Sarah Runge, Global Head of Financial Crime Compliance Regulatory Strategy for Credit Suisse, joins us on this week’s episode of FRT to discuss the benefits and challenges of applying Machine Learning in Anti-Money Laundering and Countering Terrorism Financing (AML/CTF).
Sarah highlights the potential that enhanced analytics hold to strengthen the defense mechanisms against financial crime. Today’s framework and practices lead to inefficiencies that can make one lose sight of the bigger picture. However, we also explore how technology cannot (and should not) replace the human element and vigilance in a financial institution’s safeguarding measures. It should be seen as a way to empower analysts and focus their resources on the cases that need their attention the most.
We also dive into the challenges institutions face when seeking to implement these techniques. The first is data integration challenges in legacy systems, especially for firms operating in a global environment, which are key to any successful initiative on financial crime prevention. The main challenge for global financial institutions, however, is that of data sharing. Varying rules across jurisdictions can lead to significant risks for the firm and the integrity of the financial system. Lastly, we also discuss how the implementation and execution of the global framework can be an opportunity to tackle these issues.
We thank Sarah for agreeing to join us and share her expertise on this important topic, which has been one of the focus areas of the IIF’s work.