Using Hybrid Artificial Intelligence and Machine Learning Technologies for Sustainability in Going-Concern Prediction

Der-Jang Chi,Zong-De Shen

doi:10.3390/su14031810

Abstract

The going-concern opinions of certified public accountants (CPAs) and auditors are very critical, and due to misjudgments, the failure to discover the possibility of bankruptcy can cause great losses to financial statement users and corporate stakeholders. Traditional statistical models have disadvantages in giving going-concern opinions and are likely to cause misjudgments, which can have significant adverse effects on the sustainable survival and development of enterprises and investors’ judgments. In order to embrace the era of big data, artificial intelligence (AI) and machine learning technologies have been used in recent studies to judge going concern doubts and reduce judgment errors. The Big Four accounting firms (Deloitte, KPMG, PwC, and EY) are paying greater attention to auditing via big data and artificial intelligence (AI). Thus, this study integrates AI and machine learning technologies: in the first stage, important variables are selected by two decision tree algorithms, classification and regression trees (CART), and a chi-squared automatic interaction detector (CHAID); in the second stage, classification models are respectively constructed by extreme gradient boosting (XGB), artificial neural network (ANN), support vector machine (SVM), and C5.0 for comparison, and then, financial and non-financial variables are adopted to construct effective going-concern opinion decision models (which are more accurate in prediction). The subjects of this study are listed companies and OTC (over-the-counter) companies in Taiwan with and without going-concern doubts from 2000 to 2019. According to the empirical results, among the eight models constructed in this study, the prediction accuracy of the CHAID–C5.0 model is the highest (95.65%), followed by the CART–C5.0 model (92.77%).

Highlights

Publisher’s Note: MDPI stays neutralIn recent years, in the competitive market environment, many enterprises have been bankrupt, causing serious losses to financial statement users and public investors, and the going-concern doubts of enterprises have received increased attention
As stated in SAS No 57 of Taiwan [10], in the case of existing material uncertainties related to events or conditions that may cast significant doubts on audited enterprises’ abilities to continue as going concerns, according to the risk assessment as required by IFRSs, certified public accountants (CPAs) shall issue reports in accordance with the statement on auditing standards
If a company fails to fully disclose going-concernrelated matters in notes to their financial statements or if CPAs determine that these matters are not fully disclosed, CPAs will issue a qualified opinion or an adverse opinion

Summary

Introduction

In the competitive market environment, many enterprises have been bankrupt, causing serious losses to financial statement users and public investors, and the going-concern doubts of enterprises have received increased attention. If certified public accountants (CPAs) or auditors fail to give audit opinions on going-concern doubts before corporate bankruptcy, they will cause significant damages to themselves or their firms [1]. In the face of mass data and the age of artificial intelligence (AI), the Big Four accounting firms (Deloitte, KPMG, PwC, and EY) pay more and more attention to big data, AI, and machine-learning technologies. The complex process to assess whether enterprises have going-concern doubts promotes the development of going-concern prediction models, which auditors can construct with regard to jurisdictional claims in published maps and institutional affiliations

Objectives

Methods

Results

Discussion

Conclusion