MapR Debuts New Data Science-Led Quick Start Solution Focused on Risk Management for Financial Services

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MapR Logo - New 2014MapR Technologies, Inc., provider of the Converged Data Platform, announced the immediate availability of the MapR Risk Management Quick Start Solution for Financial Services powered by the MapR Converged Data Platform. With more data available to detect, identify and avoid loss from fraud, cybercrime, or regulatory non-compliance, financial services companies can engage with MapR data scientists and deploy the MapR Converged Data Platform to quantify and manage risk.

MapR data scientists have worked with some of the world’s largest banks, brokerages and fintech companies,” said 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.”

Traditional messaging and processing technologies are insufficient to handle the increasing scale and complexity of risk management and its associated data. With the MapR data science-driven approach to risk management combined with the MapR platform, important vulnerabilities can be identified, modeled and analyzed to detect fraud, money laundering, identity theft, rogue trading and terrorist financing.

Details of the MapR Risk Management Quick Start Solution (QSS) for Financial Services

The MapR Financial Services Risk Management QSS is data science driven and delivers a working data science model and solution and a path forward from a five week consulting engagement. Two QSS paths are offered including:

  • The fraud detection path of the QSS uses predictive analytics models to tackle vulnerabilities beyond fraud. The same data science feature extraction, machine learning modeling work, and creation of transaction fraud thresholds can be applied to other important problems such as identity theft, and insurance claim fraud.
  • The anti-money-laundering path of the QSS uses advanced anomaly detection to identify hard-to-detect risk management challenges. The same approach to identify complex money laundering scenarios can be used to identify rogue trading, terrorist financing, network security monitoring, and complex regulatory compliance issues. This requires more extensive feature extraction.

Both paths include a discovery process with a MapR data scientist and culminate in a demonstration of the precise incremental monetary value to the business of the fraud detection and the clear identification of suspicious and actionable alerts from anti-money-laundering algorithms.


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