Description

• Title:
  FRAUD RISK AND AUDIT QUALITY: THE CASE OF US PUBLIC FIRMS
• Creator:
  Frikha Chaari, Hela;
  Belanès, Amel;
  Lajmi, Azhaar
• Description:
  The study raises questions about the fraud detection technique and the relevance of audit quality to mitigate fraud. The paper suggests a more comprehensive proxy for fraud risk that relies on the combination of Z-score and Beneish M-score. Basing on Logit, regressions are applied to a sample of 5,613 US-listed public firms. The study reveals that the existence of an internal auditor and independent members within the audit committee would potentially reduce the fraud risk. Hiring a Big external auditor and paying it high fees is also helpful. Findings show that, unlike the firm leverage, both firm profitability and growth opportunities have a negative effect of on fraud risk. Leverage provides a motivation for fraudulent financial reporting. It is important to note that this research underscores the audit’s monitoring role to mitigate fraud. Also, the adopted model helps regulators, bankers, managers and auditors to detect fraud at an early stage. So needed action can be taken at suitable time. Finally, in this study, we focus on financially distressed companies rather than those with financial restatements. We suggest a collective tool to predict fraud risk; which is expected to offer a more reliable proxy for fraud risk than do binary models.
• Object availability:
  free access
• Rights: https://creativecommons.org/licenses/by-nd/4.0
• Date:
  2022.06.20
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Copernican Journal of Finance & Accounting; Vol. 11 No. 1 (2022); 29-47;
  Copernican Journal of Finance & Accounting; Tom 11 Nr 1 (2022); 29-47;
  2300-3065;
  2300-1240
• Format:
  application/pdf
• Publisher:
  Uniwersytet Mikołaja Kopernika w Toruniu
• Relation: https://apcz.umk.pl/CJFA/article/view/38789/32532
• Subject:
  fraud risk;
  bankruptcy;
  audit quality;
  financial reporting;
  Beneish M-score model;
  Z-score model
• Identifier: https://apcz.umk.pl/CJFA/article/view/38789
• Data provider:
  Akademicka Platforma Czasopism
• Can I use it?:
  check license
• Type:
  text