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Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach

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  • Jianrong Yao

    (School of Information Management and Engineering, Zhejiang University of Finance and Economics, Xiasha Higher Education Zone, Hangzhou 310018, Zhejiang, China)

  • Yanqin Pan

    (School of Information Management and Engineering, Zhejiang University of Finance and Economics, Xiasha Higher Education Zone, Hangzhou 310018, Zhejiang, China)

  • Shuiqing Yang

    (School of Information Management and Engineering, Zhejiang University of Finance and Economics, Xiasha Higher Education Zone, Hangzhou 310018, Zhejiang, China)

  • Yuangao Chen

    (School of Information Management and Engineering, Zhejiang University of Finance and Economics, Xiasha Higher Education Zone, Hangzhou 310018, Zhejiang, China)

  • Yixiao Li

    (School of Information Management and Engineering, Zhejiang University of Finance and Economics, Xiasha Higher Education Zone, Hangzhou 310018, Zhejiang, China)

Abstract

Identifying financial statement fraud activities is very important for the sustainable development of a socio-economy, especially in China’s emerging capital market. Although many scholars have paid attention to fraud detection in recent years, they have rarely focused on both financial and non-financial predictors by using a multi-analytic approach. The present study detected financial statement fraud activities based on 17 financial and 7 non-financial variables by using six data mining techniques including support vector machine (SVM), classification and regression tree (CART), back propagation neural network (BP-NN), logistic regression (LR), Bayes classifier (Bayes) and K-nearest neighbor (KNN). Specifically, the research period was from 2008 to 2017 and the sample is companies listed on the Shanghai stock exchange and Shenzhen stock exchange, with a total of 536 companies of which 134 companies were allegedly involved in fraud. The stepwise regression and principal component analysis (PCA) were also adopted for reducing variable dimensionality. The experimental results show that the SVM data mining technique has the highest accuracy across all conditions, and after using stepwise regression, 13 significant variables were screened and the classification accuracy of almost all data mining techniques was improved. However, the first 16 principal components transformed by PCA did not yield better classification results. Therefore, the combination of SVM and the stepwise regression dimensionality reduction method was found to be a good model for detecting fraudulent financial statements.

Suggested Citation

  • Jianrong Yao & Yanqin Pan & Shuiqing Yang & Yuangao Chen & Yixiao Li, 2019. "Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach," Sustainability, MDPI, vol. 11(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1579-:d:214176
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    References listed on IDEAS

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    1. Liu, Chengwei & Chan, Yixiang & Alam Kazmi, Syed Hasnain & Fu, Hao, 2015. "Financial Fraud Detection Model Based on Random Forest," MPRA Paper 65404, University Library of Munich, Germany.
    2. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
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    Cited by:

    1. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    2. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    3. Cebi, Selcuk & Karakurt, Necip Fazıl & Kurtulus, Erkan & Tokgoz, Bunyamin, 2024. "Development of a decision support system for client acceptance in independent audit process," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    4. KiJeon Nam & Pouya Ifaei & Sungku Heo & Gahee Rhee & Seungchul Lee & ChangKyoo Yoo, 2019. "An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    5. Dimitrios Kydros & Michail Pazarskis & Athanasia Karakitsiou, 2022. "A framework for identifying the falsified financial statements using network textual analysis: a general model and the Greek example," Annals of Operations Research, Springer, vol. 316(1), pages 513-527, September.
    6. Papík, Mário & Papíková, Lenka, 2022. "Detecting accounting fraud in companies reporting under US GAAP through data mining," International Journal of Accounting Information Systems, Elsevier, vol. 45(C).
    7. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).

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