Author(s)

Qiao Xia

Affiliation(s)

School of Economics & Management, Southeast University, Jiulong Lake Campus, Nanjing 211189, Jiangsu, China.

Corresponding Author

Qiao Xia

ABSTRACT

In recent years, China’s capital market has witnessed increasingly sophisticated and concealed financial fraud schemes among listed companies, posing substantial threats to market integrity and stakeholder protection. Addressing this challenge, this study develops a comprehensive multi-dimensional detection framework grounded in accounting theory, integrating financial indicators, industrial characteristics, regional factors, and corporate governance elements. The research employs an innovative entropy-weighted TOPSIS methodology that effectively balances quantitative precision with theoretical foundations. Through rigorous empirical analysis of 176 documented fraud cases spanning 2000-2022, we demonstrate that the Operational Scale indicator induces “information overload” that compromises model discrimination, while optimized corporate governance factors—particularly Executive Education Level and Board Meeting Frequency—demonstrate enhanced predictive power with a combined weight of 0.658. The proposed model achieves 65.53% classification accuracy, showing particular efficacy in detecting characteristic fraud patterns involving revenue inflation and fictitious transactions. Furthermore, Our findings validate an integrated human-machine framework for financial regulation, balancing methodological rigor with practical adaptability in dynamic market environments.

KEYWORDS

Financial fraud identification; Fraud theory; The entropy weight method; TOPSIS model

CITE THIS PAPER

Qiao Xia. Design and application of financial fraud identification model under TOPSIS based on entropy weight method. World Journal of Economics and Business Research. 2025, 3(4): 33-39. DOI: https://doi.org/10.61784/wjebr3065.

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