Below are some use cases that DataFrame proposes for the robust and defensible complaince framework In today's interconnected and fast-paced financial landscape, the need for robust Fraud and Anti-Money Laundering (AML) solutions has never been more critical. Fraud and AML use cases are integral to safeguarding financial institutions, businesses, and consumers against illicit activities that threaten the integrity of the global financial system. These use cases involve the deployment of advanced technologies and data-driven strategies to identify, prevent, and mitigate fraudulent activities and money laundering risks.
1
ML/TF Typology & Policy Review
- Extensive library of 200+ AML and Fraud red flags.
- Comprehensive mapping with local guidelines and regulations (covered across many jurisdiction)
- Detail gap analysis of the bank’s policies and Local regulations.
2
Fraud and AML Risk Assessment
- Predictive analytics for a 360-degree view of the client and its related parties.
- Re-use existing due diligence and consistent treatment across clients.
- Develop a Client risk assessment model, FCRA, the country's National Risk assessment
3
Fraud and AML Alerts Optimization
- Data science and machine learning can review the detection results of rule-based monitoring systems and propose the optimal parameters.
- Easily simulate true positive and false positive rate changes within the production scenarios.
- AI model to risk score alerts and detect ML patterns.
4
Sanctions Applications
- Develop Machine Learning models to detect Name screening and specific typology i.e. Payment striping.
- Review of the application of the sanctions with the use of statistical model benchmarking.