Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

by Foster Provost

they settled on five clusters that represented very different consumer credit behavior (e.g., those who spend a lot but pay off their cards in full each month versus those who spend a lot and keep their balance near their credit limit). These different sorts of customers can tolerate very different credit lines (in the two examples, extra care must be taken with the latter to avoid default). The problem with using this clustering immediately for decision making is that the data are not available when the initial credit line is set. Briefly, Haimowitz and Schwarz took this new knowledge and cycled back to the beginning of the data mining process. They used the knowledge to define a precise predictive modeling problem: using data that are available at the time of credit approval, predict the probability that a customer will fall into each of these clusters. This predictive model then can be used to improve initial credit line decisions.