Beyond the Fraud Triangle: Developing a Technology-Enabled Theory of Fraud in the Digital Accounting Era
DOI:
https://doi.org/10.54878/rdx9qz46Keywords:
Technology-Enabled Fraud Framework (TEFF), Socio-Technical Systems, Affordance Theory, Algorithmic Governance, Forensic Accounting, Accounting Information Systems.Abstract
Accounting environments increasingly rely on cloud systems, automated processes, distributed technologies, and artificial intelligence. Although established fraud models provide useful explanations of human behavior, they give limited attention to the role of technology in contemporary accounting systems. This study addresses this limitation by developing the Technology-Enabled Fraud Framework (TEFF), which incorporates technological conditions into the explanation of how financial misconduct can arise and be concealed. • Design/Methodology/Approach: The study draws on Socio-Technical Systems Theory, socio-materiality, and Affordance Theory to develop a four-dimensional framework from the forensic accounting and Accounting Information Systems literature. • Findings: The framework comprises four constructs: Digital Pressure, Technological Opportunity, Digital Rationalization, and Technological Capability. Each construct captures a different way in which digital systems may influence fraud-related behavior. Four propositions are developed to explain the proposed relationships among these dimensions and technology-enabled financial misconduct. • Theoretical & Practical Implications: The framework extends fraud theory by considering these conditions alongside human behavior and has implications for internal control and continuous auditing. • Originality/Value: The study offers a socio-technical perspective in which technology is considered an important component of contemporary fraud processes.
References
Abdo-Salloum, A. M., & Chehade, S. (2026). The role of artificial intelligence in transforming accounting and auditing practices: A systematic review. SAGE Open, 16(1), 1–13.
Ahmi, A. (2026). AI-driven auditing: Trends, themes, and research trajectories. International Journal of Accounting & Information Management, 34(2), 210–235.
Al-Sayyed, R. (2024). Machine learning and data mining architectures in financial fraud detection: A review. Computers & Security, 139, 103720.
Alshurafat, H. (2023). Factors affecting accounting students' misuse of ChatGPT: An application of the fraud triangle theory. Journal of Financial Reporting and Accounting, 22(2), 274–292.
Amani, F. A., & Fadlalla, A. M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32–58.
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big Data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27.
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2024). Sociotechnical considerations in the adoption of continuous audit analytics. Journal of Emerging Technologies in Accounting, 21(1), 45–62.
Albrecht, W. S., Albrecht, C. O., Albrecht, C. C., & Zimbelman, M. F. (2018). Fraud Examination (6th ed.). Cengage Learning.
Al Natour, M. R., et al. (2026). Fraud detection: Do psychological traits, auditor ethics and professional skepticism make any difference? International Journal of Organizational Analysis, 34(1), 45–68.
Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, 71(4), 443–465.
Bostrom, R. P., & Heinen, J. S. (1977). MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quarterly, 1(3), 17–32.
Bandara, E., Shetty, S., Mukkamala, R., & Liang, X. (2021). Data governance in decentralized systems: A blockchain perspective. IEEE Transactions on Network and Service Management, 18(4), 3980–3994.
Bandura, A. (1999). Moral disengagement in the perpetration of inhumanities. Personality and Social Psychology Review, 3(3), 193–209.
Bandura, A. (2002). Selective moral disengagement in the exercise of moral agency. Journal of Moral Education, 31(2), 101–119.
Brazel, J. F., & Dang, L. (2008). The effect of ERP system implementations on available internal control and financial statement audit fees. Journal of Information Systems, 22(2), 1–21.
Cho, S., Vasarhelyi, M. A., Sun, T., & Zhang, C. (2020). IT initiatives in accounting: A review of research in the Journal of Information Systems. Journal of Information Systems, 34(1), 1–25.
Cressey, D. R. (1953). Other People's Money: A Study in the Social Psychology of Embezzlement. Free Press.
Cybersecurity and Infrastructure Security Agency (CISA). (2026). Known Exploited Vulnerabilities and Enterprise Access Controls Catalog. U.S. Department of Homeland Security.
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of the firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13(1), 1–36.
Dellaportas, S. (2013). Conversations with inmate accountants: Motivation, opportunity and the fraud triangle. Accounting Forum, 37(1), 29–39.
