Accepted Unknow ; Published 2024 November 2

Journal of Computer Technology and Applied Mathematics, 2024, 1(4), 3007-4126.

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This paper discusses the relationship between financial fraud risk and credit risk in China's financial market, and points out that financial statement fraud has a serious impact on the fairness and transparency of the capital market, and damages the legitimate rights and interests of investors. Research shows that financial fraud risk may be an important signal of credit risk outbreak, and may further spread through the credit network, resulting in a larger scale of credit risk. This paper reviews the evolution of credit risk assessment models, from early expert analysis methods to modern statistical and machine learning-based methods, including Z-score models, SVM models, and random forests. Special attention is paid to the application of the C5.0 algorithm in credit risk assessment, highlighting its advantages in terms of data characteristics and prediction accuracy. Finally, the ROC curve and KS curve are used to evaluate the prediction effect of the model, which shows that the model has good prediction ability and practical value, and provides a new methodology for financial fraud risk assessment.

Financial Fraud Risk , Credit Risk Assessment , Machine Learning , C5.0 Algorithm .

APA Style

Xin, Q., Song, R., Wang, Z., Xu, Z. & Zhao, F. (2024). Enhancing bank credit risk management using the c5.0 decision tree algorithm. Journal of Computer Technology and Applied Mathematics, 1(4), 100-107. https://doi.org/10.5281/zenodo.14032041

This article sincerely thanks Wang, Xiangxiang, et al. made outstanding contributions to the article [1]"Short-Term Passenger Flow Prediction for Urban Rail Transit Based on Machine Learning." Their research provides new ideas and methods for short-term passenger flow prediction of urban rail transit and opens up new possibilities for the application of machine learning in this field. Through their in-depth exploration of data analysis and model construction, we have a deeper understanding of the law and characteristics of passenger flow changes in urban rail transit systems. It is especially worth mentioning that the passenger flow forecasting method based on machine learning they proposed is not only innovative in theory but also shows sound forecasting effects and application prospects in practice. Their research results promote urban rail transit development and provide valuable experience and inspiration for our research and practice in artificial intelligence financial risk management and other fields. In addition, I am so grateful to Zhan and the outstanding research by Xiaoan et al. in [2]“Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models.” Their work has brought new ideas and methods to the field of sentiment analysis, especially the application of multiple attention mechanisms and pre-trained models, providing valuable contributions to the further development of the field. These results not only help companies better understand user needs and market feedback but also point the way to progress in natural language processing. Thanks again for their hard work and research.

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