Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

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Application of Machine Learning in Financial Fraud of Listed Companies: An Innovative Prediction Model

Authors
Zehao Wang1, *, Moqin Yang2, Yizhan Du3, Hanqing Hu4
1School of Management, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China
2School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, People’s Republic of China
3School of Law, Zhongnan University of Economics and Law, Wuhan, 430073, People’s Republic of China
4School of Economics, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China
*Corresponding author. Email: m202177730@hust.edu.cn
Corresponding Author
Zehao Wang
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_100How to use a DOI?
Keywords
Financial Fraud; Predictive Models; Machine Learning; Feature Engineering
Abstract

The over-reliance on financial statements published by listed companies as the main reference data can lead to great losses to capital market investors and hinder the orderly and healthy development of the capital market in the event of financial fraud by the company. In this context, the establishment of effective forecasting models to predict and analyze financial fraud has become the focus of research to avoid these economic traps. In this paper, we take the financial statement data of Shanghai and Shenzhen A-share listed companies in China during 2000–2020 as the observation sample, and establish a new universal and effective prediction model, which overcomes the unbalanced training of machine learning, and the innovative index system is finally externally verified with a prediction accuracy of 98.0% after three rounds of screening by psychological preference survey, feature engineering and model evaluation, leading all similar current financial fraud prediction models of listed companies.

Copyright
© 2023 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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Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-198-2_100
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_100How to use a DOI?
Copyright
© 2023 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.

Cite this article

risenwbib
TY  - CONF
AU  - Zehao Wang
AU  - Moqin Yang
AU  - Yizhan Du
AU  - Hanqing Hu
PY  - 2023
DA  - 2023/08/10
TI  - Application of Machine Learning in Financial Fraud of Listed Companies: An Innovative Prediction Model
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 957
EP  - 965
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_100
DO  - 10.2991/978-94-6463-198-2_100
ID  - Wang2023
ER  -