Bayesian inference of local government audit outcomes

Mongwe, Wilson Tsakane; Mbuvha, Rendani; Marwala, Tshilidzi

doi:10.1371/journal.pone.0261245

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…The results of the present study demonstrated that step-wise regression selects variables that capture the most variation in data better than those selected using random forest or correlation analysis. We further find that debt to operating ratio, current ratio and net operating surplus margin are the most important variables in predicting audit outcomes, which are in line with the results of [1,12].…”

Section: Resultssupporting

confidence: 86%

“…This is particularly important in South Africa where little research has been conducted, especially in the local government sphere. Our work expands on that of Mongwe and Malan [1] and Mongwe et al [12] in that we present a first-in-the-literature comparison of various machine learning algorithms and feature selection techniques for the task of analysing audit outcomes of South African municipalities. The results of this study could prove to be useful to the various users of financial statements, particularly as we also highlight which variables are important in discriminating between financial statements with qualified and unqualified audit opinions.…”

Section: Introductionmentioning

confidence: 88%

“…Mongwe et al [12] utilise a Bayesian framework for the prediction of South African audit outcomes using financial ratios as inputs. The authors consider a Bayesian logistic regression model with automatic relevance determination that was trained using the Metropolis Adjusted Langevin Algorithm, Metropolis-Hastings [20] and the No-U-Turn sampler and Separable Shadow Hamiltonian Hybrid Monte Carlo [21] Markov Chain Monte Carlo algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A sample of 1560 observations was obtained from audited financial statements of South African municipalities along with their audit outcomes over the period of 2012 to 2018. The audit opinions that can be issued by the Auditor General of South Africa on the financial statements of municipalities can be summarised as follows [4,9,12]:…”

Section: Data Descriptionmentioning

confidence: 99%

“…We use the same variables as in Mongwe and Malan [1], Mongwe et al [12], with the five number summary shown in [1,12] and the full description presented below:…”

mentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 88%

Section: Literature Reviewmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

“…We use the same variables as in Mongwe and Malan [1], Mongwe et al [12], with the five number summary shown in [1,12] and the full description presented below:…”

mentioning

confidence: 99%

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

et al. 2022

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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