Application of Analytics in Improving Efficiency of Transaction Surveillance

As regulators in key financial markets tighten regulatory controls on money laundering activities, financial crime risk is a key area for most financial institutions. Many have invested heavily in purchasing transaction surveillance systems, and taken a conservative approach to flagging suspicious transactions. Although this ensures that the chance of missing any suspicious transaction is minimized, the downside is that it generates a huge number of false positive alerts. This is extremely time consuming for transaction surveillance investigators to eliminate, causing a delay in the identification of genuine alerts. Due to close monitoring by regulators, institutions are likely to remain conservative and maintain very low detection thresholds. This paper proposes an analytical solution to enhance surveillance efficiency by risk-scoring system generated alerts, so that high risk alerts can be prioritized for investigation.