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

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. ‎