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
Use risk-based (versus rules-based) transaction monitoring analysis to improve consumer account transaction monitoring analytics.
Advanced analytics such as machine learning, natural language processing.
Use Case Summary
Firms use advanced analytics to improve their AML transaction monitoring programs. Combining more detailed KYC/CDD profiles with risk-based transaction monitoring reduces false positives and improves the efficacy and efficiency of transaction monitoring programs for compliance and regulatory reporting.