Risk Management for Financial Services

The topics of risk and risk management continue to be a critical subject for banks, lenders, insurers, and many others in the financial services industry. Companies are increasingly using data science, machine learning, and advanced analytics to identify and quantify risk in order to mitigate adverse effects on the business. Once the risks are assessed, strategies and activities can be put in place to mitigate and control the potential financial, productivity, or reputational damage that these risks pose.

The Risk Management Quick Start Solution from MapR is a data science-led product and services offering that addresses two major categories of risk for financial services companies:

  • Fraud detection through predictive analytics
  • Anti-money laundering through anomaly detection

Key business benefits include:

  • A scientific approach to risk: Customers engage with experienced MapR data scientists with financial services industry backgrounds.
  • Customized risk detection: MapR data scientists tailor the data models and algorithms for fraud detection and money laundering based on a collaborative process with a customer’s fraud experts.
  • Precise ROI: A precise demonstration of the incremental monetary value to the business is provided as well as clear identification of suspicious activity, all delivered with minimal business disruption.

What's Included?

software

Software

Trial subscription of MapR Converged Data Platform Enterprise Premier for the duration of the quick-start.

professional services

Professional Services

3-10 weeks of engagement with MapR Professional Services Engineers and Data Scientists (Duration varies based upon the particular quick start.)

certification

Certification

2 Academy Pro Subscriptions including Certification Exams.

Key solution capabilities

The Risk Management Quick Start Solution initially will follow one of two paths: one focuses on fraud detection using predictive analytics, and the other focuses on identifying money laundering using anomaly detection. These two paths are similar in that they use advanced data science techniques, but they use different data models and algorithms.

  • Data science-led discovery: Before any software is installed and any code is written, MapR data scientists collaborate with key stakeholders and identify the nature of the risk (fraud, money laundering anomalies), the relevant data, and the relationships between the data sources and the forecasting of outcomes.
  • Custom feature extraction and machine learning models: MapR data scientists go through multiple iterations of feature extraction and machine learning algorithm training until they are satisfied that significant value has been identified.
  • Demonstrable fraud and anomaly detection: In the case of fraud, data scientists can be fairly precise in building the right scoring model and associate the score with business costs. Therefore, they can provide a monetary value on detecting and preventing fraud. Money laundering is more complex to address, so data scientists instead focus on working with the customer to reach agreement on what suspicious activity looks like. This leaves the customer on the best footing for combating money laundering.

Using this approach, MapR data scientists use the MapR Converged Data Platform and distributed machine learning algorithms to provide an enterprise-grade analytics capability that can easily be refined and modified to respond to changing market conditions, new fraud models, and new data sources for defining and managing risk.

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Joe Blue Data Science for Financial Services

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