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An Analysis of Local Government Financial Statement Audit Outcomes in a Developing Economy Using Machine Learning

Author

Listed:
  • Keletso Mabelane

    (School of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg 2000, South Africa)

  • Wilson Tsakane Mongwe

    (School of Electrical Engineering, University of Johannesburg, Auckland Park, Johannesburg 2000, South Africa)

  • Rendani Mbuvha

    (School of Statistics and Actuarial Science, University of Witwatersrand, Johannesburg 2000, South Africa)

  • Tshilidzi Marwala

    (School of Electrical Engineering, University of Johannesburg, Auckland Park, Johannesburg 2000, South Africa)

Abstract

Good financial management provides economic stability and sustainability to an organization. It enables an organisation to make good use of its resources and plan effectively. South Africa’s public financial management has deteriorated over time, with only 16% of municipalities receiving a clean audit in the 2020-21 financial period as reported by the Auditor General of South Africa. This work aims to find an appropriate model for analysing and predicting audit outcomes for South African municipalities. The data used in the study include 1560 observations of which 55% were unqualified audit opinions. The features used are 13 financial ratios obtained from financial statements from years 2012 to 2018. Feature selection is performed using random forest, correlation analysis and stepwise regression analysis. The performances of three machine learning algorithms are compared; decision tree, artificial neural network (ANN) and logistic regression models. The findings indicate that ANN is the appropriate model for predicting audit opinions in South African municipalities with overall average area under the receiver operating characteristic curve of 0.6918 and overall average area under the Precision–Recall curve of 0.7074 across all feature selection methods. In addition, debt to operating ratio, current ratio and net operating surplus margin are found to be the common three important financial ratios across the various feature selection techniques.

Suggested Citation

  • Keletso Mabelane & Wilson Tsakane Mongwe & Rendani Mbuvha & Tshilidzi Marwala, 2022. "An Analysis of Local Government Financial Statement Audit Outcomes in a Developing Economy Using Machine Learning," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:12-:d:1008600
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    References listed on IDEAS

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    1. repec:eme:maj000:02686900810890625 is not listed on IDEAS
    2. repec:eme:maj000:02686900410509802 is not listed on IDEAS
    3. Chyan-Long Jan, 2021. "Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    4. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
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