政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111579

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111579

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政大機構典藏 > 商學院 > 企業管理研究所（MBA學位學程） > 學位論文 > Item 140.119/111579

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題名:	運用文字探勘技術分析金融科技之發展與趨勢 Applying text mining techniques to the development and trends of fintech`s patent
作者:	郝紹君 Hao, Shao Chun
貢獻者:	洪為璽 Hung, Wei Hsi 郝紹君 Hao, Shao Chun
關鍵詞:	文字探勘金融科技專利趨勢分析機會探索關鍵圖 Text mining Fintech Patent trend analysis Chance discovery KeyGraph
日期:	2017
上傳時間:	2017-07-31 11:32:31 (UTC+8)
摘要:	現今科技日新月異，不斷突破創新，產業環境變動的步調也越來越快，新竄出之金融科技(Finance Technology)的應用，使得許多企業越加注重技術方面的研發創新，尤其，善加運用專利資訊能有效節省研發經費與時間。因此如何有效運用專利是企業維持競爭優勢不可或缺的一環。有鑑於此，本研究搜集近年各國專利資料庫之專利資料，將資料分為三個時期，並區分申請中與已申請之專利資料，透過文字探勘技術與機會探索分析出金融科技之發展與趨勢，了解各時期詞彙間之關聯性與差異，再搭配視覺化工具KeyGraph，以描繪出金融科技領域之相關詞彙關聯趨勢圖，挖掘未來潛在趨勢。本研究之結果了解金融科技在各時期的趨勢發展變化與尋求脈絡，以及過去各時期之專利佈局，因而從結果中發現金融科技之發展方向主體為支付領域，許多支付科技接連出現在三個時期中。然而近幾年，其他金融領域如投資、融資、保險、資料分析等也漸漸浮出，從本研究之第三個時期的高頻字詞高達34個可看出，可見金融科技之專利發展佈局已快速從支付領域拓展至其他金融領域。本研究所挖掘出之潛在趨勢顯示了未來金融科技領域中將會有五大重點發展領域，分別為服務整合領域之雲端科技、支付領域之生物辨識與穿戴支付與加密貨幣、資料分析領域之機器學習與人工智慧、信息收集與處理領域之遠程信息處理科技、以及理財投資領域之理財機器人。期望本研究結果能幫助企業，在面臨新科技不斷衝擊產業，而產業不斷尋求創新發展之下，能夠快速檢閱目前市場趨勢，藉此釐清並改善自身之發展策略，以因應外部環境之變動，提供企業作為金融科技發展之策略參考，也能有助於企業釐清與制定金融科技之投資方向，以擁有持續的競爭優勢。 Nowadays, with the rapid advancement of information technologies, the changes of business environment and the way to deal with the changes are becoming faster and faster. The development and adoption of new financial technologies has made many enterprises pay more attention to the research and development (R&D) initiatives. Besides, making good use of patent information can effectively save the budget and time of R&D, so how to effectively use patent information is an indispensable part for enterprises to maintain their competitive advantages. This study collected the patent data from the national patent database, and divided the data into three periods, and distinguished the data between the applying and the applied patents. Through the text mining techniques and chance discovery, this study explored the development and trends of financial technology and also aimed to understand the relevance and differences between the major terms in each period. Then, with the visual tool, KeyGraph, this study illustrated the associations between related terms, and proposed the potential future trends based on the graphs. The results of this study help monitor the changes of the trends and financial technology’s development in the three periods, and understand the patent portfolios in each period. This study has found that the main direction of financial technology’s development is the payment field. Many technologies related to payment have successively appeared in the three periods. However, in recent years, other financial areas such as investment, financing, insurance, data analysis and other areas are gradually emerging, since we found 34 high-frequency terms in the third period. This also shows that the development of financial technology’s patent portfolios has expanded from payment to other financial areas. The potential trends of financial technology’s development in this study are five areas, namely, technologies of cloud, biometric and wearable payment and cryptocurrency, machine learning and artificial intelligence, telematics technology, and robo-advisors. It is expected that this study can serve as a reference for the development of financial technology, and help enterprises be able to quickly review their current market trends, clarify and improve their own R&D strategies to respond to the changes in the external environment. Also, it is hoped that the results can help enterprises clarify and develop their own investment directions to maintain competitive advantages.
