1. A computer-implemented method for optimal partitioning of segments in a consumer credit segmentation tree comprising: generating by a computer a first attribute-based independent variable on a segmentation tree using a primary dependent variable having two classes, wherein a value of the independent variable is selected that creates two groups that minimize misclassification of the two classes of the primary dependent variable; generating by the computer a second attribute-based independent variable on the tree using the primary dependent variable; generating by the computer risk tiers for the first attribute-based independent variable on the tree using a first risk score and the primary dependent variable, wherein values of the independent variable are selected that create two groups that minimize misclassification of the two classes of the primary dependent variable; generating by the computer risk tiers for a first segment of the second attribute-based independent variable on the tree using the first risk score and the primary dependent variable, wherein values of the independent variable are selected that create two groups that minimize misclassification of the two classes of the primary dependent variable; generating by the computer risk tiers for a second segment of the second attribute-based independent variable on the tree using a second risk score and the primary dependent variable; and generating by the computer profiles in the risk tiers for the second segment of the second attribute-based independent variable with a profile dependent variable having two classes to complete the tree, wherein values of a profile model are selected that create two groups that minimize misclassification of the two classes of the profile dependent variable.
1. A computer-implemented method for optimal partitioning of segments in a consumer credit segmentation tree comprising: generating by a computer a first attribute-based independent variable on a segmentation tree using a primary dependent variable having two classes, wherein a value of the independent variable is selected that creates two groups that minimize misclassification of the two classes of the primary dependent variable; generating by the computer a second attribute-based independent variable on the tree using the primary dependent variable; generating by the computer risk tiers for the first attribute-based independent variable on the tree using a first risk score and the primary dependent variable, wherein values of the independent variable are selected that create two groups that minimize misclassification of the two classes of the primary dependent variable; generating by the computer risk tiers for a first segment of the second attribute-based independent variable on the tree using the first risk score and the primary dependent variable, wherein values of the independent variable are selected that create two groups that minimize misclassification of the two classes of the primary dependent variable; generating by the computer risk tiers for a second segment of the second attribute-based independent variable on the tree using a second risk score and the primary dependent variable; and generating by the computer profiles in the risk tiers for the second segment of the second attribute-based independent variable with a profile dependent variable having two classes to complete the tree, wherein values of a profile model are selected that create two groups that minimize misclassification of the two classes of the profile dependent variable. 6. The method of claim 1 , wherein only a portion of the population characterized as bad is used to define the profiles.