A comparison of machine learning techniques with a qualitative response model for auditor's going concern reporting

M. Anandarajan, A. Anandarajan

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Audit reports can take the form of a non-going concern (clean) report or Going concern (financial distress) report. If a firm is facing going concern uncertainty problems the auditor has a further choice of issuing two types of audit reports, namely the modified report or the disclaimer report. The issuance of the wrong type of report can have consequences for the auditor. Prior studies have developed models in an attempt to predict the type of audit report that should be issued to clients. However, all these studies, without exception, focused on the decision whether to issue a non-going concern report or a going concern report. The present study extends this area of research by comparing three predictive models that can help facilitate the decision on the type of going concern report that should be issued. Two of the predictive models are on based machine learning techniques (Artificial Neural Networks and Expert Systems) while the third is a qualitative model (Multiple Discriminant Analysis). The validity of the models are tested by comparing their predictive ability of the type of audit report which should be issued to the client. The results of the study indicate that the artificial neural network model has a superior predictive ability in determining the type of going concern audit report that should be issued to the client.

Original languageEnglish (US)
Pages (from-to)385-392
Number of pages8
JournalExpert Systems with Applications
Volume16
Issue number4
DOIs
StatePublished - Jan 1 1999

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Keywords

  • Expert systems
  • Multiple discriminant analysis
  • Non-going concern

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