參考文獻:	References Altuntas, S., & Dereli, T. (2014). A novel approach based on DEMATEL method and patent citation analysis for prioritizing a portfolio of investment projects. Expert Systems With Applications, 42(3), 1003-1012. http://dx.doi.org/10.1016/j.eswa.2014.09.018 Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based on patent data. Technological Forecasting And Social Change, 96, 202-214. http://dx.doi.org/10.1016/j.techfore.2015.03.011 Ark Speier, Brain Hughes,Dennis Fortnum, Ian Pollari, Warren Mead. (2017). The Pulse of Fintech, Q3 2016, KPMG, Retrieved 19 June 2017. Arner, D. W., Barberis, J. N., & Buckley, R. P. (2015). The Evolution of Fintech: A New Post-Crisis Paradigm? (2015/047). Hong Kong. Ashton, W. B., and Sen, R. K. (1988), “Using Patent Information in Technology Business Planning-II”, Research Technology Management, November-December, 31(6), 42-46. Boldrini, L., & Giorgino, M. (2017). An explorative study on Robo advisory and digital trends in the asset management industry. POLITESI. Retrieved 23 June 2017, from https://www.politesi.polimi.it/handle/10589/133337 Chen, L. C., Yu, T. J., & Hsieh, C. J. (2013). KeyGraph-based chance discovery for exploring the development of e-commerce topics. Scientometrics, 95 (1), 257-275. Daim, T., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting And Social Change, 73(8), 981-1012. http://dx.doi.org/10.1016/j.techfore.2006.04.004 Dhar, Vasant and Stein, Roger M., (December 14, 2016). FinTech Platforms and Strategy, MIT Sloan Research Paper No. 5183-16. Available at SSRN: https://ssrn.com/abstract=2892098 Fayyad, U. M., Pitatesky-Shapiro, G., Smyth, P., & Uthurasamy, R. (1996). Advances in knowledge discovery and data mining, AAAI/MIT Press. Feldman, R., & Dagan, I. (1995, August). Knowledge Discovery in Textual Databases (KDT). In KDD (pp.112-117). Canada. Groshoff, David and Urien, Kurtis R. and Nguyen, Alex, (July 31, 2014). Crowdfunding 6.0: Does the SEC`s FinTech Law Failure Reveal the Agency’s True Mission to Protect — Solely Accredited — Investors? Available at SSRN: https://ssrn.com/abstract=2483371 or http://dx.doi.org/10.2139/ssrn.2483371 Hall, Browyn H., Griluchesm Zvi, Hausman, Jerry A. (1986), “Patents and R and D: Is There A Lag?”, International Economic Review, 27(2), 265-283. Hauke, S., & Leker, J. (2016). Using Startup Communication for Opportunity Recognition — an approach to identift future products trends. World Scientific Publishing Company. Hong, C. F. (2009). Qualitative chance discovery–Extracting competitive advantages. Information Sciences, 179 (11), 1570-1583. Hotho, A., Nuernberger, A., & Paaß, G. (2005). A Brief Survey of Text Mining. Journal for Language Technology and Computational Lingustics, from https://www.kde.cs.uni-kassel.de/hotho/pub/2005/hotho05TextMining.pdf Julian Skan, Richard Lumb, Samad Masood, Sean K. Conway. (2017). The Boom in Global Fintech Investment, Accenture, Retrieved 17 June 2017. Wu J., K. Ota, M. Dong and C. Li, (2016), "A Hierarchical Security Framework for Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities," In IEEE Access, vol. 4, no., pp. 416-424. doi: 10.1109/ACCESS.2016.2517321 Kim, G., & Shim, M. (2016). Study on the Mobile FinTech Vacant Technology using Patent Analysis. 학술교육원 eArticle. Kim, J., Hwang, M., Jeong, D., & Jung, H. (2012). Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Systems With Applications, 39(16), 12618-12625. http://dx.doi.org/10.1016/j.eswa.2012.05.021 Koo, J. M., & Cho, S. B. (2005). Interpreting chance for computer security by viterbi algorithm with edit distance. New Mathematics and Natural Computation, 1(3), 421-433. Lee, C., Jeon, J., & Park, Y. (2011). Monitoring trends of technological changes based on the dynamic patent lattice: A modified formal concept analysis approach. Technological Forecasting And Social Change, 78(4), 690-702. http://dx.doi.org/10.1016/j.techfore.2010.11.010 Lee, S., Kim, M., Park, Y., & Kim, C. (2016). Identification of a technological chance in product-service system using KeyGraph and text mining on business method patents: International Journal of Technology Management: Vol 