AI-Enabled CTAS and Digital Tax-Fraud Detection: A PLS-SEM Study in Indonesia

Authors

DOI:

https://doi.org/10.26877/asset.v7i4.2609

Keywords:

anomaly detection, ctas, digital governance, fraud detection, tax administration

Abstract

This study investigates the factors determining digital tax fraud based on the New Fraud Star Theory, with great emphasis on the moderating role of AI-empowered CTAS. Data were collected from 107 corporate taxpayers in Indonesia through a structured survey and analyzed using Partial Least Squares Structural Equation Modeling. The results indicated that System Pressure, Technological Capability, and External Digital Pressure significantly heightened fraud attempts, while Digital Opportunity, AI Rationalization, Cyber Arrogance, Internal IT Governance, and Techno-Culture were not significant. The model explained a substantial variance in the effectiveness of fraud detection with R² = 0.723. Moderation analysis showed that AI-powered CTAS significantly weakened the effects of System Pressure (X1×CTAS), Technological Capability (X4×CTAS), Internal IT Governance (X6×CTAS), and External Digital Pressure (X7×CTAS). These findings identify CTAS's strategic role in improving compliance by enabling real-time data integration, anomaly detection rules, and strengthened access control. Implications are that digital governance reforms should give full attention to the establishment of robust AI-empowered monitoring systems to minimize the risk of tax fraud effectively.

Author Biographies

  • Alif Faruqi Febri Yanto, State University of Malang

    Accounting Department, Faculty of Economics and Business, State University of Malang, Malang, Indonesia

  • Nuraini Sari, Bina Nusantara University

    Accounting Department, BINUS Online Learning, Bina Nusantara University, 11480 Jakarta, Indonesia

  • Defrina Eka Orchidta Ramadina, State University of Malang

    Accounting Department, Faculty of Economics and Business, State University of Malang, Malang, Indonesia

  • Tomy Prasetia, University of Western Australia

    University of Western Australia, 35 Stirling Hwy, Crawley WA 6009, Perth, Australia

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Published

2025-10-31

Issue

Vol. 7 No. 4 (2025): August-October

Section

Articles