Fairfax, Virginia, May 2016 – Exprentis Inc., a Virginia-based provider of compliance data analytics, knowledge engineering services, and cutting edge regulatory compliance technology, has completed a customer risk model project. In a record three months’ timeframe, Exprentis developed a pilot Customer AML Risk Ranking (CARR) tool that evaluates the risk of a new or existing client in compliance with KYC, EDD, and CCD regulatory requirements.

Exprentis also helped a multinational financial services firm to define the CARR methodology for their Private Banking line of business. Development of CARR methodologies and supporting software tools requires consideration of several regulatory, data, analytical modeling, and technology aspects.  One of the first decisions to be made is if CARR is a reflection of a corporate customer AML risk policy, or is it a pure, predictive risk-scoring model, or a combination of both. Each approach has its benefits and drawbacks.

Policy-driven CARRs are usually easier implementations with less need to validate outcomes (since they are not predictive models). They don’t require development of alternate models, since there is no model to start with. However, they are static and do not adjust to statistical changes in behavior or activity on their own. Even for policy driven CARRs, there are still alternative options for definitions of risk ranking methodologies, such as average-based, weight-based, or simply additive algorithms. Sometimes strength is found in simplicity. “We are extremely pleased to be able to help our client to automate and streamline a customer risk assessment process that was previously achieved via completing many manual processes and data transformations in a tedious and risk prone manner,” said Tom Dybala, Exprentis’ Founder and President. The customer model designed and developed by Exprentis includes all risk standards currently practiced by regulatory compliance industry. Our client is now well suited with a regulatory model assessing clients’ customers risk in accordance with the company’s policies and procedures.

 Data-driven CARRs are predictive models that are more dynamic and adjustable toward statistical changes in behavior or activity. However, they require formal validations and comparisons with alternate models to determine the best option in sensitivity, stability and scalability.

“The solution we helped to design and implement was successful, because it was a result of our many years of specialized experience with development, optimization, and validation of AML models, scenarios, and risk assessment methodologies,” said Gaby Anaya, Lead Regulatory Analyst and Project Manager at Exprentis.

You can read more about the Exprentis experience with Customer AML Risk Rating models by requesting our white paper from the Contacts page.

About Exprentis, Inc.
Exprentis is a software solutions firm providing products and services targeted to the regulatory compliance and risk management needs of the financial services industry. Exprentis’ services include compliance data analytics, development of detection models, model optimization and validation, setup and enhancements of model risk management frameworks, and technology research and feasibility studies.  Exprentis has also developed technology solutions that integrate semantic technology and statistical methods to assist organizations with model governance and automated decision making. The Exprentis’ products suite provides cutting edge technological assistance in regulatory knowledge collection and management, enhancing regulatory processes with state of the art semantic knowledge bases, model optimization tools, and big data technology. For more information go to www.exprentis.com.