Leading banks and capital markets firms require extreme performance as they start leveraging real-time analytics for many initiatives including risk management, fraud detection, compliance, consumer metrics to gain a competitive edge and comply with regulations.
Here’s a roundup of the most interesting posts we’ve ready lately on Financial Services and big data:
No, “alternative data” isn’t some sort of new electronic music scene. It refers to data sets that are not typically included in financial analyses, such as changes to a company’s website, satellite imagery, geolocation data, and more. The TL;DR: Hedge funds are turning to “alternative data” to make smarter predictions and investments, though not without certain legal risks. This in-depth article that explores the challenges—and successes—hedge funds are having using big data.
Boom in big data needs to find its way into more CFO offices / Mark Garrett, Executive Vice President & CFO, Adobe
When the CFO of a major software company talks, we should probably all listen. Mark Garrett of Adobe argues that a company’s finance team is the place to start when undertaking a data-centered digital transformation. Why? Because they’re “data-oriented by nature,” and because they touch every other part of the business. Garrett shares some of his insights into how Adobe took the time and resources to invest in integrating a large variety of data sources in order to create “actionable business intelligence.”
4 best ways to use advanced analytics for Financial Services / Sabine Vollmer, Senior Editor, @cgma
This quick but insightful read provides some high-level applications for advanced analytics programs for financial services companies. Based on a study by the Financial Executives Research Foundation, successful advanced analytics programs:
- Work to gain deeper insights from data
- Help identify root causes of problems
- Identify consumer behavior patterns to nip attrition in the bud
- Identify and manage risk
Some typical challenges to implementing advanced analytics often include managing data from legacy IT system that wasn’t designed to share data, as well as the human side of finding “qualified data scientists” who can work with the business’s various departments to be successful.
In fraud prevention, big data technology is essential / @zeljkazorz
While this article focuses on big data technology needs in the eCommerce sector, its lessons also quite easily apply to financial institutions. Long-Ji Lin, chief scientist of Signifyd, points out that while machine learning systems are being implemented to fight fraud in eCommerce, big data analytics is impossible without the appropriate technology or the appropriate architecture. Our favorite quote: “One cannot just ‘plug’ big data technology into an existing system and expect it to work wonders.”
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