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Do data mining techniques assist auditors in predicting high-risk accounts in MENA Region countries?

Author

Listed:
  • Wafaa Salah Mohamed
  • Lamiaa Fattouh Ibrahim
  • Moid Uddin Ahmad

Abstract

This study aims to construct a model that increases the accuracy of forecasting qualified audit opinions using publicly available measures and artificial intelligence. Additionally, the study probes the financial variables affecting an auditor's propensity to issue a qualified audit report. This study investigated the predictive abilities of three models: binary logistic regression, random forest, and decision tree. The study examined 564 audit reports (282 qualified reports) from nine MENA region countries from 2012 to 2018. The random forest technique produces the most accurate audit prediction. The study found that the significant firm-level variables that affect auditor opinion are book value per share, client size, and leverage ratio. The study's findings will bolster auditors, policymakers, and managers in effective decision-making.

Suggested Citation

  • Wafaa Salah Mohamed & Lamiaa Fattouh Ibrahim & Moid Uddin Ahmad, 2023. "Do data mining techniques assist auditors in predicting high-risk accounts in MENA Region countries?," Afro-Asian Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 13(5), pages 673-692.
    • Handle: RePEc:ids:afasfa:v:13:y:2023:i:5:p:673-692
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