Credit Risk Identification Algorithm Based on <scp>BaggingFCBF</scp> ‐ <scp>TCN</scp>

ABSTRACT The identification of personal credit risk constitutes a fundamental concern within the realm of financial risk management. As the credit industry experiences significant growth, the precise evaluation of borrowers' credit risk and the mitigation of credit default risk have emerged as critical priorities for financial institutions and researchers worldwide. To enhance the ability to identify defaulting customers, this paper proposes a credit risk identification algorithm based on Bagging Fast Correlation‐Based Filter with Temporal Convolutional Network (BaggingFCBF‐TCN). This algorithm initially incorporates the feature selection approach inherent in the Bagging strategy to identify and filter the characteristics associated with defaulting customers, which serves to mitigate the bias in feature selection outcomes that may favor the majority class. Subsequently, it employs an enhanced Temporal Convolutional Network (TCN) classifier for the purpose of credit risk assessment, thereby improving the ability to discern both long‐term and short‐term dependencies present in personal credit data. The test results show that: (1) The BaggingFCBF‐TCN algorithm significantly enhances the model's ability to identify defaulting customers, achieving optimal overall identification performance. (2) The results of the combination effect analysis indicate that the personal credit risk identification model constructed using the BaggingFCBF‐TCN combination algorithm outperforms other combination algorithms in both the original dataset and the dataset after class balancing treatment.