Traditional rule-based monitoring methods are ill-equipped to detect increasingly sophisticated money laundering schemes which leaves avenues for organized money launderers to slip through the cracks. As a result, financial institutions and government agencies struggle with detecting complex money laundering networks and divert too many resources to the manual processing of false positives and time consuming, cumbersome investigations. The combination of graph analytics and AI offers unique opportunities to more effectively detect false negatives, reduce false positives and accelerate AML investigations.