With talk of predictive modeling and data mining becoming all the rage, it is time for card marketers to take a crash course in this science so they can better understand where the industry is heading. At its most basic level, a predictive model takes attributes about a customers or transaction and produces a score. For example, the score may indicate the likelihood of attrition, the likelihood of delinquency, the expected response to an offer, the expected profit, or the timelife value of a customer. Predictive modeling for financial services began in earnest with a scorecard.
Score cards are simple to explain.
They essentially are no different than the quizzes present in many teen magazines, but do not have the predictive ability of more powerful techniques. Next to complexity are the logistic regression models. The score produced by these models is related in a simple fashion to a linear function of the attributes about the customer or transaction. These types of linear predictors are simple to compute, but often do not capture the complexity of data. For example, linear function of age, while, for example, the likelihood of delinquency or response to an offer does not vary so simple with age.