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FinTech, Volume 3, Issue 2 (June 2024) – 3 articles

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28 pages, 7944 KiB  
Article
Systemic Risk and Bank Networks: A Use of Knowledge Graph with ChatGPT
by Ren-Yuan Lyu, Ren-Raw Chen, San-Lin Chung and Yilu Zhou
FinTech 2024, 3(2), 274-301; https://doi.org/10.3390/fintech3020016 - 16 May 2024
Viewed by 492
Abstract
In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT [...] Read more.
In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk. Full article
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25 pages, 4193 KiB  
Review
Analyses of Scientific Collaboration Networks among Authors, Institutions, and Countries in FinTech Studies: A Bibliometric Review
by Carson Duan
FinTech 2024, 3(2), 249-273; https://doi.org/10.3390/fintech3020015 - 17 Apr 2024
Viewed by 413
Abstract
Purpose: FinTech research has grown rapidly, but few studies have measured the levels of scientific collaboration among authors, institutions, and nations. This study aimed to reveal the status and levels of scientific collaboration in this field. The results will help scholars to [...] Read more.
Purpose: FinTech research has grown rapidly, but few studies have measured the levels of scientific collaboration among authors, institutions, and nations. This study aimed to reveal the status and levels of scientific collaboration in this field. The results will help scholars to combine their knowledge and resources to generate new ideas that may not have been possible if they worked alone and enable them to work more efficiently, resulting in higher-quality results for all parties. Design/methodology/approach: Research papers in the FinTech field indexed in the Web of Science databases from 1999 to 2022 were included in the research dataset. Using R-bibliometrix and VOS viewer (Visualisation of Similarities viewer), co-authorship networks were drawn. Additionally, some measures of the co-authorship network were assessed, such as the links, total link strength, total number of articles, total citations, normalized total citations, average year of publication, average citations, and average normalized normal citations. Beyond bibliometric analyses, this research gathers other statistics for analysis to gain further insights. Result: A total of 1792 publications were identified, and a number of these revealed an increase in the forms of collaboration, including collaboration among authors and institutions. Three lists of the most collaborative authors, institutions, and countries were compiled. The top authors, affiliations, and countries were ranked according to their total links, citations, average citations, and annual normalized citations. There were six distinct clusters of collaboration among authors, thirteen among affiliations, and eleven among countries. In terms of author collaborations, the links and total link strength had three nodes and four nodes, respectively. John Goodell, Chi-Chuan Le, and Shaen Corbet were the top three collaborative authors. In terms of affiliations, the two strength attributes were 8 and 12 nodes, with Sydney University, Hong Kong University, and the Shanghai University of Finance and Economics topping the list. In terms of collaboration among countries, these two attributes had 14 and 34 nodes. Three of the most collaborative countries were England, the People’s Republic of China, and the United States. Originality/value: In contrast with previous systematic literature reviews, this study quantitatively examines the collaboration status in the FinTech field on three levels: authors, affiliations, and countries. Full article
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13 pages, 280 KiB  
Article
Argumentation Schemes for Blockchain Deanonymisation
by Dominic Deuber, Jan Gruber, Merlin Humml, Viktoria Ronge and Nicole Scheler
FinTech 2024, 3(2), 236-248; https://doi.org/10.3390/fintech3020014 - 27 Mar 2024
Viewed by 478
Abstract
Cryptocurrency forensics have become standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, [...] Read more.
Cryptocurrency forensics have become standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, this implicitness complicates investigations. On the other hand, it can have far-reaching consequences for the rights of those affected. Argumentation schemes could remedy this untenable situation by rendering the underlying premises more transparent. Additionally, they can aid in critically evaluating the probative value of any results obtained by cryptocurrency deanonymisation techniques. In the argumentation theory and AI community, argumentation schemes are influential as they state the implicit premises for different types of arguments. Through their critical questions, they aid the argumentation participants in critically evaluating arguments. We specialise the notion of argumentation schemes to legal reasoning about cryptocurrency deanonymisation. Furthermore, we demonstrate the applicability of the resulting schemes through an exemplary real-world case. Ultimately, we envision that using our schemes in legal practice can solidify the evidential value of blockchain investigations, as well as uncover and help to address uncertainty in the underlying premises—thus contributing to protecting the rights of those affected by cryptocurrency forensics. Full article
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