Abstract
Competition of the consumer credit market in Taiwan has become severe recently. Therefore, most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers. This study uses a genetic algorithm for feature selection and decision trees for customer segmentation. Moreover, it utilizes logistic regression to build the application and credit bureau scoring models where the two scoring models are combined for constructing the scoring matrix. The scoring matrix undergoes more accurate risk judgment and segmentation to further identify the parts required enhanced management or control within a personal loan portfolio. The analytical results demonstrate that the predictive ability of the scoring matrix outperforms both the application and credit bureau scoring models. Regarding the K-S value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 18.40 and 5.70%, respectively. Regarding the AUC value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 10.90 and 6.40%, respectively. Furthermore, this study applies the scoring matrix to the credit approval decisions for corresponding risk groups to strengthen bank’s risk management practices. Key words: Scoring matrix, application scoring model, credit bureau scoring model, genetic algorithm, logistic regression, decision trees.
Highlights
With the rapid growth in the credit industry and the management of large loan portfolios, application and behavioral scoring models have been extensively used for the credit risk evaluation decisions by the finance industry.Application scoring models help banks determine whether credit should be granted to new applicants based on customer characteristics such as income, education, age, and so on (Akhavein, 2005)
Most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers
We utilize a hybrid mining approach in the design of credit scoring models to support credit approval decisions based on the four main steps: (1) using genetic algorithm (GA) to select input features, (2) using decision trees for customer segmentation, (3) using regression (LR) to build the application and credit bureau scoring models based on important input variables of bank’s internal application data and credit bureau data, (4) combining the application and credit bureau scoring models to construct the scoring matrix
Summary
With the rapid growth in the credit industry and the management of large loan portfolios, application and behavioral scoring models have been extensively used for the credit risk evaluation decisions by the finance industry. Application scoring models help banks determine whether credit should be granted to new applicants based on customer characteristics such as income, education, age, and so on (Akhavein, 2005). Behavioral scoring models help banks predict the probability that existing customer will default or become delinquent based on consumer's repayment and usage behavior (Boyer and Hult, 2005). We utilize a hybrid mining approach in the design of credit scoring models to support credit approval decisions based on the four main steps: (1) using genetic algorithm (GA) to select input features, (2) using decision trees for customer segmentation, (3) using regression (LR) to build the application and credit bureau scoring models based on important input variables of bank’s internal application data and credit bureau data, (4) combining the application and credit bureau scoring models to construct the scoring matrix
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