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Trends in blockchain in finance: unveiling latent research topics using a structural topic modelling approach
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DOI：10.3389/fbloc.2026.1730387.png)
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Abstract
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Introduction Technological advances, such as blockchain, have revolutionized the finance sector by providing a secure, transparent, and efficient system for transactions.Methods The present study utilized the Latent Dirichlet Allocation (LDA) method to analyse 2,401 scholarly pieces using Python, to explore the research areas in the domain of finance.Results The results of the study show 15 important topics in blockchain and finance. Later, these topics were grouped into five clusters to identify future research avenues. We have also proposed research questions under each cluster aimed at filling the research gap.Discussion The study offers suggestions to future researchers by uncovering the existing gaps in the body of knowledge in blockchain in finance, along with documenting the emerging trends in the domain of finance.
Keywords:
blockchain
finance
Latent Dirichlet Allocation (LDA)
literature review
structural topic modelling (STM)
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IF:
2.4
Papers:
59
Citations:
558
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Cited Papers