Promise of AI in DeFi, a Systematic Review
- A systematic study of various recent research publications based on the use of artificial intelligence in decentralized finance.
- Insights to such research publications according to impact, reliability, and security. A relevance score calculated on the basis of year of publication, citation, and ranking of the publication platform.
- A trend analysis as to where DeFi could be heading with AI.
2. Materials and Methods
2.1. Technical Backgrounds
2.1.3. Decentralized Finance
- Title, abstract contains the term “AI” and “DeFi”.
- Only English article.
- Not book or book chapter
- Published from the year 2011 till 2021.
- Publicly available.
- Published in conferences or journals.
2.2.1. Grading Impact, Reliability, and Security
- Yes: All the points are met.
- Subject to experiment: Authors addressed the points partially, which can be overcome with more experiments.
- No: None of the points were addressed in the publication.
2.2.2. Scoring Relevancy of a Publication
- Conference (A*) and Journal (Q1):
- Conference (A) and Journal (Q2):
- Conference (B) and Journal (Q3):
- Conference (C) and Journal (Q4):
- Unranked: 0.0
2.2.3. Validity of Our Methodology
3. Literary Analysis
- Security concern remains the most persisting problem. This could be a major barrier to entry for DeFi itself, and with AI.
- Not enough subsistent experiments are being conducted to support applicability in financial institutions. Again, this could be a byproduct of security concerns which does not permit for such experiments.
- The relevance score does not necessarily imply the importance of the literature studied in this paper, but it can also give us a brief idea of where the knowledge of DeFi-AI is mostly based.
- Higher relevance score does not necessarily imply that the publication satisfies the criteria mentioned in Section 2.2.1. The relevance score is significant on the number of citations and the year of publication. The same is true for vice-versa.
- About 63% of the publications completely satisfied Impact, 16% satisfied Reliability, and 21% satisfied Security.
- Reliability and Security are mutually inclusive in the studied domain. Investing research on security will subsequently increase the reliability of the work.
- Compared to DeFi as a standalone entity, utilization of AI has proved to be more significant in driving integration and bridging the gap of reliability.
- Does AI’s utilization in the DeFi add value to the original purpose?
- Will the utilization of AI comply/compromise with security that is at stake?
- What will be the trade-off between the robustness (impact and reliability) and the trustworthiness (security) of the system (AI in DeFi).
- Google Scholar returns around 1000 results in our initial search, making it impossible to study owing to time constraints. As a result, we’ve chosen the first 50 pages. The author’s consensus determined the selection of 50 pages for surfing. If the surfing range is expanded, however, the analysis should be more thorough.
- Only one data source was used during the article selection process: Google Scholar. Other databases, such as Scopus and the Web of Science, as well as Google Scholar for article searching, will be examined in the near future.
5. Concluding Remarks
6. Related Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|DLT||Distributed Ledger Technology|
|||2021||Subject to experiments||No||Subject to experiments||NA|
|||2018||Yes||Subject to experiments||No||2.00|
|||2018||Yes||Subject to experiments||Subject to experiments||18.00|
|||2019||Yes||Subject to experiments||No||9.33|
|||2019||Subject to experiments||Subject to experiments||Yes||4.33|
|||2018||Subject to experiments||Subject to experiments||Subject to experiments||70.75|
|||2020||Yes||Yes||Subject to experiments||2.50|
|||2019||Yes||Yes||Subject to experiments||2.33|
|||2017||Subject to experiments||Subject to experiments||No||28.80|
|||2019||Yes||Subject to experiments||Yes||5.0|
|||2018||Subject to experiments||Subject to experiments||Subject to experiments||5.75|
|||2021||Yes||Subject to experiments||No||2.0|
|||2020||Subject to experiments||Subject to experiments||Yes||1.0|
|||2014||Yes||Subject to experiments||No||27.75|
|||2019||Yes||Subject to experiments||Yes||4.00|
|||2018||Yes||Subject to experiments||Yes||41.00|
|||2019||Yes||Subject to experiments||Subject to experiments||2.33|
|||2020||Subject to experiments||Subject to experiments||No||2.50|
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Sadman, N.; Ahsan, M.M.; Rahman, A.; Siddique, Z.; Gupta, K.D. Promise of AI in DeFi, a Systematic Review. Digital 2022, 2, 88-103. https://doi.org/10.3390/digital2010006
Sadman N, Ahsan MM, Rahman A, Siddique Z, Gupta KD. Promise of AI in DeFi, a Systematic Review. Digital. 2022; 2(1):88-103. https://doi.org/10.3390/digital2010006Chicago/Turabian Style
Sadman, Nafiz, Md Manjurul Ahsan, Abdur Rahman, Zahed Siddique, and Kishor Datta Gupta. 2022. "Promise of AI in DeFi, a Systematic Review" Digital 2, no. 1: 88-103. https://doi.org/10.3390/digital2010006