Improvement On Behavior Scores By Dual-Model Scoring System

In credit risk management, the on-line analytical process has been accepted by most credit card issuers. The major tools used in such an OLAP are statistics and neural networks. Through a designed algorithm, the OLAP generates scores for each account or for each customer which depends on the level of the processing. Generally speaking, logistic regressions and feed-forward networks are the major players in OLAP of this field and usually are used separately. This paper discusses an approach — Dual-Model Scoring System — to combine these two major players and use them together in the credit scoring. Primarily, the classification problem for two classes are considered. By the Bayesian rule, the objective function of classification can be reduced to estimate the Bayesian posterior probability. Such a probability is estimated by using the MLE approach in logistic regressions and the Two-Stage (Gibbs) learning algorithm3in feed-forward networks. The motivation of the proposal comes from the following two considerations: (1) Both logistic regression and neural networks have their advantages and disadvantages and the combined of these two can enhance their predictive ability and offset their weakness. (2) To reduce the false positive rate in the decision region. Besides the discussion of the architecture design of Dual-Model Scoring System, the paper has demonstrated the power of the present proposal in a real data set.