As firms’ use of AI and machine learning continues to grow, the practical considerations and challenges for effective implementation of data analytics strategies need to be discussed.
According to the Association of Certified Fraud Examiners, the use of AI and machine learning as part of an organization’s anti-fraud program is expected to almost triple over the next two years. These trends are largely driven by the exponential growth of data generated within an organization, an ever-expanding list of responsibilities for corporate compliance and legal officers, and – most importantly – the expectation of regulators that effective compliance programs must incorporate elements of technology. Yet as firms’ use of AI and machine learning continues to grow, there are a number of implementation challenges that may arise.
To help guide a smooth and effective implementation of a compliance analytics strategy, FRA Director Mason Pan, and Associate Director William Mui, delve into the important practical considerations that should be addressed at the forefront to avoid the most common challenges stakeholders face.