This article was first published on Dr. Craig Wright’s blog, and we republished with permission from the author. Read Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7, Part 8, Part 9, Part 10, Part 11, Part 12, Part 13, Part 14, Part 15, Part 16, Part 17, and Part 18.
It is necessary to note that the hype given to blockchain-based solutions can lead to false claims of solving problems that are structured differently than the researcher notes. For instance, Zhao et al. (2023) argue that blockchain technology will solve fraud within supply chains. While Zhao et al. (2023, p. 2) note that “it is possible to fraud [sic] by informational deviation, recording false, or others, which dues to not accurate or timely verify the financing information,” they overemphasize the immutability of data, ignoring the ability to enter false information. The paper falsely notes that an unalterable log source will mitigate all fraud, while not noting that significant frauds are currently committed using blockchain-based technology (Corbet, 2022).
Kumar (2022, p. 1) presents an analogous approach, noting that “Blockchain was formally originated to prevent forward in digital currency exchanges.” Such a claim is demonstrably false, as digital currency exchanges were created subsequent to Bitcoin and the release of blockchain technology. The misunderstanding of the technology has been promoted through the perceived financial rewards associated with the technology, which may also be seen in analogous claims, such as those by Amponsah et al. (2022). Research from such authors integrates “Blockchain 3.0” and machine learning, despite the nascent state of the technology overall.
Finally, Esfandiari (2022) approached and analyzed the impact of blockchain technology as it could apply to the Mexican food supply-chain management industry. The paper discusses the concept of creating dynamic connections between network stakeholders to enable mitigating threats to food safety and reduce fraud. In part, the concept embeds the traceability of a blockchain while failing to understand the movement of goods and services. The problem with it and the other papers lies in using blockchain technology as a panacea for other ills—without understanding the nature of supply-chain problems. In some ways, they capture the problems within technology groups that fail to understand the systems they are producing.
Annotated Bibliography
Amponsah, A. A., Adekoya, A. F., & Weyori, B. A. (2022). A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology. Decision Analytics Journal, 4, 100122. https://doi.org/10.1016/j.dajour.2022.100122
Amponsah et al. (2022, p. 1) correctly (but only partially) introduce blockchain as a technology designed “to provide a permanent solution to the double spending problem,” yet ignore the prime purpose as a micropayment system and bypass the statement in the Bitcoin white paper noting, “A certain percentage of fraud is accepted as unavoidable” and “no mechanism exists to make payments over a communications channel without a trusted party” (Wright, 2008, p. 1). While it may seem interesting to attribute Bitcoin and blockchain technology to solutions of fraud in all instances, the fact remains that external technologies need to be implemented to achieve such an outcome, if indeed possible.
The paper begins by documenting issues with fraud, noting the cost to society attributable to healthcare fraud. The work extends previous papers by the authors by integrating machine learning technology. The preceding paper (Amponsah et al., 2022) introduces the concept of blockchain technology as a means to reduce fraud in the insurance and healthcare industries. Yet, it starts with the false assumption that fraud will correctly be entered as such, and that information concerning physical goods will not link to falsely applied goods and services. An example of an oversight by the authors would be to note how physicians can add bills inflating the cost to society and sometimes do so in collusion with patients (Pande & Maas, 2013). Such fraud would not be detectable merely because it is recorded on the blockchain.
The authors analyse a purported model of healthcare and fraud to create decision tree classification algorithms designed to determine fraudulent information associated with false medication and service claims. Yet, it is noted that the approach is premised on known patterns that can be detected and does not incorporate the activity of fraudulent agents into the analysis to model a system based not on an ideal but on the actions of individuals and corporations seeking to take money from others fraudulently. An example of the error made by the authors could be more clearly understood in an analysis of Enron.
Enron was a corporation that maintained records that an external accounting firm audited. Despite doing so and the accuracy of records, massive and detrimental frauds occurred. The presumption presented in this research is that blockchain technology alone will aid in determining fraud and, when coupled with machine learning algorithms, stop loss within the healthcare industry. Yet, the authors have placed undue faith in technology, rather than understanding the issue or the processes utilised in falsifying records. In particular, it is not the record itself that is the problem—but the lack of real-world connectivity to an honest event having occurred.
