A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning
With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of diffe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/12/12/588 |
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| author | Shanshan Zhu Haotian Wu Eric W. T. Ngai Jifan Ren Daojing He Tengyun Ma Yubin Li |
| author_facet | Shanshan Zhu Haotian Wu Eric W. T. Ngai Jifan Ren Daojing He Tengyun Ma Yubin Li |
| author_sort | Shanshan Zhu |
| collection | DOAJ |
| description | With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conducted case studies on specific companies and industries, confirming the significant interpretability and practical applicability of the proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reached 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research study is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors. |
| format | Article |
| id | doaj-art-8ae2b71f498d41c4968b8c2838f5da56 |
| institution | DOAJ |
| issn | 2079-8954 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-8ae2b71f498d41c4968b8c2838f5da562025-08-20T02:50:43ZengMDPI AGSystems2079-89542024-12-01121258810.3390/systems12120588A Financial Fraud Prediction Framework Based on Stacking Ensemble LearningShanshan Zhu0Haotian Wu1Eric W. T. Ngai2Jifan Ren3Daojing He4Tengyun Ma5Yubin Li6School of Economics and Management, Harbin Institute of Technology, Shenzhen 518000, ChinaFaculty of Computer, Harbin Institute of Technology, Harbin 150001, ChinaDepartment of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong 00852, ChinaSchool of Economics and Management, Harbin Institute of Technology, Shenzhen 518000, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518000, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518000, ChinaSchool of Economics and Management, Harbin Institute of Technology, Shenzhen 518000, ChinaWith the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conducted case studies on specific companies and industries, confirming the significant interpretability and practical applicability of the proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reached 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research study is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors.https://www.mdpi.com/2079-8954/12/12/588financial statementfraud predictionmachine learningstacking model |
| spellingShingle | Shanshan Zhu Haotian Wu Eric W. T. Ngai Jifan Ren Daojing He Tengyun Ma Yubin Li A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning Systems financial statement fraud prediction machine learning stacking model |
| title | A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning |
| title_full | A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning |
| title_fullStr | A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning |
| title_full_unstemmed | A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning |
| title_short | A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning |
| title_sort | financial fraud prediction framework based on stacking ensemble learning |
| topic | financial statement fraud prediction machine learning stacking model |
| url | https://www.mdpi.com/2079-8954/12/12/588 |
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