Revisiting the choice model of Ponzi and Pyramid schemes: analysis of case law

Nolasco, Claire Angelique; Vaughn, Michael S.; Carmen, Rolando V. del

doi:10.1007/s10611-013-9456-8

Cited by 18 publications

(4 citation statements)

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“…While it is possible that many of the participants in the prior example Fomo3D were aware of the risks, there are numerous other smart Ponzi schemes that entice unsuspecting investors under the pretense of an investment plan with high returns 16 . Additionally, some smart Ponzi schemes (i.e., hidden smart Ponzi schemes do not provide source code 17 , in which case even experts are unable to determine whether the scheme is a smart Ponzi scheme. It is vital to research the identification method of a smart Ponzi scheme given the rapid development of blockchain technology, the bulk of users involved lack of professional understanding, and the relatively lax supervision.…”

Section: Smart Ponzi Schemes and Identificationmentioning

confidence: 99%

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Onu,

Omolara,

Alawida

et al. 2023

Sci Rep

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Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like Nigeria, particularly due to the high poverty rate. Many individuals have fallen victim to these scams, resulting in significant financial losses. Despite efforts to detect Ponzi schemes using various methods, including machine learning (ML), current techniques still face challenges, such as deficient datasets, reliance on transaction records, and limited accuracy. To address the negative impact of Ponzi schemes, this paper proposes a novel approach focusing on detecting Ponzi schemes on Ethereum using ML algorithms like random forest (RF), neural network (NN), and K-nearest neighbor (KNN). Over 20,000 datasets related to Ethereum transaction networks were gathered from Kaggle and preprocessed for training the ML models. After evaluating and comparing the three models, RF demonstrated the best performance with an accuracy of 0.94, a class-score of 0.8833, and an overall-score of 0.96667. Comparative evaluations with previous models indicate that our model achieves high accuracy. Moreover, this innovative work successfully detects key fraud features within the Ponzi scheme dataset, reducing the number of features from 70 to only 10 while maintaining a high level of accuracy. The main strength of this proposed method lies in its ability to detect clever Ponzi schemes from their inception, offering valuable insights to combat these financial threats effectively.

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Section: Smart Ponzi Schemes and Identificationmentioning

confidence: 99%

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Onu,

Omolara,

Alawida

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…There are examples in the literature of scammers offering returns of up to 40% within six to eight weeks, or 100% within a month (Frankel, 2012). On the other hand, the scheme perpetrated by Bernard Madoff, supposedly the largest Ponzi scheme ever, offered investors modest but steady returns of 10-12% per year, regardless of market conditions (Nolasco et al, 2013).…”

Section: Investment Scams: Sham Business Ventures and Ponzi Schemesmentioning

confidence: 99%

Financial Fraud: A Literature Review

Reurink

2018

Journal of Economic Surveys

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This paper describes the empirical universe of financial fraud as it has been documented in the academic literature. Specifically, it describes the different forms of fraudulent behaviour in the context of financial market activities, the prevalence and consequences of such behaviour as identified by previous research, and the economic and market structures that scholars believe facilitate it. To structure the discussion, a conceptual distinction is made between three types of financial fraud: false financial disclosures, financial scams, and financial mis‐selling. The findings of the literature review highlight a number of recent developments that scholars think have facilitated the occurrence of financial fraud, including (1) the development of new fundamental conflicts of interest and perverse incentive structures in the financial industry; (2) an influx of unsophisticated, gullible participants in the financial marketplace; (3) the increasing complexity involved in financial market transactions as a result of rapid technological, legal, and financial innovation and an ever‐widening menu of financial products; (4) an increase in the use of justified secrecy in the form of a mystification of trading models adopted by fund managers.

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“…Despite the profound and pervasive impact of pyramid schemes (Button and Cross, 2017;Shen, 2020), researchers know very little about why some people participate in pyramid schemes. Limited research is largely American and specific to certain forms of pyramids, especially illegal multi-level marketing (MLM) schemes (Liu, 2018;Nolasco et al, 2013;Walsh, 2016). This paper aims to contribute to this small body of research by investigating why people join pyramid schemes.…”

Section: Introductionmentioning

confidence: 99%

Why do people join pyramid schemes?

Hock

Button²

2022

JFC

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Purpose Pyramid schemes create an unusual situation, where the victims might be effectively turned into offenders, as their role is to recruit more victims to the pyramid scheme. This paper aims to investigate the prevalence of pyramid schemes, their modern forms and why people join them. Design/methodology/approach This paper has developed from a structured literature review carried out as part of a wider study into policing pyramid schemes. Findings This paper identifies a range of reasons why people join pyramid schemes. Some of these reasons are “participant dominant”, including the vision of high reward for little work and the attraction to a better lifestyle. Other reasons are “organiser dominant”, including the exploitation of specific groups and high-pressure sales. These findings suggest significant differences in levels of culpability of pyramid schemes victims and perpetrators. This complexity is accompanied by conceptual, regulatory and institutional challenges. Originality/value Despite the profound and pervasive impact of pyramid schemes, researchers know very little about why some people participate in pyramid schemes. Limited research is largely American and specific to illegal multi-level marketing schemes. To the best of the authors’ knowledge, this paper is the first to provide an overview of a mix of strategies of participants of pyramid schemes to recruit new victims and reasons why people are joining pyramid schemes.

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Revisiting the choice model of Ponzi and Pyramid schemes: analysis of case law

Cited by 18 publications

References 50 publications

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Financial Fraud: A Literature Review

Why do people join pyramid schemes?

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