High false positives continue to be a primary area of concern for financial firms, which are substantial drain on resources that could be used more productively. Rules-based systems without advanced analytical capabilities are not adequate to identify suspicious activity and meet regulatory requirement and expectations.
Goals and Objectives
Improve efficiency of AML processes to reduce operational costs while more accurately identifying suspicious activity. As a result, compliance and reputational risks are reduced.
AI, machine learning, RPA, predictive analytics
Use Case Summary
Firms leverage AI to drive down false positives and automate elements of the investigative process. Explainable machine learning models are continually tuned by feeding investigative results and external data. Predictive analytics prioritizes case work. Suspicious activity regulatory reporting is highly automated through natural language processing and generation.