Data mining approach in detecting inaccurate financial statements in government-owned enterprises
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 o...
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| Language: | English |
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Croatian Operational Research Society
2025-01-01
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| Series: | Croatian Operational Research Review |
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| Online Access: | https://hrcak.srce.hr/file/473260 |
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| author | Amra Gadžo Mirza Suljić Adisa Jusufović Slađana Filipović Erna Suljić |
| author_facet | Amra Gadžo Mirza Suljić Adisa Jusufović Slađana Filipović Erna Suljić |
| author_sort | Amra Gadžo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8d7ba1351ffc425aa55d2d3814c61c4a |
| institution | Kabale University |
| issn | 1848-0225 1848-9931 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Croatian Operational Research Society |
| record_format | Article |
| series | Croatian Operational Research Review |
| spelling | doaj-art-8d7ba1351ffc425aa55d2d3814c61c4a2025-02-04T14:04:49ZengCroatian Operational Research SocietyCroatian Operational Research Review1848-02251848-99312025-01-0116111510.17535/crorr.2025.0001Data mining approach in detecting inaccurate financial statements in government-owned enterprisesAmra Gadžo0Mirza Suljić1Adisa Jusufović2Slađana Filipović3Erna Suljić4Faculty of Economics, University of Tuzla, Tuzla, BiHCenter for Quality Assurance and Internal Evaluation, University of Tuzla, Tuzla, BiHFaculty of Economics, University of Tuzla, Tuzla, BiHFaculty of Economics, University of Tuzla, Tuzla, BiHPublic Elementary School "Simin Han", Tuzla, BiHThe 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.https://hrcak.srce.hr/file/473260data miningfinancial statement fraudsgovernment-owned enterprisesprediction of financial statements accuracy |
| spellingShingle | Amra Gadžo Mirza Suljić Adisa Jusufović Slađana Filipović Erna Suljić Data mining approach in detecting inaccurate financial statements in government-owned enterprises Croatian Operational Research Review data mining financial statement frauds government-owned enterprises prediction of financial statements accuracy |
| title | Data mining approach in detecting inaccurate financial statements in government-owned enterprises |
| title_full | Data mining approach in detecting inaccurate financial statements in government-owned enterprises |
| title_fullStr | Data mining approach in detecting inaccurate financial statements in government-owned enterprises |
| title_full_unstemmed | Data mining approach in detecting inaccurate financial statements in government-owned enterprises |
| title_short | Data mining approach in detecting inaccurate financial statements in government-owned enterprises |
| title_sort | data mining approach in detecting inaccurate financial statements in government owned enterprises |
| topic | data mining financial statement frauds government-owned enterprises prediction of financial statements accuracy |
| url | https://hrcak.srce.hr/file/473260 |
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