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The Literature Review Section

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The Literature Review Section

Introduction

Cryptocurrency swindling is increasingly becoming an international concern, with numerous sources indicating a rise in the occurrence and losses from cryptocurrency fraud (Trozze et al., 2022). Due to the rise in of cryptocurrency fraud there is need to research on common issues and ponzi schemes related to cryptocurrency and how the cases can be minimized. The literature review contained only research articles about ponzi schemes in relation to cryptocurrency. Factors that were reviewed by the research articles were blockchain fraud, pseudo-anonymity, fraudulent behavior, smart Ponzi contracts, Ethereum security, and top influencer on blockchain fraud. The literature review was conducted using EBSCO Host, Gale and ProQuest databases from the Monroe College Library. The search terms used to compile pertinent articles were blockchain fraud, Ponzi schemes in Ethereum, fraud in cryptocurrency, blockchain technology, and cryptocurrency and security.

Review of Literature

Ponzi Schemes on Ethereum

Galletta & Pinelli (2023) conducted a study about Ethereum blockchain between January 2015 and January 2019 in Italy. The researchers collected data on all Ethereum transactions during this period and filtered it to identify transactions that were associated with known Ponzi schemes. The study was completed with the purpose of improving the detection and prevention of Ponzi schemes and other types of financial fraud in the cryptocurrency market. The researchers hoped to learn more about the key factors that contribute to the success of Ponzi schemes in the cryptocurrency market and to develop a machine learning algorithm that could detect fraudulent activity in real-time. The researchers used a variety of data analysis and machine learning techniques to identify patterns and trends in the data, including clustering analysis, classification algorithms, and graph-based analysis. They found that there were several key features that were common to most Ponzi schemes on the Ethereum blockchain, including high rates of return, short-term contracts, and referral programs (Galletta & Pinelli, 2023). The study found that machine learning algorithms can be effective in detecting Ponzi schemes on the Ethereum blockchain, and that this approach could be used to improve the detection and prevention of financial fraud in the cryptocurrency market.

Similar observations were made by He et al. (2022) in a study in China about Ethereum blockchain. The study was completed with the purpose of developing a tool for identifying Ponzi schemes on the Ethereum blockchain. The researchers hoped to learn more about the characteristics of Ethereum-based Ponzi schemes and to develop a system that could automatically detect these schemes in real-time. The data used in the study was collected from the Ethereum blockchain between August 2017 and August 2018. The researchers collected data on all smart contracts deployed on the Ethereum blockchain during this period and filtered it to identify contracts that exhibited Ponzi scheme characteristics. The researchers used a variety of data analysis and machine learning techniques to identify patterns and trends in the data, including clustering analysis, feature selection, and classification algorithms. They found that Ethereum-based Ponzi schemes typically had a large number of participants, high rates of return, and used a multi-level referral system to attract new investors (He et al., 2022). The researchers developed a tool called CTRF (Contractor Trust Rank Factor) that uses machine learning algorithms to automatically detect Ethereum-based Ponzi schemes.

Prevention and detection of financial crime

Phan et al. (2019) researched the blockchain issue and what scams blockchain users endure when using such technology. The three authors are qualified to be in the study because they are members of Bryant University. Since several individuals and companies globally are moving to a new cashless payment method of crypto, the researchers wanted to discover the fraudulent problems involved. The authors purposed to review the issue of cryptocurrency and how enterprises are adopting this new method of making payments. Phan et al. (2019) conducted this research in the year 2018 after having to collect Twitter data from November to December. The research had no based geographical boundaries as the data in use came from various sources across the globe. The researchers used Twitter as a social media platform to gather data from tweets that mentioned the keywords of the study. The researchers wanted to contribute to the research surrounding blockchain technology and using bitcoin to make payments. The researchers were determining the kinds of scams that cryptocurrency users face when dealing with that means of payment. The research found that fraud in this new payment method was rampant in first-world nations that deeply use it.

Trozze et al. (2022) completed a study with the purpose of identifying the key risks and challenges associated with cryptocurrencies and financial crime and to explore potential solutions and best practices for addressing these challenges. The researchers hoped to learn more about the ways in which cryptocurrencies are being used for criminal activities, including money laundering, terrorist financing, and Ponzi schemes (Trozze et al., 2022). The study was conducted between 2013 and 2018. The data used in the study was drawn from various sources, like academic sources, government reports, industry publications, and case studies. The researchers analyzed this data to identify common themes and trends related to cryptocurrency and financial crime.

The study found that cryptocurrencies present significant risks and challenges to the prevention and detection of financial crime, particularly due to the anonymity and decentralization of blockchain technology. The researchers identified several areas where improvements are needed, including greater regulation and oversight, improved detection and monitoring capabilities, and enhanced collaboration between law enforcement agencies and the cryptocurrency industry.

Analysis of Literature

Cases of Ponzi Schemes in relation to Cryptocurrency continue to rise. Factors like lack of regulation, pseudonymity, new technology, hype and speculation, greed, and lack of transparency are some of the factors contributing to the rise in ponzi schemes. Both He et al. (2022) and Galletta & Pinelli (2023) used experimental methodology for the study. The other studies used in this literature review used a different form of research methodology to collect data, including observation and use of systematic review. Galletta & Pinelli (2023) used open source similar to He et al. (2022). On the other hand, Phan et al. (2019) used social media (Twitter) whereas Trozze et al. (2022) used publication databases. All the studies indicated that fraud in relation to cryptocurrency has been on the rise. While the findings from all studies pointed to the rise in crypto scams, they featured variations in their focus points. While Trozze et al. (2022) focused on classifying the frequency of different forms of crypto fraud, including Ponzi schemes and market manipulation, Galletta and Pinelli (2023) and He et al. (2022) on the other hand only focused on Ponzi schemes and smart Ponzi contracts associated with Ethereum. The study by Phan et al. (2019) was also different from the other research in that their findings featured coverage of scams and fraud in general. To reduce the cases of ponzi schemes in relation to cryptocurrency He et al. (2022), Phan et al. (2019), and Trozze et al. (2022) suggested that more research should be conducted to aid the understanding or detection, and prevention of crypto fraud whereas Galletta & Pinelli (2023) suggested a further step which was developing a model for classifying and detecting different forms of cryptocurrency fraud.

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