Establishing a reasonable and effective feature system is the basis of credit risk early warning. Whether the system design is appropriate directly determines the accuracy of the credit risk evaluation results. In this paper, we proposed a feature system through a validity index with maximum discrimination and commercial banks' loan profit maximization. First, the first objective function is the minimum validity index constructed by the intra-class, between-class, and partition coefficients. The maximum difference between the right income and wrong cost is taken as the second objective function to obtain the optimal feature combination. Second, the feature weights are obtained by calculating the change in profit after deleting each feature with replacement to the sum of all change values. An empirical analysis of 3, 425 listed companies from t-1 to t-5 time windows reveals that five groups of feature systems selected from 614 features can distinguish between defaults and non-defaults. Compared with 14 other models, it is found that the feature systems can provide at least five years' prediction and enable financial institutions to obtain the maximum profit.
Citation: Meng Pang, Zhe Li. A novel profit-based validity index approach for feature selection in credit risk prediction[J]. AIMS Mathematics, 2024, 9(1): 974-997. doi: 10.3934/math.2024049
Establishing a reasonable and effective feature system is the basis of credit risk early warning. Whether the system design is appropriate directly determines the accuracy of the credit risk evaluation results. In this paper, we proposed a feature system through a validity index with maximum discrimination and commercial banks' loan profit maximization. First, the first objective function is the minimum validity index constructed by the intra-class, between-class, and partition coefficients. The maximum difference between the right income and wrong cost is taken as the second objective function to obtain the optimal feature combination. Second, the feature weights are obtained by calculating the change in profit after deleting each feature with replacement to the sum of all change values. An empirical analysis of 3, 425 listed companies from t-1 to t-5 time windows reveals that five groups of feature systems selected from 614 features can distinguish between defaults and non-defaults. Compared with 14 other models, it is found that the feature systems can provide at least five years' prediction and enable financial institutions to obtain the maximum profit.
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Meng Pang, Zhe Li. A novel profit-based validity index approach for feature selection in credit risk prediction[J]. AIMS Mathematics, 2024, 9(1): 974-997. doi: 10.3934/math.2024049
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