Constructing a reassigning credit scoring model

Credit scoring model development became a very important issue as the credit industry has many competitions and bad debt problems. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. In order to solve the classification and decrease the Type I error of credit scoring model, this paper presents a reassigning credit scoring model (RCSM) involving two stages. The classification stage is constructing an ANN-based credit scoring model, which classifies applicants with accepted (good) or rejected (bad) credits. The reassign stage is trying to reduce the Type I error by reassigning the rejected good credit applicants to the conditional accepted class by using the CBR-based classification technique. To demonstrate the effectiveness of proposed model, RCSM is performed on a credit card dataset obtained from UCI repository. As the results indicated, the proposed model not only proved more accurate credit scoring than other four common used approaches, but also contributes to increase business revenue by decreasing the Type I and Type II error of scoring system.