Kadek Puteri Wulandari
Faculty of Economics and Business, State University of Semarang, Semarang, Indonesia

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Big data and artificial intelligence on fraud detection: the mediating role of fraud awareness Kadek Puteri Wulandari; Dhini Suryandari
Jurnal Akademi Akuntansi Vol. 9 No. 1 (2026): Jurnal Akademi Akuntansi (JAA)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jaa.v9i1.43599

Abstract

Purpose:  This research aims to examine the influence of Big Data and Artificial Intelligence on Fraud Detection and analyze the role of Fraud Awareness as a mediating variable in strengthening these relationships in modern auditing practices. Methodology/approach: The sample for this research consists of auditors working at Big Four Public Accounting Firms. The sampling method used a purposive sampling technique with a total of 65 respondents. This study uses primary data obtained from respondents’ questionnaire responses. The data analysis in this research employs the SmartPLS method using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. Findings:  The research results show that Big Data has a positive impact on fraud detection and fraud awareness, whereas Artificial Intelligence has no significant effect on either of these areas. Fraud awareness enhances fraud detection and partially mediates the effect of Big Data, but does not mediate the effect of Artificial Intelligence. Practical and Theoretical contribution/Originality: This research integrates the Fraud Triangle Theory and the Technology Acceptance Model (TAM) to explain that the effectiveness of audit technology sophistication in fraud detection depends not only on technological capability but also on auditors’ fraud awareness. Research Limitation:  This research is limited to a specific group of auditors, which restricts the generalizability of the findings. In addition, this study focuses solely on Big Data and Artificial Intelligence, and therefore does not consider other technological factors that may also influence the effectiveness of fraud detection.