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  5. Big Data in Risk Management: Tools Providing New Insight

Big Data in Risk Management: Tools Providing New Insight

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5 December 2013
Bill Fearnley

Abstract

Celent estimates spending on Big Data in risk management will grow from $470 million in 2014 to $730 million in 2016 as tools mature and firms deploy more enterprisewide solutions.

Financial firms remain under constant pressure to improve their risk management systems, analysis, and reporting. New open source software tools are helping firms analyze more data types and sources faster. Celent believes Big Data will become an integral part of risk systems and analysis as a complement to existing systems and tools.

In the report Big Data in Risk Management, Celent discusses the benefits of Big Data in risk management and evaluates the potential for Big Data in:

  • Risk assessment and measurement.
  • Front office and risk operations.
  • Risk control and monitoring.
  • Risk reporting and governance.

So why Big Data now? In the recent financial crisis, many market participants and regulators discovered that their data architecture and IT systems could not support monitoring and managing a broad spectrum of risks. Regulators want more frequent reporting of a wide variety of risks and expect firms to be able to respond quickly to ad hoc requests. To meet more timely and detailed management and regulatory requirements, firms are increasingly investing in open source software solutions (e.g., Hadoop and MapReduce) and sophisticated data management tools such as in-memory databases and analytics.

“Firms can gain insights from adding new types and sources of nonfinancial data to risk management models,” says Bill Fearnley, Jr., Senior Analyst with Celent’s Securities & Investments Group and author of the report. “Firms should start with controlled pilot projects that can be scaled for use by other groups and functions within the firm.”

This report examines the benefits of Big Data in major risk domains and provides a detailed discussion evaluating the potential for Big Data in risk management. The report includes nine brief case studies.

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