Effect of Ponzi schemes on a country: the case of Ghana

Mohammed, Umar

doi:10.1108/jfc-09-2020-0177

Cited by 4 publications

(14 citation statements)

References 9 publications

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“…Therefore, only four journal articles were identified that listed and discussed red flags for Ponzi schemes. These articles included, but were not limited to, work by Mohammed (2021, p. 928), Williams (2017, pp. 100–101), Kramer and Buckhoff (2012, p. 49) and Albrecht et al (2017, p. 259).…”

Section: Methodsmentioning

confidence: 99%

“…It becomes evident that Ponzi scheme red flags can, in principle, be grouped or categorised. This is supported by Mohammed (2021, p. 929), which discusses the five core elements that a Ponzi scheme may be considered to exhibit, viz. : “The benefit” or enticement to investors where it is promised that the investment will achieve high returns; “The setup”, which vaguely explains how these returns are achieved; “Initial Credibility” and “Initial Investors Paid Off”, where investor payments promote credibility and develop trust in the scheme (both being essential to ensuring that the scheme gains popularity); and “Communicated Successes” or word-of-mouth, which would attract more investors to sustain the scheme.…”

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 95%

“…: “The benefit” or enticement to investors where it is promised that the investment will achieve high returns; “The setup”, which vaguely explains how these returns are achieved; “Initial Credibility” and “Initial Investors Paid Off”, where investor payments promote credibility and develop trust in the scheme (both being essential to ensuring that the scheme gains popularity); and “Communicated Successes” or word-of-mouth, which would attract more investors to sustain the scheme. These elements play a role in the success of the Ponzi scheme (Mohammed, 2021, p. 929).…”

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 99%

“…From the journal articles selected, various red flags were noted to be associated with Ponzi schemes. Mohammed (2021, pp. 928–929) refers to and uses the SEC red flags list, which provides a good basis for a Ponzi scheme’s most commonly associated red flags (SEC, n.d.).…”

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 99%

“…Although Williams (2017, p. 100) listed additional red flags, he found that other researchers offered similar lists of red flags for Ponzi schemes. The seven SEC red flags (Mohammed, 2021, pp. 928–929) refers to are reflected in Table 5.…”

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 99%

See 4 more Smart Citations

A red flag checklist for cryptocurrency Ponzi schemes

Heyman

2023

JFC

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Purpose This study aims to, firstly, develop a red flag checklist for cryptocurrency Ponzi schemes and, secondly, to test this red flag checklist against publicly available marketing material for Mirror Trading International (MTI). The red flag checklist test seeks to establish if MTI’s marketing material posted on YouTube® (in the form of a live video presentation) exhibits any of the red flags from the checklist. Design/methodology/approach The study uses a structured literature review and qualitative analysis of red flags for Ponzi and cryptocurrency Ponzi schemes. Findings A research lacuna was discovered with regard to cryptocurrency Ponzi scheme red flags. By means of a structured literature review, journal papers were identified that listed and discussed Ponzi scheme red flags. The red flags from the identified journal papers were subsequently used in a qualitative analysis. The analyses and syntheses resulted in the development of a red flag checklist for cryptocurrency Ponzi schemes, with five red flag categories, containing 18 associated red flags. The red flag checklist was then tested against MTI’s marketing material (a transcription of a live YouTube presentation). The test resulted in MTI’s marketing material exhibiting 88% of the red flags contained within the checklist. Research limitations/implications The inherent limitations in the design of using a structured literature review and the lack of research regarding the cryptocurrency Ponzi scheme red flags. Practical implications The study provides a red flag checklist for cryptocurrency Ponzi schemes. The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags. Social implications The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags. Originality/value The study provides a red flag checklist for cryptocurrency Ponzi schemes.

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Section: Methodsmentioning

confidence: 99%

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 95%

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 99%

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 99%

Section: The Red Flag Checklist For Cryptocurrency Ponzi Schemesmentioning

confidence: 99%

See 3 more Smart Citations

A red flag checklist for cryptocurrency Ponzi schemes

Heyman

2023

JFC

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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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Victimisation of investors from fraudulent investment schemes and their protection through financial education

Singh¹,

Misra²

2022

JFC

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Purpose The purpose of this study is to identify the significant demographic and socio-economic characteristics of individuals who are likely to invest in a fraudulent investment scheme. It also quantifies the extent to which financial literacy helps in reducing the odds of investments in such schemes. Based on these findings, it provides policy recommendations to regulators and governments. Design/methodology/approach This study uses nationally representative data from the “India Assessment of Financial Capability 2018” survey. It further uses logistic regression with a binary outcome variable to assess the individual-level odds of investments in fraudulent investment schemes. Findings This study concludes that males between 40 and 59 years of age, who are well-educated (are at least graduates), score low in financial literacy, belong to the middle-income group, and SEC A3 households are most vulnerable to victimization by financial fraudulent investment schemes. It finds that financial literacy significantly reduces the odds of investment into fraudulent schemes to the extent of 39.118%. Originality/value This study quantifies the extent to which financial literacy helps in reducing the odds of individual investments in a fraudulent investment scheme. As financial literacy has a significant and negative relationship with the likelihood of investment in such schemes, this study provides policy interventions and recommendations to regulators and governments to safeguard the interest of individual investors.

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Effect of Ponzi schemes on a country: the case of Ghana

Cited by 4 publications

References 9 publications

A red flag checklist for cryptocurrency Ponzi schemes

A red flag checklist for cryptocurrency Ponzi schemes

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Victimisation of investors from fraudulent investment schemes and their protection through financial education

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