Ponzi Scheme: Risk and Regulation in Indonesia

Yuspin, Wardah; Fadhlulloh, Qolbi Hanif

doi:10.47814/ijssrr.v5i10.599

Cited by 1 publication

(1 citation statement)

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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 . Obamuyi et al 31 , emphasized that after the demise of the Mavrodi Mundal Movement (MMM), which crashed in the year 2016/2017 with a total investment loss of 12 billion nairas on participants.…”

Section: Related Workmentioning

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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