Exprentis develops risk monitoring framework for major firm

Fairfax, Virginia, April 2015 – Exprentis Inc., a Virginia-based provider of compliance data analytics and knowledge engineering services, recently helped to develop a risk model for a suspicious activity monitoring system for a global financial services firm.

The firm, which serves clients in more than 50 countries, hired Exprentis to design a comprehensive customer AML risk model evaluation to improve their understanding of customers and their transactional activities to better comply with anti-money laundering regulations and rules. Exprentis helped streamline the firm’s AML risk information flows and designed its AML risk data standardization, risk derivation methodologies, and automated risk monitoring models. The new design, along with subsequent development, will enable the client to more effectively monitor customer risk on an ongoing, enterprise-wide basis.

“Working closely with the client’s business and IT groups, we identified necessary data, processes and rules that profoundly improved an AML risk assessment that spans business lines and a variety of customer bases,” said Tom Dybala, Exprentis’ President. “Improving the quality of risk assessments on various levels of AML monitoring operations  is always our key objective and I am pleased that we achieved it so well in this case.”

About Exprentis, Inc.
Exprentis is a software solutions firm providing products and services targeted to the regulatory compliance and risk management needs of financial services firms. Exprentis’ services include compliance data analytics, development of analytical detection models, model optimization and validation, setup and enhancements of model risk management frameworks, and technology research and feasibility studies. Exprentis’ products include regulatory knowledge-based products that help to automate compliance processes, enhance workflows, and ensure consistency of alert and case investigations and reporting. For more information go to www.exprentis.com.