A Robust Credit Screening Model Using Categorical Data

Motivated by an application in a public utility, the credit screening problem is re-examined from a decision theoretic viewpoint. The relationships between several alternative problem formulations are explored, and compared to the classical linear discriminant analysis (LDA) approach. Several mathematical programming based solution methods are proposed when the data are binary, and an efficient algorithm is developed for the case when the screening function must also have binary weights. Actual results of both the mathematical programming and LDA methods are presented and compared. The resulting mathematical programming rules are effective, robust, and flexible to administer. Practical advantages of the resulting "n out of N" type rules are discussed. These screening rules have been widely implemented by a major public utility and have resulted in substantial benefits to the utility and to the public.