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Computer Assisted Audit Techniques as a Moderating Variable on Factors Affecting Fraud Detection

In: Proceedings of the 3rd International Conference on Management and Business (ICOMB 2024)

Author

Listed:
  • Betri Betri

    (Universitas Muhammadiyah Palembang)

  • Ridho Hafidz

    (Universitas Muhammadiyah Palembang)

  • Gumulya Sonny Marcel Kusuma

    (Universitas Muhammadiyah Palembang)

  • M. Amien Dwi Putra

    (Universitas Muhammadiyah Palembang)

Abstract

This study uses computer-assisted audit techniques as a moderating variable to investigate the impact of big data, auditor religiosity, and task-specific knowledge on fraud detection. This study was carried out at the Financial and Development Supervisory Agency’s Representative Office in Sumatra and is an example of associative research. 220 questionnaires were acquired using a basic random sample technique, yielding primary data. The Structural Equation Modeling (SEM) approach was used for the analysis. According to the t test analysis, fraud detection is not significantly impacted by big data. Task-specific knowledge and auditor religiosity have a notable partial impact on fraud detection. Computer Assisted Audit Techniques do not moderate the effect of Auditor Religiosity and Task Specific Knowledge on Fraud Detection (homologizer moderator); nevertheless, they do moderate (strengthen) the effect of Big Data on Fraud Detection (pure moderator). With Computer Assisted Audit Techniques as a moderating variable, this study looks at the impact of Big Data, Auditor Religiosity, and Task Specific Knowledge on Fraud Detection. The results show a termination of 0.974 with a relatively low effect size f2 value.

Suggested Citation

  • Betri Betri & Ridho Hafidz & Gumulya Sonny Marcel Kusuma & M. Amien Dwi Putra, 2025. "Computer Assisted Audit Techniques as a Moderating Variable on Factors Affecting Fraud Detection," Advances in Economics, Business and Management Research, in: Alfiatul Maulida & Md. Mahmudul Alam & ⁠⁠Mark Gabriel Wagan Aguilar (ed.), Proceedings of the 3rd International Conference on Management and Business (ICOMB 2024), pages 325-334, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-698-7_34
    DOI: 10.2991/978-94-6463-698-7_34
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