The Banking Industry Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling

In today’s slow-moving economy the banking industry is in tough competitive “boxing ring,” in which they are getting hit with high customer attrition rates. And, achieving their goals – reduce costs and increase profit – is a matter of “survival of the fittest.” Fortuitously, their gargantuan volumes of transaction data gathered daily are the key ingredient for achieving their goals. High performance computing for discovering interesting and previously unknown information within the gargantuan data is needed as part of a tactical analytical strategy to build models to win their goals. Traditional statistical approaches are virtually ineffectual at data mining, i.e., uncovering undetected cost-reduction/profit-gaining predictive relationships. This knowledge is vitally necessary for building models for reducing costs, and increasing profits. The purpose of this article is to demonstrate the strength of the data mining muscle of the genetic data-mining feature of the GenIQ Model©. I discuss case studies, which use the “body blows” of genetic data mining to produce victorious cost-reduction, and profit-gaining models.