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Nancy Feig
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The Next Level in Business Intelligence

To improve the value of business intelligence to the enterprise, banks are centralizing BI efforts and improving data management, as well as turning to predictive analytics.

Rise of Predictive Analytics

Still, the vast wealth of data at banks' disposal presents a tremendous opportunity, and how and when banks utilize this data is changing. With advances in predictive analytics, "now" is now too late when it comes to delivering decision-making information inside the bank. Instead of real-time information, decision makers at banks now are asking for BI systems that predict the future. BI is "going up steps from reactive to proactive," says Ronnie Ray, VP, marketing for Infovista, a Herndon, Va.-based performance-management software company.

With more and more sophisticated information, banks can spot trends and cycles, even by customer type, according to TowerGroup's Kopp. Predictive analytics can be used across the bank, particularly in the areas of marketing, capacity planning and risk management, he says.

"For years, banks have talked about cross-selling," says Kelly Pennock, CEO of Bellevue, Wash.-based Intelligent Results, a provider of analytics and decision-management software. "The real key to cross-selling is being able to determine what new offer will motivate which individual customers to buy a new product or service."

Worldwide revenue from predictive analytics will grow through 2008 with a compound annual growth rate of 8 percent, according to a 2005 study from IDC. The Framingham, Mass.-based research and consulting firm defines predictive analytics as the use of sophisticated analytics, which are more complex in their mathematics than core analytics, to determine the likelihood of future trends or events.

Some banks are taking predictive analytics a step further. Cincinnati-based U.S. Bank ($208.9 billion in assets), for example, recently selected Fair Isaac's (Minneapolis) Strategy Science tool for its risk management practice. Fair Isaac is developing a decision model for U.S. Bank's credit line management that leverages the bank's own data and analytics to go beyond predictions to make actual decisions. To do that, Strategy Science combines methodology, process and platform, and models the economics of customer decisions, according to Fair Isaac.

A decision model maps the relationship between multiple input variables to the range of decision choices available to the user. Strategy Science works by establishing optimal actions for organizations to take for customers in any area, says Sally Taylor-Shoff, VP of analytic product management at Fair Isaac. "It explicitly manages the trade-off between risk and reward or cost and benefit," she adds. Strategy Science enabled U.S. Bank to see exactly what would happen if a customer's credit line was increased and make the appropriate decision based on its risk-reward formula, Taylor-Shoff contends, helping the bank increase its profits by $7 per active account.

Intelligent Results also recently released a product that integrates BI analytics and decision management. The new software, Predigy, includes five modules that map each step in the analysis-to-action progression, according to the firm's Pennock. With such developments in predictive and actionable analytics, "From now on, banks can focus on action, not just customer outreach," she says. *

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