Ponzi Schemes and Crypto Currency
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Ponzi Schemes and Crypto Currency
The usage of Cryptocurrency and Ponzi schemes has been on the rise but, the high fluctuation rates and uncertainty make 83% of its users end up in unprecedented losses rendering them bankrupt (Galletta & Pinelli, 2023). Researchers have investigated ways of ensuring that losses due to the Ponzi schemes are ended once and for all. The rising swindling has gained global attention because of the methods used by scammers to steal from unsuspecting investors. The researchers discovered that when there is a massive investment in machine learning and detection platforms, all the Ponzi schemes and crypto fraud will be avoided. The strategies lower the rate of crypto losses and empower investors only to take part in legit businesses that would lead to profits in the discourse. Fraudulent crypto schemes have ravaged society, and investors are falling prey to untrustworthy traders; however, an installation of deep machine learning which would detect unusual activity and report the scam and make the investors safe at all times, the global community must therefore leverage machine learning and other software detection for safety (He et al., 2019).
Literature Review
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 cryptocurrency fraud, there is a need to research 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 about 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 used 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 CryptocurrencyCryptocurrency, blockchain technology, and CryptocurrencyCryptocurrency and security.
Review of Literature
Ponzi Schemes on Ethereum
Galletta & Pinelli (2023) conducted a study about the Ethereum blockchain between January 2015 and January 2019 in Italy. The researchers collected data on Ethereum transactions during this period and filtered it to identify transactions associated with known Ponzi schemes. The study was completed to improve the detection and prevention of Ponzi schemes and other types of financial fraud in the cryptocurrency market (Galletta & Pinelli, 2023). The researchers hoped to learn more about the key factors contributing to the success of Ponzi schemes in the cryptocurrency market and develop a machine-learning algorithm that could detect fraudulent activity in real-time. The researchers used various 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 several key features 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 effectively detect Ponzi schemes on the Ethereum blockchain, and this approach could be used to improve the detection and prevention of financial fraud in the cryptocurrency market (Galletta & Pinelli, 2023).
Similar observations were made by He et al. (2022) in a study in China about the Ethereum blockchain. The study was completed to develop 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 (He et al., 2022). 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 various data analysis and machine learning techniques to identify patterns and trends in the data, including clustering analysis, feature selection, and classification algorithms (He et al., 2022). They found that Ethereum-based Ponzi schemes typically had many participants and high rates of return and used a multi-level referral system to attract new investors (He et al., 2022). The researchers developed a CTRF (Contractor Trust Rank Factor) tool that uses machine learning algorithms to detect Ethereum-based Ponzi schemes automatically.
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 payment method. 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 (Phan et al., 2019). 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 intensely use it.
Trozze et al. (2022) completed a study to identify 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 how cryptocurrencies are 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 were 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. Trozze et al. (2022) found that cryptocurrencies present significant risks and challenges to preventing and detecting financial crime, mainly 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 (Trozze et al., 2022).
Analysis of Literature
Cases of Ponzi Schemes about cryptocurrency continue to rise. Factors like lack of regulation, pseudonymity, new technology, hype and speculation, greed, and lack of transparency contribute 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 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 about 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) also differed from the other research in that their findings featured coverage of scams and fraud in general. To reduce the cases of Ponzi schemes about 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. Prevention of crypto fraud, whereas Galletta & Pinelli (2023) suggested a further step: developing a model for classifying and detecting different forms of cryptocurrency fraud.
Discussion
Several sources indicate a surge in the incidence and losses from bitcoin fraud, causing cryptocurrency scams to become a global problem (Galletta & Pinelli, 2023). The need to examine prevalent problems and Ponzi schemes connected to cryptocurrencies and how Studies might mitigate the instances has arisen due to the surge in bitcoin fraud. Like He et al. (2022), Galletta and Pinelli (2023) utilized open source (2022). Trozze et al. (2022) used published databases, whereas Phan et al. (2019) used social media (Twitter). According to every study, there has been an increase in bitcoin fraud. Even though all research conclusions indicated increased cryptocurrency fraud, their target areas varied. Different studies present proposals for dealing with fraud in crypto. Some authors have identified deep learning techniques to help deal with feature engineering concerns in this topic (Galletta & Pinelli, 2023). Other researchers propose that a detection platform for Ponzi contracts may be suitable to reduce the chances of conning investors (He et al., 2019). Future research will also require the use of a more fantastic source of data compared to the current one used by Phan et al. (2019).
Evidence-Based Recommendation
Galletta and Pinelli (2023) recommended that researchers should expand the solution model in many ways in further work. The researchers should also consider the bytecode of contracts available in the dataset but not utilized by his classifier and employ deep learning techniques to decrease the feature engineering work. Future research should enhance the process for optimizing the optimal collection of features. Phishing is one of the most promising frauds on Ethereum that may be identified similarly using this method (Phan et al., 2019). According to He et al. (2022), Ponzi schemes can be detected on Ethereum-CTRF. This method extracts smart code contracts' word and sequence features to form a dataset (He et al., 2022). Detecting these contracts using Ethereum-CTRF improves the recall compared to the other detection methods.
The knowledge of how fraudulent actions are carried out and how to spot and stop them has to be expanded via more study. The frequency of these terms may be studied over a more extended period using time-series analysis (Phan et al., 2019). Research should reveal trends when comparing how individuals talked about scams and other fraudulent actions to when these activities occurred (Phan et al., 2019). It is also vital to remember that fraud detection and prevention are supported by blockchain technology. Unfortunately, there are some fraudulent behaviors that the technology also encounters due to weaknesses or scams. It may be erroneous to infer whether words are good or harmful when we analyze them and base our analysis on their rating value.
Conclusion
Fraudulent crypto schemes have ravaged society, and investors are falling prey to untrustworthy traders; however, an installation of deep machine learning which would detect unusual activity and report the scam and make the investors safe at all times, the global community must therefore leverage machine learning and other software detection for safety (He et al., 2019). As witnessed in the intervention programs implemented in Italy, crypto frauds can be easily eliminated when the countries invest in machine learning and detection software to help investors determine the probability of being scammed. The invention enabled different economic sites to install it and reduced the fraud rate to protect the investors. When the country implemented the system, fraud cases fell from 83% to 21%, which signaled a positive outcome (Galletta & Pinelli, 2023). The cases of crypto fraud have been increasing exponentially globally as many people are influenced to invest in the Ponzi schemes. However, the people will be protected when a policy similar to the one implemented in Italy is put in place in the country as well. Investment platforms that have not invested in machine learning to detect scammers are ten times more likely to drive unsuspecting investors to losses. Investment in technology, especially artificial intelligence, to detect the schemes is the antidote to lower the negative impacts caused by the Ponzi schemes. The usage of Cryptocurrency and Ponzi schemes has been on the rise but, the high fluctuation rates and uncertainty make 83% of its users end up in unprecedented losses rendering them bankrupt (Galletta & Pinelli, 2023).
References
Galletta, L., & Pinelli, F. (2023). Sharpening Ponzi schemes detection on Ethereum with machine learning.
arXiv preprint arXiv:2301.04872. https://arxiv.org/pdf/2301.04872.pdf
He, X., Yang, T., & Chen, L. (2022). CTRF: Ethereum-based Ponzi contract identification.
Security and Communication Networks,
2022. https://doi.org/10.1155/2022/1554752
Phan, L., Li, S., & Mentzer, K. (2019). Blockchain technology and the current discussion on fraud.
Information Systems and Analytics Journal Articles. Paper 28. https://digitalcommons.bryant.edu/cgi/viewcontent.cgi?article=1027&context=cisjou\
Trozze, A., Kamps, J., Akartuna, E. A., Hetzel, F. J., Kleinberg, B., Davies, T., & Johnson, S. D. (2022). Cryptocurrencies and future financial crime.
Crime Science,
11, 1-35. https://doi.org/10.1186/s40163-021-00163-8
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