Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Development of LDA Algorithm-Based Financial Technology Roadmap Using Patent Data

  • Koopo KWON (Department of Shipping and Air Cargo & Drone Logistics, Youngsan University) ;
  • Kyounghak LEE (IACF, Kwangwoon University)
  • Received : 2024.08.05
  • Accepted : 2024.09.05
  • Published : 2024.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study aims to derive a technology development roadmap in related fields by utilizing patent documents of financial technology. To this end, patent documents are extracted by dragging technical keywords from prior research and related reports on financial technology. By applying the TF-IDF (Term Frequency-Inverse Document Frequency) technique in the extracted patent document, which is a text mining technique, to the extracted patent documents, the Latent Dirichlet Allocation (LDA) algorithm was applied to identify the keywords and identify the topics of the core technologies of financial technology. Based on the proportion of topics by year, which is the result of LDA, promising technology fields and convergence fields were identified through trend analysis and similarity analysis between topics. A first-stage technology development roadmap for technology field development and a second-stage technology development roadmap for convergence were derived through network analysis about the technology data-based integrated management system of the high-dimensional payment system using RF and intelligent cards, as well as the security processing methodology for data information and network payment, which are identified financial technology fields. The proposed method can serve as a sufficient reason basis for developing financial technology R&D strategies and technology roadmaps.

Keywords

Acknowledgement

The present Research has been conducted by the Research Grant of Kwangwoon University in 2023.

References

  1. Ahmed, A. S., Kumar, M., & Ali, M. A. M. (2020). Adoption of FinTech and Future Perspective: An Empirical Evidence from Bahrain on Digital Wallets. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 751-755).
  2. Aizawa, A. (2003). An information-theoretic perspective of tf-idf measures. Information Processing & Management, 39(1), 45-65.
  3. Awotunde, J. B., et al. (2021). Fintech with Artificial Intelligence, Big Data, and Blockchain. In Springer (pp. 107-139).
  4. Blei, D. M., & Ng, A. Y. (2003). Latent Dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022.
  5. Das, S. (2019). Opportunities and challenges of FinTech. Keynote Address Delivered at NITI Aayog's Fin-Tech Conclave, New Delhi, 25(3).
  6. Goldstein, I., Jiang, W., & Karolyi, G. A. (2019). To Fintech and Beyond. The Review of Financial Studies, 32(5), 1647-1661.
  7. Ji, J. L., & Qiu, E. (2002). Study on Quotation and Citation Form of Network Document [J]. Information Science, 1.
  8. Ko, M.-S. (2017). Unstructured Data Processing Using Keyword-Based Topic-Oriented Analysis. Journal of the Korea Information Processing Society, 6(11), 521-526.
  9. Lee, H., & Kang, P. (2018). Identifying core topics in technology and innovation management studies: A topic model approach. Journal of Technology Transfer, 43(5), 1291-1317.
  10. Lee, I. S., Jang, J. H., & Yoo, K. H. (2017). Twitter Data Analysis System using LDA model. Proceedings of the Korea Information Processing Society Conference, 24(2), 389-390.
  11. Park, Y., Yoon, B., & Lee, S. (2005). The idiosyncrasy and dynamism of technological innovation across industries: Patent citation analysis. Technology in Society, 27(4), 471-485.
  12. Pompella, M., & Costantino, L. (2021). Fintech and Blockchain Based Innovation: Technology Driven Business Models and Disruption. In Springer (pp. 403-430).
  13. Pradha, S., Halgamuge, M. N., & Tran Quoc Vinh, N. (2019). Effective text data preprocessing technique for sentiment analysis in social media data. In 2019 11th International Conference on Knowledge and Systems Engineering (KSE) (pp. 1-8). Da Nang, Vietnam.
  14. Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science & Technology, 60(10), 2107-2118.
  15. Yeo, J.-S., & Jeong, Y. (2020). Pathway toward market entry of perovskite solar cells: A detailed study on the research trends and collaboration networks through bibliometrics. Energy Reports, 6(2), 2075-2085.
  16. Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
  17. Zhang, H., Daim, T., & Zhang, Y. P. (2021). Integrating patent analysis into technology roadmapping: A latent Dirichlet allocation based technology assessment and roadmapping in the field of Blockchain. Technological Forecasting & Social Change, 167, 1-13.