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Back in the Saddle; Risk professionals lost credibility when their flawed decisioning models led banks straight down a hole. Now they are examining more of the variables, and the "whys" behind the data to produce business intelligence models that work.

Article Excerpt
Byline: Michael Dumiak

When SAS chief marketing officer Jim Davis told a roomful of executives in Washington last spring that he "doesn't believe business intelligence is where the future is," it went off like a little bomb in the close-knit circles of financial technology.

Not that Davis seemed to be forsaking SAS' bread-and-butter business intelligence software empire, but because he put his finger on a much broader issue: the blind adherence to enterprise data. The predictive modeling, automated credit decisioning, risk management and business decisions made with confidence behind BI-bred numbers had proved to be a disastrously unreliable guide through the storm of a financial meltdown, and there are ramifications to come for institutions' books.

What is needed, he and others believe, is a new discipline that more closely examines the raw numbers relating to customer and market activity. By adding more variables that ask "what if" instead of just "what happened," bankers and traders are looking for next-generation, deeper-diving metrics that will hopefully erase the blind spots in current data-based decisioning, and prevent future catastrophe. Thomas Davenport, author of "Competing on Analytics," management professor and former Accenture fellow, said he was once told by a former Washington Mutual chief risk officer "if they had had...

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