Esfandiari, S. (2022). The effect of blockchain technology on supply chain management: Its potential to prevent fraud and reduce risks to food safety and its effects on the relationships between supply chain actors in the Mexican food processing industry. 2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE), 179–183. https://doi.org/10.1109/TEMSCONEUROPE54743.2022.9801908
Esfandiari (2022, p. 179), in analyzing problems connecting food safety and supply-chain fraud, notes that “there is urgent need for research into resolving these issues.” The research incorporates questions about the barriers to adopting blockchain technology, and introduces the presumption that a blockchain could alter the nature of the principal-agent relationship in business systems. Unfortunately, the research mostly adopts terminology and hyped-up techniques, including blockchain-based ‘smart contracts,’ without incorporating any reason as to how they may lead to benefits.
The study methodology starts with analyzing the beliefs and views of individuals within the Mexican food industry. Unfortunately, the methodology conflates the measurement of views and understanding held by individuals with a quantitative measurement of technology in an industry, which undermines much of the approach taken by the author. Further, noting that “[c]osts are a major consideration in the adoption of blockchain” (2022, p. 180) adds no value to the analysis and does not document the cost against the alternatives.
The methodology aims to answer six separate hypotheses, presented as quantitative while being analyzed as a qualitative research program. In analyzing the eight participants, the author sought to argue that using SPSS changed it from a qualitative investigation to a quantitative one—merely because statistical software had been deployed. In the results, the pilot study presents a thematic analysis of the perceptions held by people in unrelated industries. Consequently, the study was more effective as a means to demonstrate the problems attributed to integrating new technologies and the misuse of information based on aligning outcomes with the hype cycle.
Zhao, H., Liu, J., & Zhang, G. (2023). Blockchain-driven operation strategy of financial supply chain under uncertain environment. International Journal of Production Research, 1–21. https://doi.org/10.1080/00207543.2023.2190816
Zhao et al. (2023) have presented a concept of using a blockchain to reduce fraud as it applies to the logistics industry. While the authors correctly note how fraud increases costs and reduces trust, the paper assumes that accurate logging or immutable information will negate fraud. The research investigates the influence of financial models on enterprise analogy, and extends the scenario into examining how different business models will be impacted in terms of both price and quality when the cost of producing a product is increased through the added inclusion of loss by fraud.
The research includes an investigation into various factors associated with risk transfer, and models how they change based on the inclusion of different financial business models. The literature review by Zhao et al. (2023) details several research topics investigating loans and transfer pricing problems where deception has been a factor. Yet, the inclusion of assumptions that trust and fraud can be mitigated in the supply-chain industry undermines any value in the model. The research places “technical value in the aspects of transparency, trust or decentralization” (Zhao et al., 2023, p. 17) without documenting how it will reduce risk.
While mitigating risk and fraud is very interesting, the authors falsely assume that merely including a blockchain in a technical solution will create trust. Rather, the discussion models an idealized exchange where all transfers are accurate, and fraud is no longer a concern merely because data cannot be changed. Such an approach has not investigated the “garbage in” phenomena associated with data or considered that physical products could differ from what was recorded within a database. Consequently, the paper is more noteworthy in demonstrating the problems associated with the hype surrounding blockchain technologies, rather than in its analysis of fraud and creating a model of an idealized world.
References
Amponsah, A. A., Adekoya, A. F., & Weyori, B. A. (2022). A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology. Decision Analytics Journal, 4, 100122. https://doi.org/10.1016/j.dajour.2022.100122
Corbet, S. (Ed.). (2022). Understanding cryptocurrency fraud: The challenges and headwinds to regulate digital currencies. De Gruyter.
Esfandiari, S. (2022). The effect of blockchain technology on supply chain management: Its potential to prevent fraud and reduce risks to food safety and its effects on the relationships between supply chain actors in the Mexican food processing industry. 2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE), 179–183. https://doi.org/10.1109/TEMSCONEUROPE54743.2022.9801908
Kumar, Y. (2022). AI Techniques in Blockchain Technology for Fraud Detection and Prevention. In Security Engineering for Embedded and Cyber-Physical Systems. CRC Press.
Pande, V., & Maas, W. (2013). Physician Medicare fraud: Characteristics and consequences. International Journal of Pharmaceutical and Healthcare Marketing, 7(1), 8–33. https://doi.org/10.1108/17506121311315391
Wright, C. S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3440802
Zhao, H., Liu, J., & Zhang, G. (2023). Blockchain-driven operation strategy of financial supply chain under uncertain environment. International Journal of Production Research, 1–21. https://doi.org/10.1080/00207543.2023.2190816
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