Dornheim, S., & Zarnekow, R. (2025). Cybersecurity management: An empirical analysis of dynamic capabilities framework for enhancing cybersecurity intelligence. Information & Computer Security, 33(4), 473–495.
Dorminey, J., Fleming, A. S., Kranacher, M. J., & Riley, R. A. (2012). The evolution of fraud theory. Issues in Accounting Education, 27(2), 555–579.
Detert, J. R., Treviño, L. K., & Sweitzer, V. L. (2008). Moral disengagement in ethical decision making: A study of antecedents and outcomes. Journal of Applied Psychology, 93(2), 374–391.
Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Houghton Mifflin
Gepp, A., Linnenluecke, M. K., O'Neill, T. J., & Smith, T. (2018). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature, 40, 102–115.
Glan, V. (2026). Fraud triangle revisited: How pressure, opportunity, and rationalization have evolved. Journal of Financial Crime, 33(3), 512–528.
Kassem, R., & Higson, A. (2012). The new fraud triangle model. Journal of Emerging Trends in Economics and Management Sciences, 3(3), 191–195.
Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press.
Leonardi, P. M. (2011). When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147–167.
Leonardi, P. M. (2012). Materiality, sociomateriality, and infrastructure: What difference do the talk and the tools make? In Materiality and Organizing: Social Interaction in a Technological World (pp. 25–48). Oxford University Press.
Leonardi, P. M. (2013). Theoretical foundations for the study of sociomateriality. Information and Organization, 23(2), 59–76.
Li, H., Dai, J., Gershberg, T., & Vasarhelyi, M. A. (2018). Understanding usage and value of audit analytics for internal auditors: An organizational approach. International Journal of Accounting Information Systems, 28, 59–76.
Marks, J. T. (2012). The Mind Behind the Fraudsters Crime: Key Behavioral and Environmental Elements. Crowe Horwath LLP.
Majchrzak, A., & Markus, M. L. (2012). Technology affordances and constraints in management information systems (MIS). Encyclopedia of Management Theory, 832–836.
Markus, M. L., & Silver, M. S. (2008). A foundation for the study of IT effects: A new look at DeSanctis and Poole’s concepts of structural features and spirit. Journal of the Association for Information Systems, 9(10), 609–632.
Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic Process Automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10.
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. A. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209–234.
Mökander, J. (2023). Auditing of AI systems: A governance framework for assessing algorithmic risk. Minds and Machines, 33(2), 241–268.
Moore, C., Detert, J. R., Treviño, L. K., Baker, V. L., & Mayer, D. M. (2012). Why employees do bad things: Moral disengagement and unethical organizational behavior. Personnel Psychology, 65(1), 1–48.
Wolfe, D. T., & Hermanson, D. R. (2004). The Fraud Diamond: Considering four elements of fraud. The CPA Journal, 74(12), 38–42.
Orlikowski, W. J. (2007). Sociomaterial practices: Exploring technology at work. Organization Studies, 28(9), 1435–1448.
Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2(1), 433–474.
Qatawneh, A. M. (2025). The role of artificial intelligence in auditing and fraud detection in accounting information systems: Moderating role of natural language processing. International Journal of Organizational Analysis, 33(6), 1391–1409.
Schuchter, A., & Levi, M. (2016). The fraud triangle revisited. Security Journal, 29(2), 107–121.
Simeunović, N., Kamissoko, F. B., & Djurić, Z. (2023). Algorithmic opacity and systemic vulnerabilities in cloud-native enterprise systems. Computers in Human Behavior, 142, 107645.
Tieu, T. T. H. (2026). Integrating the fraud triangle with machine learning for financial misstatement detection: Evidence from an emerging market. Cogent Business & Management, 13(1), 2614367.
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big Data in accounting: An overview. Accounting Horizons, 29(2), 381–396.
Vousinas, G. L. (2019). Advancing theory of fraud: The S.C.O.R.E. model. Journal of Financial Crime, 26(1), 145–159.
Wang, T., & Cuthbertson, R. (2015). The eight value triggers of enterprise resource planning (ERP) systems. Information & Management, 52(4), 434–445.
Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2024). Dynamics of artificial intelligence adoption in accounting: A multi-theoretical lens. International Journal of Information Management, 74, 102711.
