Financial Fraud Prediction in Chinese Growth Enterprise Board Listed Companies

--Based on the Machine Learning Experience of GWO+XGBoost

DOI

10.2991/978-94-6463-538-6_23How to use a DOI?

Keywords

Financial Fraud; GWO+XGBoost; Machine Learning; Financial Characteristics Variables; Non-financial Characteristic Variables

Abstract

In recent years, there have been frequent incidents of financial fraud in China’s A-share GEM-listed companies. In order to effectively detect instances of financial fraud, this study focuses on 980 listed companies in China’s Growth Enterprise Market (GEM) and utilizes the GWO+XGBoost algorithm to develop a predictive model for identifying such fraudulent activities. This study incorporates both financial and non-financial information from the companies. The empirical studies reveal that machine learning-based models such as SVM and XGBoost exhibit superior predictive performance compared to traditional statistical methods, including Naive Bayes and Logistic regression.; The GWO+XGBoost model outperforms other machine learning models in terms of Precision, Recall, F₁ and AUC; The incorporation of non-financial information indicators, such as corporate governance and audit information, significantly enhances the predictive accuracy of the model, underscoring the efficacy of non-financial information in providing valuable incremental information content for financial fraud prediction.; The study also employs Shapley’s value method to examine the contribution of characteristic variables in predicting financial fraud. This analysis provides valuable decision-making guidance for auditors, investors, and regulators, helping to reduce information asymmetry in the capital market and enhance resource allocation efficiency.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xi Chen
PY  - 2024
DA  - 2024/10/01
TI  - Financial Fraud Prediction in Chinese Growth Enterprise Board Listed Companies
BT  - Proceedings of the 4th International Conference on Economic Development and Business Culture (ICEDBC 2024)
PB  - Atlantis Press
SP  - 192
EP  - 205
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-538-6_23
DO  - 10.2991/978-94-6463-538-6_23
ID  - Chen2024
ER  -