions or accounts to broader money-laundering threats. As a result, financial institutions have sought to create more productive mechanisms to help compliance employees sift through enormous piles of data and more efficiently report suspicious activity to regulators.

Specifically, financial institutions have turned to tools like FICO’s AML Threat Score, which incorporates machine learning to generate its AML tool. Machine learning is an application of AI that gives machines access to data so that the machine, or tool in this case, can learn for itself. As FICO’s Scott Zoldi highlights in a blog about AML and machine learning, “[FICO’s] AML Threat Score prioritizes investigation queues for SARs, leveraging behavioral analytics capabilities from Falcon Fraud Manager. It uses transaction profiling technology, customer behavior sorted lists (BList), and self-calibrating models that adapt to changing dynamics in the banking environment.” With a threat score ranging from 1-999, compliance analysts are able to identify customers whose transactions have a high likelihood of suspicious activity quicker and more accurately. Then, analysts can send in SAR reports to regulators to ensure that they don’t run into the same problems JP Morgan faced in 2014. By sending in more accurate and timely reports, financial institutions don’t just avoid fines, but also help law enforcement identify criminals or terrorist-financing linked with specific bank accounts or transactions.

Just as we highlighted in our previous spotlights on AI, the AML Threat Score does not displace human work, but rather functions as a result of human input and requires human expertise to interpret whether or not the problem flagged requires an SAR or more investigation. Without human expertise to verify and decipher real money laundering threats, the tool would generate more false positives, feed these back into the system and use this faulty data to regenerate more false positives; this creates an inefficient and unsustainable false-positive feedback loop. In this sense, machine learning tools like FICO’s AML Threat Score not only contribute to a greater social well-being by facilitating more accurate and efficient AML tools, but also help to supplement human work and expertise.

In SIIA’s Issue Brief on Artificial Intelligence and The Future of Work, we emphasize how AI is a natural outgrowth of the developments in computer technology like changes in data size, memory and processing speeds. Thus, these developments will lead to innovations not just in niche markets like AML, but they will also have the ability to drive innovation in education, health care, transportation, speech recognition, and many other markets. Additionally, though some low-skilled jobs characterized by manual tasks may be replaced by AI, more job opportunities in patient care, construction, and high-skilled technical work are all also natural outgrowths of innovations in AI. The transformative benefits brought by AI in many aspects of daily life will continue to become more apparent and universal as we move towards a future defined by technological innovation in AI.

Niko Nikola Marcich is an intern with the SIIA Policy team. He is currently an undergraduate student at the University of Virginia studying foreign affairs and international economics.