A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model

• Published in: Expert systems with applications Vol. 39; no. 3; pp. 2650 - 2661
• Main Authors: Chi, Bo-Wen, Hsu, Chiun-Chieh
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.02.2012
• Subjects: Banking; Credit bureau scoring model; Customers; Dual scoring model; Genetic algorithm; Genetic algorithms; Logistic regression; Mathematical models; Mortgage behavioral scoring model; Risk; Risk management; Scoring models; Strategy; Logistic regression; Credit bureau scoring model; Genetic algorithm; Dual scoring model; Mortgage behavioral scoring model
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2011.08.120

► The K–S and AUC values of the behavioral scoring model are 44.6% and 78.7%. ► The K–S and AUC values of the credit bureau scoring model are 51.0% and 80.9%. ► The K–S and AUC values of the dual scoring model are 59.3% and 87.7%. ► The dual scoring model exhibits greater predictive ability. Credit scoring model is an important tool for assessing risks in financial industry, consequently the majority of financial institutions actively develops credit scoring model on the credit approval assessment of new customers and the credit risk management of existing customers. Nonetheless, most past researches used the one-dimensional credit scoring model to measure customer risk. In this study, we select important variables by genetic algorithm (GA) to combine the bank’s internal behavioral scoring model with the external credit bureau scoring model to construct the dual scoring model for credit risk management of mortgage accounts. It undergoes more accurate risk judgment and segmentation to further discover the parts which are required to be enhanced in management or control from mortgage portfolio. The results show that the predictive ability of the dual scoring model outperforms both one-dimensional behavioral scoring model and credit bureau scoring model. Moreover, this study proposes credit strategies such as on-lending retaining and collection actions for corresponding customers in order to contribute benefits to the practice of banking credit.