Deceptive transparency and masked discourses in Ponzi schemes: a critical discourse analysis of MMM Nigeria

Chiluwa, Isioma M.; Kamalu, Ikenna; Anurudu, Steve

doi:10.1080/17405904.2020.1816481

Cited by 4 publications

(5 citation statements)

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“…Research has shown that some of the common features of fraudulent schemes are mouth-watering returns and no negative impacts 1 , 29 . Findings suggest that increased regulatory supervision with tight requirements for compulsory reporting of their activities will help to identify such schemes 30 .…”

Section: Related Workmentioning

confidence: 99%

“…The answer is; that Nigerians have shown that they long for high-risk and high-return investments, despite their mode of operation 32 . Supporting the above answer, Chiluwa et al 1 stated that it is due to a low amount of low-risk savings promised by the initiator of these schemes to its participants to go against inflation. Oyedeji et al 33 stated that many people fall victim to these Schemes because of the following reasons: They prefer investing their money in any investment that will double their invested money within a short period of time than working for it.…”

Section: Related Workmentioning

confidence: 99%

“…According to him, these Schemes will continue to scam people because of the inability of the monetary policy to deliver real risk-free-return investments, making the risk-return investments strive deeply in society. There is a need for better investor education and an effective detection model for the Security and Exchange Commission to do a better job of monitoring Ponzi schemes 1 . In line with the above statements, the SEC introduced the whistleblower bounty program.…”

Section: Related Workmentioning

confidence: 99%

“…The conviction of Bernard Madoff, an American investment adviser, and his 150-year prison sentence in 2009 for orchestrating a $65 billion Ponzi scheme, raised public concern about this form of online financial fraud 1 , 2 . It has also enticed some individuals to seek quick ways of making money 3 , 4 while increasing awareness of investment and securities fraud in the financial industry 2 .…”

Section: Introductionmentioning

confidence: 99%

“…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.The conviction of Bernard Madoff, an American investment adviser, and his 150-year prison sentence in 2009 for orchestrating a $65 billion Ponzi scheme, raised public concern about this form of online financial fraud 1,2 . It has also enticed some individuals to seek quick ways of making money 3,4 while increasing awareness of investment and securities fraud in the financial industry 2 .…”

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confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%