Research on financial irregularities identification: a machine learning perspective Online publication date: Tue, 31-Jan-2023
by Kuang-Cheng Chai; Jiawei Zhu; Yang Yang; Hao-Ran Lan; Yang-Lu Ou; Qiang Li
International Journal of Internet Manufacturing and Services (IJIMS), Vol. 8, No. 4, 2022
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Internet Manufacturing and Services (IJIMS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook