Regtech has already helped banks cut costs by automating many compliance tasks. However, when it comes to anti-money laundering (AML) standards and commitments to comply with Office of Foreign Assets Control (OFAC) sanctions, the issue for banks is an expense and major legal and reputational risk.
FREMONT, CA: Financial technology (fintech) innovations are fast revolutionizing the global banking sector, not only from a consumer standpoint but also in ways that are concealed from view. While fintech startups and established technology corporations try to enhance how consumers engage with the financial system, banks are investing billions of dollars in inventing faster and more effective ways to meet their regulatory compliance duties. In a post-crisis climate marked by large new laws and restrictions on activities and operations, as well as record-breaking enforcement penalty amounts, lowering the cost of regulatory compliance is an appealing way to increase margins.
Regulation technology (regtech) is a subset of fintech that focuses on enhancing the compliance and internal control systems of financial services firms. Regtech solutions, among other things, automate risk management operations, facilitate regulatory reporting, prevent fraud, allow businesses to stay up to date on regulatory developments around the world, and support strategic planning.
AI in Regtech for AML
Supervised learning has effectively built AI for a variety of activities, including chess (learned not by programming in chess rules, but by enabling it to study millions of actual past games) and language translation (trained not by programming grammar rules but by allowing the machine to learn from billions of conversations).
Similarly, technologies are being created to train artificial intelligence (AI) to work within a bank's computer systems to detect everything from fraud and ineffective internal controls to money laundering and terrorist financing.
Regtech has already helped banks cut costs by automating many compliance tasks. However, when it comes to anti-money laundering (AML) standards and commitments to comply with Office of Foreign Assets Control (OFAC) sanctions, the issue for banks is not only an expense but also a major legal and reputational risk. AML exacerbates the difficulty, and OFAC compliance is difficult to achieve with present educational algorithms and rules-based systems.
AML rules-based approaches, for example, might flag cash transactions above a given currency value, limit transactions to specific countries, use customer data to select accounts for extra monitoring, and categorize merchant accounts based on prior transactions. These systems are already in use, but they necessitate a large number of bank resources to evaluate the reported or denied transactions to eliminate false positives. Furthermore, a rules-based AML approach will be unable to respond to changes in criminal behavior meant to avoid detection.
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