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Data mining approach in detecting inaccurate financial statements in government-owned enterprises

Author

Listed:
  • Amra Gadžo

    (University of Tuzla)

  • Mirza Suljić

    (UNTZ - Univerity of Tuzla)

  • Adisa Jusufović

    (University of Tuzla)

  • Slađana Filipović

    (University of Tuzla)

  • Erna Suljić

    (Tuzla - Public Elementary School "Simin Han", Sarajac 4, 75207 Tuzla)

Abstract

The study aims to assess the capability of various data mining techniques in detecting inaccurate financial statements of government-owned enterprises operating in the Federation of Bosnia and Herzegovina (FBiH). Inaccurate financial statements indicate potential financial fraud. Prediction models of four classification algorithms (J48, KNN, MLP, and BayesNet) were examined using a dataset comprising 200 audited financial statements from government-owned enterprises under the supervision of the Audit Office of the Institutions in the Federation of Bosnia and Herzegovina. The results obtained through data mining analysis reveal that a dataset encompassing seven balance sheet items provides the most comprehensive depiction of financial statement quality. These seven attributes are: opening entry of accounts receivable, profit (loss) at the end of the period, operating assets at the end of the period, accounts receivable at the end of the period, opening entry of operating assets, short term financial investments at the end of the period, and opening entry of short-term financial investments. By employing these seven attributes, the MLP algorithm was implemented to construct the most precise predictive model, achieving a 76% accurate classification rate for financial statements. Leveraging the identified attributes, a mathematical model could potentially be formulated to effectively predict financial statements of government-owned enterprises in FBiH. This, in turn, could considerably facilitate the process of selecting GOEs for inclusion in the annual work plan of state auditors. Presently, due to resource constraints, government-owned enterprises in FBiH do not undergo regular annual scrutiny by state auditors, with only 10 to 15 such enterprises being subject to audits each year. The results of this research can also be beneficial to both the public and the Financial Intelligence Agency in the FBiH. The paper contributes to filling the gap in the literature regarding the applied methodology, particularly in the part concerning the attributes used in the research.

Suggested Citation

  • Amra Gadžo & Mirza Suljić & Adisa Jusufović & Slađana Filipović & Erna Suljić, 2025. "Data mining approach in detecting inaccurate financial statements in government-owned enterprises," Post-Print hal-04929522, HAL.
    • Handle: RePEc:hal:journl:hal-04929522
    DOI: 10.17535/crorr.2025.0001
    Note: View the original document on HAL open archive server: https://hal.science/hal-04929522v1
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    References listed on IDEAS

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    1. José Ramón Sánchez-Serrano & David Alaminos & Francisco García-Lagos & Angela M. Callejón-Gil, 2020. "Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks," Mathematics, MDPI, vol. 8(8), pages 1-14, August.
    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.
    3. Sevala Isakovic-Kaplan & Lejla Demirovic & Mahir Proho, 2021. "Benford’s Law in Forensic Analysis of Income Statements of Economic Entities in Bosnia and Herzegovina," Croatian Economic Survey, The Institute of Economics, Zagreb, vol. 23(1), pages 31-61, June.
    4. 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.
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