Machine learning has had a dramatic effect on anti-money laundering (AML) risk management. Its automated analysis and risk-mitigating algorithms have strengthened AML programs overall and made them more efficient than ever. However, it has presented unique challenges in the form of systemic vulnerabilities such as algorithmic biases. This means an algorithm may de-risk or deny a customer banking service based on one element of their risk profile such as being domiciled high-risk jurisdiction for example. Conversely, a biased algorithm could turn an automated blind eye toward genuine AML risks.

This webinar will dive into algorithmic bias, how it affects the global financial system and how to avoid and remedy problems it can cause.