Abstract
Financial technology, or FinTech, has recently attracted considerable attention both in the financial industry and academia. It covers a large range of technologies, including big data, cloud computing, and cryptocurrency, and is widely used in the finance industry. Despite the broad application of FinTech, little academic research has explored the development of this new wave of technological innovations. Our study aims to identify, classify, and track the development of FinTech innovations using patent data. A difficulty is that there are no accurate International Patent Classification (IPC) codes that we can refer to as FinTech innovations. Hence, in this paper we provide a comprehensive method for identifying FinTech patents. We first use a text-based filtering technique to locate potential FinTech patents and get a data set comprising 37,156 records. We then construct a training sample of FinTech patents by reading those patent files manually. Next, textual analysis and machine-learning techniques are applied to identify all FinTech patents in the whole data set, based on the initial sample. We classify FinTech patents into seven categories according to the key underlying technologies and track the development of each category. Thus a whole picture of FinTech innovations is formed.
Financial support is gratefully acknowledged from the Chinese National Natural Science Foundation (No. 71903189), the China Postdoctoral Science Foundation (No. 2019M660052).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The final list of filtering terms is available on request.
References
Wigglesworth, R.: Fintech: search for a super-algo. https://www.ft.com/content/5eb91614-bee5-11e5-846f-79b0e3d20eaf. Accessed 20 Jan 2016
Wen, S., Zhou, W., Zhang, J., Xiang, Y., Zhou, W., Jia, W.: Modeling propagation dynamics of social network worms. IEEE Trans. Parallel Distrib. Syst. 24(8), 1633–1643 (2013)
Zhang, J., Chen, C., Xiang, Y., Zhou, W., Xiang, Y.: Internet traffic classification by aggregating correlated naive Bayes predictions. IEEE Trans. Inform. Forensics Secur. 8(1), 5–15 (2013)
Zhang, Q., Yang, L., Chen, Z.: Privacy preserving deep computation model on cloud for big data feature learning. IEEE Trans. Comput. 65(5), 1351–1362 (2016)
Gai, K., Qiu, M., Sun, X.: A survey on FinTech. J. Netw. Comput. Appl. 103, 262–273 (2018)
Milian, E.Z., Spinola, M.D.M., de Carvalho, M.M.: Fintechs: a literature review and research agenda. Electron. Commer. Res. Appl. 34, 100833 (2019)
Chen, M.A., Wu, Q., Yang, B.: How valuable is fintech innovation? Rev. Financ. Stud. 32(5), 2062–2106 (2019)
Board, F.S.: Financial stability implications from FinTech: supervisory and regulatory issues that merit authorities’ attention, Basel, June 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, L., Lu, X., Yang, G., Shi, B. (2020). Identifying FinTech Innovations with Patent Data: A Combination of Textual Analysis and Machine-Learning Techniques. In: Sundqvist, A., Berget, G., Nolin, J., Skjerdingstad, K. (eds) Sustainable Digital Communities. iConference 2020. Lecture Notes in Computer Science(), vol 12051. Springer, Cham. https://doi.org/10.1007/978-3-030-43687-2_70
Download citation
DOI: https://doi.org/10.1007/978-3-030-43687-2_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-43686-5
Online ISBN: 978-3-030-43687-2
eBook Packages: Computer ScienceComputer Science (R0)