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August 19, 2014
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Discover How Financial Services Firms Use Hadoop to Reduce Risk and Accelerate Earnings

Few industries depend as heavily on data as financial services. Insurance companies, retail and investment banks aggregate, price and distribute capital with the aim of increasing their return on assets with an acceptable level of risk.

To do that, financial decision-makers need data. Apache Hadoop helps them store new data sources, then process the larger combined dataset for batch, interactive and real-time analysis. More data and better analysis improves bottom-line results.

Read this Hortonworks white paper on seven common financial services use cases that are generating enthusiasm in the industry.

Download the White Paper

David Gleason, managing director and head of data strategy at BNY Mellon, recently shared his enthusiasm for Hadoop in The Wall Street Journal saying, “It’s the most exciting technology I’ve seen in my career since the advent of relational database management systems.”

Here are three examples of financial services use cases covered in the white paper:

  • Screening New Account Applications. Large retail banks take millions of applications for new accounts and loans, often overriding do-not-open recommendations from third-party risk screening services. Many of these high-risk applicants overdraw their accounts or default on their loans, costing banks millions of dollars in losses. Hadoop can be used to store and analyze additional data streams, retained for longer to help bankers control risk and minimize losses.
  • Improving Insurance Underwriting. Insurance companies store and analyze streaming vehicle data to underwrite Pay As You Drive (PAYD) policies. One HDP customer was able to go from retaining under 25% of policyholder PAYD data to retaining all of it, and can now process that data stream in three days or less. This allows the company to better align premiums with risk, and reward safer drivers with more affordable policies.
  • Maintaining Sub-Second SLAs in a “Ticker Plant.” Ticker plants collect and process massive data streams on equity markets, allowing traders and trading systems to make real-time decisions. This same historical market data can also be stored in Hadoop for long-term analysis of market trends.

While participating in the 2014 Hadoop Summit keynote panel discussion “Hadoop in the Enterprise”, Gleason from BNY Mellon articulated how his bank came to understand the opportunity presented by Hadoop:

“As we brought [Hadoop] in, we started to realize that there was so much more we could be doing, and that it really was more than just big data and decision science. It was a platform for really changing the way we manage data around the organization.”

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