Research on financial irregularities identification: a machine learning perspective

被引:0
作者
Chai K.-C. [1 ]
Zhu J. [1 ]
Yang Y. [1 ]
Lan H.-R. [1 ]
Ou Y.-L. [1 ]
Li Q. [1 ]
机构
[1] Business School, Guilin University of Electronic Technology, Guangxi Province, Guilin
来源
International Journal of Internet Manufacturing and Services | 2022年 / 8卷 / 04期
关键词
China; financial irregularities identification; machine learning; SMOTE algorithm;
D O I
10.1504/IJIMS.2022.10050659
中图分类号
学科分类号
摘要
In the era of big data, data-driven analytics can generate many meaningful insights. With the gradual maturity of artificial intelligence (AI) algorithm, it can help solve problems that are difficult to identify in the financial field. Through theoretical analysis, this paper constructs feature engineering of multiple internal and external factors that affect corporate financial irregularities, and then automatically identifies Chinese listed companies with financial irregularities based on machine learning algorithm. In this paper, we verify the effectiveness of SMOTE algorithm in improving the imbalance data of financial irregularities of Chinese listed companies and use LightGBM algorithm to sort the ten factors of characteristic importance of financial irregularities of Chinese listed companies. This paper provides a new way to detect financial irregularities for financial regulatory authorities and a paradigm for the applying of AI in the financial field. Copyright © 2022 Inderscience Enterprises Ltd.
引用
收藏
页码:383 / 399
页数:16
相关论文
共 40 条
  • [21] Ke G., LightGBM: a highly efficient gradient boosting decision tree, Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 3149-3157, (2017)
  • [22] Kini O., Williams R., Tournament incentives, firm risk, and corporate policies, Journal of Financial Economics, 103, 2, pp. 350-376, (2012)
  • [23] Kirkos E., Spathis C., Manolopoulos Y., Data mining techniques for the detection of fraudulent financial statements, Expert Systems with Applications, 32, 4, pp. 995-1003, (2007)
  • [24] Kumar G., Muckley C.B., Pham L., Et al., Can alert models for fraud protect the elderly clients of a financial institution?, The European Journal of Finance, 25, 17, pp. 1683-1707, (2019)
  • [25] Lazear E., Rank-order tournaments as optimal labor contracts, Journal of Political Economy, 89, 5, pp. 841-864, (1981)
  • [26] Liang J., Lv W., Research on detecting technique of financial statement fraud based on fuzzy genetic algorithms BPN, International Conference on Management Science and Engineering, (2009)
  • [27] Lokanan M., Theorizing financial crimes as moral actions, European Accounting Review, 27, pp. 1-38, (2017)
  • [28] Loughran T., McDonald B., When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks, Journal of Finance, 66, 1, pp. 35-65, (2011)
  • [29] Maka K., Pazhanirajan S., Mallapur S., Selection of most significant variables to detect fraud in financial statements, Materials Today: Proceedings, (2020)
  • [30] Milhaupt L., Reputational sanctions in China’s securities market, Columbia Law Review, 108, 4, pp. 929-983, (2008)