1. A method for optimal partitioning of segments in a consumer credit segmentation tree comprising: defining a first attribute-based independent variable on a first tree using a primary dependent variable having two classes; defining a second attribute-based independent variable on the first tree using the primary dependent variable; defining risk tiers for the first attribute-based independent variable on the first tree using a first risk score and the primary dependent variable; defining risk tiers for the second attribute-based independent variable on the first tree using a second risk score and the primary dependent variable; superimposing the first tree structure, based on the primary dependent variable, onto a second tree; and defining profiles in the risk tiers for the second attribute-based independent variable with a profile dependent variable having two classes, completing the second tree, wherein the second tree is used to segment a population according to credit related behavior.