A Review of Auditors’ GCOs, Statistical Prediction Models and Artificial Intelligence Technology


Auditors’ GCOs; Bankruptcy Prediction Models; decision trees; evolutionary approaches; rough sets; artificial intelligence techniques

How to Cite

Alareeni, B. (2019) “A Review of Auditors’ GCOs, Statistical Prediction Models and Artificial Intelligence Technology”, International Journal of Business Ethics and Governance, 2(1), pp. 16-29. doi: 10.51325/ijbeg.v2i1.30.


The main aim of this study is to give an overview of literature in accounting and finance regarding the performance of Auditors’ GCOs, Statistical Failure Prediction Models (SFPMs), and Artificial Intelligence Technology (AIT). The study reviews the accounting and finance literature regarding (SFPMs) and presents the most important types of SFPMs and AIT that have been developed to evaluate a company’s financial position from 1968 to date. The study focuses on studies that compare the relative performance of auditors’ GCOs with SFPMs and AIT. Our findings illustrated that SPFMs and AIT are better in predicting companies’ failure than auditors’ GCOs. We found that the prediction power of SFPMs is in many instances very high. Their accuracy differed from one model to another, depending on several factors such as industry, time period, and economic environment. The most commonly used and accurate models are the Altman models, logit models, and neural networks models, although overall the NNs models produce better results. We found that SFPMs and AIT can be very useful to users when assessing a company’s future position. Incorporating the use of SFPMs and AIT in the audit program can provide further evidence that the auditors exerted professional competence and due care. This study provides a comprehensive overview of research on Auditors’ GCOs, SFPMs, and AIT. The study provides a clear picture of the best tools used in failure/bankruptcy prediction in the last decades. Thus, it is an aid to future research in the area.  



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