This is a guest post from Nikola Marcich with the Policy team at the Software & Information Industry Association (SIIA), the principal trade association for the software and digital content industry. 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 J