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MapR's New Approach to Financial Crimes

By CIOReview | Thursday, July 21, 2016
Jack Norris, Senior VP, Data and Applications, MapR Technologies

Jack Norris, Senior VP, Data and Applications, MapR Technologies

SAN JOSE, CA: MapR Technologies debuts the immediate availability of MapR Risk Management Quick Start Solution for financial services to qualify and manage risk. MapR Technologies, provider of Converged Data Platform—offers a best-in-class risk management solution for companies that increasingly use data science, machine learning, and advanced analytics to identify and quantify risk in order to defend advanced persistent threats.

The firm’s Risk Management Quick Start Solution is a data science-led product and service offering that addresses two main categories of risk: fraud detection and anti-money laundering. The focus on fraud detection uses predictive analytics model that utilizes current and historical facts for assessment of risk involved in financial services and also to detect, identify and avoid loss from fraud and cybercrime. “Our M7 Enterprise Database Edition enables continuous real-time operations and handles high-volume and high-velocity of database workloads with batch analytical tasks,” says Jack Norris, Senior VP, Data and Applications, MapR Technologies.

The other focus is on identifying money laundering using anomaly detection that employs segmenting customers’ normal behaviors and uses statistical techniques to determine deviation of a particular transaction from the corresponding group behavior. This risk-based approach monitors the transaction process to combat the entrance of illegal money into the financial systems.

“MapR data scientists have worked with some of the world’s largest banks, brokerages and fintech companies,” says Dave Jespersen, Vice President of Worldwide Services, MapR Technologies. “With this new Risk Management Quick Start solution, we have created a proven, repeatable and demonstrable approach to identifying fraud and other illicit activities using the scale and reliability of the MapR Converged Data Platform.”