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Identifying Qualified Auditors' Opinions: A Data Mining Approach

Efstathios Kirkos · Charalambos T. Spathis · Αλέξανδρος Νανόπουλος · Yannis Manolopoulos

44 citations0/0 files storedJournal of Emerging Technologies in Accounting

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Auditing, Earnings Management, Governance Data Mining and Machine Learning Applications Financial Distress and Bankruptcy Prediction

Abstract

Data Mining methods can be used in order to facilitate auditors to issue their opinions. Numerous of these methods have not yet been tested on the purpose of discriminating cases of qualified opinions. In this study, we employ three Data Mining classification techniques to develop models capable of identifying qualified auditors' reports. The techniques used are C4.5 Decision Tree, Multilayer Perceptron Neural Network, and Bayesian Belief Network. The sample contains 450 publicly listed, nonfinancial U.K. and Irish firms. The input vector is composed of one qualitative and several quantitative variables. The three developed models are compared in terms of their performance. Additionally, variables that are associated with qualified reports and can be used as indicators are also revealed. The results of this study can be useful to internal and external auditors and company decision-makers.

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References

2000Does size matter? The influence of large clients on office-level auditor reporting decisions 1993Recognizing Financial Distress Patterns Using a Neural Network Tool 2002Detecting false financial statements using published data: some evidence from Greece 1996The Role of Economic Trade-Offs in the Audit Opinion Decision: An Empirical Analysis 2005The Impact of Managing Director Changes and Financial Distress on Audit Qualification and Auditor Switching 2002A roadmap for future neural networks research in auditing and risk assessment 1995The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*1991Empirical Analysis of Audit Uncertainty Qualifications

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2020Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks 2021Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction 2015Integrating corporate governance and financial variables for the identification of qualified audit opinions with neural networks 2012Identifying qualified audit opinions by artificial neural networks

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