Application of the fraud triangle model in mitigating tax evasion in developing countries: a conceptual framework
Year: 2025
Type: article
Abstract: Since the seminal work of Allingham and Sandmo in 1972, tax evasion has been firmly established as a complex and multifaceted global issue. This study posits that one practical approach to mitigate tax evasion in developing countries is to utilise Cressey’s (1953) fraud triangle model as a framework for analysing taxpayers’ decisions. The theory explains why individuals commit fraud, including tax evasion, through three key factors: pressure, opportunity, and rationalisation. The study used the theoretical research method (literature review) as postulated by Alm (2011) to examine and explain the tax evasion phenomenon. The findings suggest that utilising the fraud triangle model (in the context of new technology such as artificial intelligence, machine learning, data mining and deep learning) will be beneficial to policymakers. First, it will assist tax authorities in profiling taxpayers by focusing on behaviours and other factors (economic and non-economic) that influence tax evasion. Next, with this framework, tax authorities can formulate advanced risk management strategies that directly tackle the fundamental elements of the Fraud Triangle, thereby improving their efforts against tax evasion. Furthermore, it offers invaluable guidance to lawmakers and policymakers, preparing them to develop strong legislation aimed at narrowing the tax gap from aggressive tax avoidance and evasion schemes. Finally, the framework can illuminate broader economic or industry-wide circumstances that increase the overall risk of tax evasion, facilitating proactive measures before difficulties arise. Thus, integrating the fraud triangle framework into the tax administration process can improve the efficiency and effectiveness of tax authorities and contribute to fairer tax systems in developing countries.
Author: Emmanuel Kojo Oseifuah
Institution: University of Venda
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
Funder: University of Venda
OpenAlex ID: W4410036566