Cited by 99Financial Literacy and BehaviorCorporate Governance and Financial ManagementAccounting and Financial Management

Testing the fraud triangle: a systematic review

Homer, Emily M.·2019·Journal of Financial Crime

Abstract

Purpose: The purpose of this study is to examine the existing literature on the fraud triangle. The fraud triangle framework, popularized by Donald Cressey and W. Steve Albrecht, has been used to explain financial crimes since the 1940s. The theory includes that workplace financial crime and fraud occurs only when an offender has sufficient opportunity, pressure and rationalization to commit the crime. The fraud triangle has been empirically applied to the array of criminal behaviors and specific financial crimes and offenders internationally to determine if all three elements are necessary for the crimes to occur. Design/methodology/approach: This systematic review summarized 33 empirical studies that have applied all three components of the fraud triangle to study financially criminal behavior committed by both corporations and individuals. The review included published and non-published papers and manuscripts from a variety of sources internationally. Findings: Of the 33 studies included, 32 found support for at least one element of the fraud triangle and 27 found support for all three elements. Overall, these studies have shown that the fraud triangle has generally received support across different subjects, industries and countries. Research limitations/implications: This research only examined papers using the “fraud triangle” term. Originality/value: This paper systematically reviewed different types of studies internationally, concluding that the fraud triangle is largely valid internationally as an explanation for financial crimes.

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Crossref · 10.1108/jfc-12-2018-0136

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  • AI-based techniques such as machine learning models for anomalous behavior detection and biometric analyses can quickly analyze large volumes of transactional data and detect and flag potentially fraudulent activity in real time.

    “Techniques based on AI, such as machine learning models for anomalous behavior detection and biometric analyses, can provide a way to quickly analyze large volumes of transactional data and detect and flag potentially fraudulent activity in real time (Bhatla et al. 2003; Jang-Jaccard and Nepal 2014; Homer 2020).”

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References 45 · Cited by 99

Authors
Homer, E. M.
Affiliations
University of Louisville (US)
Corresponding author
Homer, Emily M.
Venue
Journal of Financial Crime
Publisher
Emerald
Published
2019-12-19
Volume
27
Issue
1
Pages
172-187
ISSN
1359-0790 (ISSN-L)
OpenAlex
W3000486259
Cited by
99
References
45
Citations by year
2026: 12 · 2025: 29 · 2024: 15 · 2023: 20 · 2022: 12 · 2021: 8 · 2020: 3
FWCI
3.17
Type
journal article
Language
en
Open access
closed
Locations
Journal of Financial Crime (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Literacy and Behavior
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Corporate Governance and Financial Management
  • Social Sciences › Business, Management and Accounting › Accounting › Accounting and Financial Management
Keywords
fraud triangle, opportunity, pressure, rationalization, financial crime, workplace fraud, corporate fraud, fraud theory, fraud risk factors

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