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Article
Publication date: 7 October 2019

Vasant Raval and Vivek Raval

This paper aims to analyze the attributes of Ponzi schemes (“Ponzis”) to determine whether they are a unique class of financial fraud.

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Abstract

Purpose

This paper aims to analyze the attributes of Ponzi schemes (“Ponzis”) to determine whether they are a unique class of financial fraud.

Design/methodology/approach

The authors apply the disposition-based fraud model to classify and differentiate the attributes of Ponzis. This classification exercise helps comprehend the distinct drivers of Ponzis.

Findings

Fraud risk factors of Ponzis are different from those involved in other financial frauds. Four propositions about risk and risk mitigation measures are developed.

Research limitations/implications

The research approach used is conceptual, not empirical. However, the insights from this exercise should inform how different Ponzis are from other financial frauds and why they should be treated as a separate class for prevention and enforcement. In turn, this may trigger an interest in empirical research focused on the unique risks of Ponzis.

Practical implications

Knowledge of risk factors unique to Ponzis will permit a consideration of customized risk mitigation measures to prevent or detect Ponzis. Enforcement actions can also become more effective because of a distinct risk-based classification of Ponzis.

Social implications

The prevention of damage from Ponzis hinges upon how well prospective victims are educated to become aware of signs of Ponzis. This should lead to the more effective protection of investors from victimization from Ponzi schemes.

Originality/value

The implicit understanding that all financial frauds are alike and that the risk-factors involved are substantially the same across all classes of fraud is challenged. This revelation opens opportunities to add value through focused research on Ponzis as a distinct class of fraud.

Details

Journal of Financial Crime, vol. 26 no. 4
Type: Research Article
ISSN: 1359-0790

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