70, No 4. Inderscienceonline.com. Retrieved 24 June 2017, from http://www.inderscienceonline.com/doi/pdf/10.1504/IJTM.2016.075884 Lee, S., Yoon, B., Lee, C., & Park, J. (2009). Business planning based on technological capabilities: Patent analysis for technology-driven roadmapping, Technological Forecasting And Social Change, 76(6), 769-786. http://dx.doi.org/10.1016/j.techfore.2009.01.003 Li, G., Dai, J.S., Park, EM. et al. J Comput Virol Hack Tech (2017). A study on the service and trend of Fintech security based on text-mining: focused on the data of Korean online news. doi:10.1007/s11416-016-0288-9 Lin, Tom C. W., Infinite Financial Intermediation (January 5, 2016). Wake Forest Law Review, Vol. 50, No. 643, 2015; Temple University Legal Studies Research Paper No. 2016-06. LI, S., & WONG, K. (2016). Educational data mining using chance discovery from discussion board. Repository.lib.ied.edu.hk. Retrieved 24 June 2017, from http://repository.lib.ied.edu.hk/jspui/handle/2260.2/20402 Manning, C. D. (1999). Foundations of statistical natural language processing. MA: MIT press. Mogee, M. E. (1991), “Using Patent Data For Technology Analysis And Planning”, Research Technology Management, 34(4), 43. M. Y. Day and C. C. Lee, "Deep learning for financial sentiment analysis on finance news providers," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, 2016, pp. 1127-1134. Ohsawa, Y., Benson, N. E., & Yachida, M. (1998, April). KeyGraph: Automatic indexing by co-occurrence graph based on building construction metaphor. In Proceedings of the IEEE International Forum on Research and Technology Advances in Digital Libraries. (pp. 12-18). Ohsawa, Y. (2002a). Chance discoveries for making decisions in complex real world. New Generation Computing, 20(2), 143-163. Ohsawa, Y. (2002b). KeyGraph as Risk Explorer in Earthquake–Sequence. Journal of Contingencies and Crisis Management, 10(3), 119-128. Ohsawa, Y., & Fukuda, H. (2002). Chance discovery by stimulated groups of people. Application to understanding consumption of rare food. Journal of Contingencies and Crisis Management, 10(3), 129-138. Ohsawa, Y., & McBurney, P. (2003). Chance discovery. Heidelberg: Springer. Schueffel, Patrick (2016-03-09). "Taming the Beast: A Scientific Definition of Fintech". Journal of Innovation Management. 4 (4): 32–54. ISSN 2183-0606 Seo, JH. & Park, EM. Wireless Pers Commun (2017). A Study on Financing Security for Smartphones Using Text Mining. doi:10.1007/s11277-017-4121-7 Seongyong Choi & Sunghae Jun (2014) Vacant technology forecasting using new Bayesian patent clustering, Technology Analysis & Strategic Management, 26:3, 241-251, DOI:10.1080/09537325.2013.850477 Sun PC., Hong CF., Kuo TF., Iqbal R. (2017) Chance Discovery in a Group-Trading Model ─ Creating an Innovative Tour Package with Freshwater Fish Farms at Yilan. Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science, vol 10192. Springer, Cham Tsai, Y., Huang, Y., & Yang, J. (2016). Strategies for the development of offshore wind technology for far-east countries – A point of view from patent analysis. Renewable And Sustainable Energy Reviews, 60, 182-194. http://dx.doi.org/10.1016/j.rser.2016.01.102 Yoon, B., & Park, Y. (2003). A text-mining-based patent network: Analytical tool for high-technology trend, ScienceDirect. Sciencedirect.com. Retrieved 7 July 2017, from http://www.sciencedirect.com/science/article/pii/S1047831003000439 Wang, H., & Ohsawa, Y. (2012). Idea discovery: A scenario-based systematic approach for decision making in market innovation. Expert Systems with Applications, 40(2), 429-438. Walch, Angela, (March 16, 2015). The Bitcoin Blockchain as Financial Market Infrastructure: A Consideration of Operational Risk. 18 NYU Journal of Legislation and Public Policy 837 (2015). Available at SSRN: https://ssrn.com/abstract=2579482 Wu, M., Chang, K., Zhou, W., Hao, J., Yuan, C., & Chang, K. (2015). Patent Deployment Strategies and Patent Value in LED Industry. PLOS ONE, 10(6), e0129911. http://dx.doi.org/10.1371/journal.pone.0129911
描述:	碩士國立政治大學企業管理研究所(MBA學位學程) 104363041
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