The Effect of Employing Artificial Intelligence Techniques in Fraud Risk Assessment: The Moderating Role of Audit Firm Size

Purpose: In recent decades, Artificial Intelligence (AI) has become more prevalent in many accounting disciplines, including auditing. AI enables auditors to analyze massive amounts of data and promptly identify inconsistencies and sequences in audits. This study aimed to examine the effect of employing AI techniques by Jordanian external auditors in fraud risk assessment (FRA), the moderating effect of audit firm size. Design/methodology/approach: The descriptive analytical approach was adopted by mailing 280 electronic questionnaires to certified auditors working in auditing firms that operated in Jordan. A total of 196 valid questionnaires were answered and analyzed, representing 70%. To test the study hypotheses, we relied on multiple and hierarchical regression models. Findings: The findings show that AI techniques are highly useful in assessing fraud risk at the reporting level, assertion level for transaction classes, account balances, and disclosures, as well as the risk of management override controls, because there is a direct and positive relationship between them, and audit firm size has a significant direct effect on FRA. Research limitations/implications: One of the main implications of the current study is the importance of employing AI techniques by Jordanian external auditors when evaluating fraud risk. Using such techniques will increase audit quality. Conversely, the respondents' knowledge about AI concepts may be imprecise sometimes, which may limit their awareness of AI techniques. Originality/value: This study contributes to the academic literature by providing empirical evidence of the effects of using AI techniques in fraud assessment, a region with limited prior research in this area. The findings expand our awareness of how AI techniques can influence fraud detection.