Enhancing Bank Credit Risk Management Using the C5.0 Decision Tree Algorithm

Qi Xin; Runze Song; Zeyu Wang; Zeqiu Xu; Fanyi Zhao

doi:10.5281/zenodo.14032041

Authors

Qi Xin University of Pittsburgh
Runze Song California State University
Zeyu Wang University of Toronto
Zeqiu Xu Carnegie Mellon University
Fanyi Zhao Stevens Institute of Technology

DOI:

https://doi.org/10.5281/zenodo.14032041

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n4a12

Disciplines:

Computer Science

Subjects:

Machine Learning in Finance

References:

35

Keywords:

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

Abstract

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.

Author Biographies

Qi Xin, University of Pittsburgh

Management Information Systems, University of Pittsburgh, Pittsburgh, PA, USA.

Runze Song, California State University

Information System & Technology Data Analytics, California State University, CA, USA.

Zeyu Wang, University of Toronto

Computer Science, University of Toronto, Toronto, Canada.

Zeqiu Xu, Carnegie Mellon University

Computer Science, Carnegie Mellon University, CA, USA.

Fanyi Zhao, Stevens Institute of Technology

Computer Science, Stevens Institute of Technology, NJ, USA.

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