More and more, regulators are examining AML and terrorist financing monitoring software solutions to see if they are tuned correctly; and citing financial institutions who fail to meet the regulatory standards. One issue for both regulators and these institutions is the creation of excessive volumes of “false positive” activity alerts, or alerts on activity that after evaluation, is not determined to be suspicious.

Every anti-money laundering (AML) program should be reviewed periodically to confirm that the program is performing efficiently and effectively. Analytics can play a big role in this review by providing new insights that support evidence-based decision-making.

When it comes to anti-money laundering (AML) transaction monitoring, financial services firms are under more pressure than ever to prove that the approach they are taking is working. Regulators want to see obvious evidence that firms are generating the right level of suspicious activity reports (SARs) for their size, geography, and business types, usually in the form of statistics and reporting. In turn, boards and senior management teams are now demanding to see this same information to be sure the firm is meeting its compliance obligations. As a result, AML transaction monitoring analytics are in more demand than ever before.

The Three Lines of Defense Model has gained popularity as the de facto model for organizing governance, risk management and internal control roles and responsibilities since the Institute of Internal Auditors (IIA) published “The Three Lines of Defense in Effective Risk Management and Control,” position paper in 2013. The IIA recently announced that they would embark on a key project to refresh and update this document.

Technology is an essential weapon for financial services firms in the battle against anti-money laundering (AML). The Office of the Comptroller of the Currency (OCC) issued a joint statement with other regulators earlier this month encouraging financial institutions to try new and innovative ways of combating AML. Three types of technology – advanced analytics, software robots, and artificial intelligence (AI) – could help make it easier to detect and prevent money laundering, as well as comply with existing regulations and follow the guidance of this joint statement.