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Financial Statement Fraud Prediction System: A Deep Learning-Based Approach

Yunsen Wang et al.

Journal of Forensic Accounting Research2025https://doi.org/10.2308/jfar-2024-003article
AJG 2ABDC B
Weight
0.44

Abstract

Predicting financial statement fraud is a critical task for auditors and forensic accountants. Machine learning offers valuable support in this endeavor. Incorporating emerging technologies in accounting, this study employs deep learning algorithms to design a financial statement fraud detection system. Following the design science research paradigm, the study develops a framework and assesses its efficacy through prototype evaluation. The results demonstrate that the deep learning-based financial statement fraud prediction system achieves notably high prediction accuracy compared with existing methods. Moreover, the system possesses the capability of predicting specific types of fraud. The user-friendly nature of the system facilitates its adoption by auditors and forensic accountants in practical settings. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: C45; C53; M41; M42.

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https://doi.org/https://doi.org/10.2308/jfar-2024-003

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@article{yunsen2025,
  title        = {{Financial Statement Fraud Prediction System: A Deep Learning-Based Approach}},
  author       = {Yunsen Wang et al.},
  journal      = {Journal of Forensic Accounting Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/jfar-2024-003},
}

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Financial Statement Fraud Prediction System: A Deep Learning-Based Approach

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.