There's been a lot of conversation recently around SAS High-Performance Analytics. And, rightly so - it's incredibly cool and applicable to every industry imaginable, including financial services.
What if your risk calculations had seen the '08 credit collapse in time? CBS SmartPlanet addresses this (and highlights SAS High-Performance Analytics, with comments from SAS Chief Marketing Officer Jim Davis and SAS customer United Overseas Bank) in their article, "Less Risk, More Reward - expanding the borders of financial analysis."
Below is an excerpt from the article:
SAS’s high-performance analytics platform also takes advantage of in-memory computing. In an early demonstration of the technology, SAS reduced the time it typically took for a value-at-risk calculation on one server from 18 hours to under three minutes. SAS’s products, and others like it, can also integrate the piles of data sprinkled throughout an organization and analyze them quickly, without the need to overhaul an entire system — something particularly important for any organization that’s spent many millions on existing IT infrastructure.
That type of performance and capability has people like Ming Soong, chief risk officer of the United Overseas Bank in Singapore, lobbying to implement high-performance computing throughout the organization.
Check out the entire article for more!
Did you miss?
Be sure to check out the two-part Risk Management Knowledge Exchange series, "Banking on Big Data" to learn more about how banks are putting high-performance analytics to work. Here's a teaser:
By using high-performance analytics, banks can:
- Achieve better operational efficiency, which improves IT while reducing spending.
- Acquire and retain profitable customers by delivering higher value.
- Improve risk management – market, credit, liquidity and firmwide.
- Strengthen the integration of social media with business processes and decision making.
- Differentiate and innovate to stand out in the marketplace.
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I would like to know what the disadvantages of using the BIG DATA.