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Identifying Fraudulent Financial Reports: Verification Between the M-Score Model and the Auditor’s Opinion

Author

Listed:
  • Nguyen Anh Phong

    (University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam)

  • Phan Huy Tam

    (University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam)

  • Ngo Phu Thanh

    (University of Economics and Law, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam)

Abstract

[Purpose] This study examines and forecasts financial reporting fraud in listed enterprises using the M-score model and auditor opinions based on the fraud triangle model. [Methodology] Research data was collected from non-financial enterprises listed on the HSX and HNX exchanges from 2018-2022. This study uses today’s popular machine learning methods to evaluate the performance of models to have a basis for recommendations (machine learning methods such as ANN, KNN, Decision Tree, and Random Forest) and gradient boosting algorithms (XGBoost and LightGBM). These methods help make decisions more accurately and help financial managers identify fraudulent financial reporting of companies early. This is consistent with requirements in management science and decision sciences. [Findings] The ANN model for the M-Score achieved the highest accuracy (97.9%) and F1-score (0.979). In comparison, the Decision Tree model was most effective for auditor opinions with an accuracy of 82.1% and an F1-score of 0.831. Additionally, the XGBoost algorithm consistently delivered strong results across both models, with an F1-score of 0.984 for M-Score and 0.942 for auditor opinions. [Originality/Value] In this article, this study relies on the fraud triangle theory, briefly finding the elements of the three factors from the fraud triangle model, combined with the auditor’s opinion on all financial statements. From there, predict whether a company has fraudulent financial statements or not. This way, this study combines the financial statement fraud theory with reality based on auditors’ comments. In addition, this study also compares the traditional forecasting method, M-score, to evaluate the performance of forecasting models. [Implications] The auditor opinion model holds practical value, integrating qualitative and quantitative insights for early fraud detection. [Limitations] Further empirical research is required to select indicators representing identifying signs in the fraud triangle model. The model based on auditors’ opinions holds significant reference value as it integrates qualitative and quantitative aspects, thereby combining theory with practical application.

Suggested Citation

  • Nguyen Anh Phong & Phan Huy Tam & Ngo Phu Thanh, 2024. "Identifying Fraudulent Financial Reports: Verification Between the M-Score Model and the Auditor’s Opinion," Advances in Decision Sciences, Asia University, Taiwan, vol. 28(4), pages 23-45.
    • Handle: RePEc:aag:wpaper:v:28:y:2024:i:4:p:23-45
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    References listed on IDEAS

    as
    1. Quick, Reiner & Pinto, Ines & Morais, Ana Isabel, 2020. "The Impact of the Precision of Accounting Standards on the Expanded Auditor’s Report in the European Union," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124766, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Nur Mazkiyani & Sigit Handoyo, 2017. "Audit Report Lag Of Listed Companies In Indonesia Stock Exchange," Jurnal Aplikasi Bisnis, Universitas Islam Indonesia, vol. 17(1), pages 77-95.
    3. Sigit Hermawan & Duwi Rahayu & Sarwenda Biduri & Ruci Arizanda Rahayu & Nur Amalina Nisfa Salisa, 2021. "Determining Audit Quality in the Accounting Profession with Audit Ethics as a Moderating Variable," Indonesian Journal of Sustainability Accounting and Management, Asian Online Journal Publishing Group, vol. 5(1), pages 11-22.
    4. Pinto, Inês & Morais, Ana Isabel & Quick, Reiner, 2020. "The impact of the precision of accounting standards on the expanded auditor’s report in the European Union," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 40(C).
    5. Normah Omar & Zulaikha ‘Amirah Johari & Malcolm Smith, 2017. "Predicting fraudulent financial reporting using artificial neural network," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 24(2), pages 362-387, May.
    6. Maria Krambia-Kapardis & Chris Christodoulou & Michalis Agathocleous, 2010. "Neural networks: the panacea in fraud detection?," Managerial Auditing Journal, Emerald Group Publishing, vol. 25(7), pages 659-678, July.
    7. Maria Krambia‐Kapardis & Chris Christodoulou & Michalis Agathocleous, 2010. "Neural networks: the panacea in fraud detection?," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 25(7), pages 659-678, July.
    8. repec:eme:maj000:02686901011061342 is not listed on IDEAS
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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