AMLOCK Analytics uses various statistical methods and machine learning algorithms to derive analyses and predictions based on institution specific historical data.
3i Infotech has launched AMLOCK Analytics, its advanced anti-money laundering (AML) solution powered by AI and ML, which enables banks and financial institutions to identify complex and hidden AML patterns. It helps organizations to meet their most critical challenge of managing high false positives and provides a holistic view of investigating an alert. AMLOCK Analytics uses various statistical methods and machine learning algorithms to derive analyses and predictions based on institution specific historical data.
One of the important features of 3i Infotech’s AMLOCK Analytics is the reduction of false positives using risk profiling, through predictive analytics that identifies potential risk and thereby enhances decision making. The solution also provides an insight on the trends followed by customers, based on seasonality and identifies the anomalies based on deviation from these trends, where machine learning helps in customer segmentation. This enables users to investigate effectively by working closely on those groups which are risky or deemed outliers.
Ravikanth Sama, Global Head- AML Practice, 3i Infotech said, “AMLOCK Analytics blends both the traditional rule-based system and the power of Analytics to bring better efficiency & risk focus. It can be hosted both on-premise and on cloud infrastructure. The solution provides a probability score indicating the chances of closing an alert based on the past actions taken by the users on similar alerts. AMLOCK Analytics improves the conversion rate of Suspicious Transaction Report (STR) or Suspicious Activity Report (SAR), as it dynamically correlates between the alerts in which suspicious transaction reports have been generated and those that have been tagged as false positives by the investigators.”
