Financial services organizations face immense hurdles in maintaining profitability, building competitive advantage and meeting service levels for customers and in-house staff. All of these can be severely impacted through a widening variety of risk factors, including regulatory risk, criminal risk, macroeconomic risk, etc. Increased demands from customers, regulators and shareholders are also driving financial services businesses to seek new ways to simultaneously sharpen investing prowess, minimize risk and fraud, and improve compliance and customer service at unprecedented rates.
For example, the recent global economic crisis has resulted in further regulatory pressures on financial institutions, such as investment and commercial banks. The Basel III Accord has introduced both increased capital requirements and countercyclical capital buffer requirements. Meeting these kinds of increased risks and demands requires deep, timely analysis of data – data that accumulates relentlessly. This accumulation has started to push many institutions past the performance limits of their existing data warehousing platforms, especially those that weren’t designed for complex analytics. Whether they’re mortgage banks, investment banks, credit card providers, or other institutions within the industry, financial services organizations must perform “what-if” scenario analysis, identify risks, and detect fraud patterns. The advanced analytic complexity required often makes such analysis slow and painful, if not impossible. This in turn can result in sizeable losses.
This white paper outlines these challenges and provides a clear path to providing the accelerated insight needed to perform in today’s complicated business environment to reduce risk, stop fraud and increase profits.
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