Analysis of the Current and Future of the Artificial Intelligence in Financial Industry with Big Data Techniques
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Purpose: This study finds the current and future trends of artificial intelligence techniques in financial industry. Design/methodology/approach: This study tried to find trends in application of AI to financial areas using news data over last three years of 2017 to 2019 to predict new opportunities in financial area with technologies in AI. Text mining and social network analysis are used to analyze the news data containing AI applications in the financial industry. Network analysis on text from news is used for the analysis and modeling, eventually to get major key words as current and future trends. Findings: The results of the analysis produced some meaningful implications. In 2017, the government's investments and the interests on developed countries formed domestic awareness of AI in each country. In 2018, AI accelerated the innovation in the financial industry from the interests by banks and customers in financial areas. In 2019, the investment leader in this area has changed from government to commercial enterprises. It means that in 2019, the introduction of AI technologies in financial industry created strong positive effects. Therefore, this study predicts that innovation will be accelerated in the financial industry using artificial intelligence over the next five years from 2020. In addition, there will be more diverse commercial sites in financial industry using AI based on the analysis of social network analysis in 2019. This study identified that keywords such as automation, customers, and services are associated together. As more and more content-based financial services are provided to customers in these days, this study predicts that AI-based transaction channels will be combined with the existing financial systems to satisfy the needs of customers in near future. Research limitation/implication: The study uses news data from 2017 to 2019. The period of data collection can be extended to last 10 years to get more accurate trends. The study implies that techniques in big data can be applied to find trends using text data such as a news data. Originality/value: The value of this study is to identify current and future patterns in the applications of AI in financial industry using techniques in big data. It can be used to respond to crises in the future and to predict possible opportunities in the future.
Abstract
Purpose: This study finds the current and future trends of artificial intelligence techniques in financial industry. Design/methodology/approach: This study tried to find trends in application of AI to financial areas using news data over last three years of 2017 to 2019 to predict new opportunities in financial area with technologies in AI. Text mining and social network analysis are used to analyze the news data containing AI applications in the financial industry. Network analysis on text from news is used for the analysis and modeling, eventually to get major key words as current and future trends. Findings: The results of the analysis produced some meaningful implications. In 2017, the government's investments and the interests on developed countries formed domestic awareness of AI in each country. In 2018, AI accelerated the innovation in the financial industry from the interests by banks and customers in financial areas. In 2019, the investment leader in this area has changed from government to commercial enterprises. It means that in 2019, the introduction of AI technologies in financial industry created strong positive effects. Therefore, this study predicts that innovation will be accelerated in the financial industry using artificial intelligence over the next five years from 2020. In addition, there will be more diverse commercial sites in financial industry using AI based on the analysis of social network analysis in 2019. This study identified that keywords such as automation, customers, and services are associated together. As more and more content-based financial services are provided to customers in these days, this study predicts that AI-based transaction channels will be combined with the existing financial systems to satisfy the needs of customers in near future. Research limitation/implication: The study uses news data from 2017 to 2019. The period of data collection can be extended to last 10 years to get more accurate trends. The study implies that techniques in big data can be applied to find trends using text data such as a news data. Originality/value: The value of this study is to identify current and future patterns in the applications of AI in financial industry using techniques in big data. It can be used to respond to crises in the future and to predict possible opportunities in the future.
참고문헌 (35)
- Angelina, K., "Landscaping UK FinTech" (2014), UK Trade & Investment
- 배재권, "핀테크(FinTech) 산업 활성화를 위한 법적, 제도적, 기술적 요인에 관한 연구: 전문가 합의 델파이 기법을 중심으로", 예술인문사회 융합 멀티미디어 논문지, 8(1), pp.101~112 (2018), 사단법인 인문사회과학기술융합학회[DOI]
- Bank of Korea, "deposit and withdrawal basis", ~ (2016)
- Bank of Korea, "the Number of Stores in Korean Financial Institutions", ~ (2017)
- Batini, C., "A capacity and value based model for data architectures adopting integration technologies", AMCIS, pp.327~ (2011)
- "The importance of 'big data': A definition"[URL]
- Bizer, C., "The Meaningful Use of Big Data : Four Perspectives-Four Challenges", ACM Sigmod Record, 40(4), pp.56~60 (2012)
- "AI 100: The Artificial Intelligence startups redefining industries" (2017)[URL]
- The cost of cash in the United States, "Chakravorti, B.", Institute for Business in the Global Context. The Fletcher School, Tufts University, ~ (2013)
- 최창열, "핀테크 기업의 비즈니스 모델에 대한 이론적 연구", e-비즈니스연구, 16(4), pp.85~100 (2015), 국제e-비즈니스학회[DOI]
- Coates, V., "On the future of technological forecasting", Technological forecasting and social change, 67(1), pp.1~17 (2001)
- Anh Do Nguyet, "The Impact of Earnings Volatility on Earnings Predictability", Global Business and Finance Review, 22(2), pp.82~89 (2017), 사람과세계경영학회[DOI]
- Enforcement Decree of the Capital Markets and Financial Investment Business and Legislative Notice for Amendment of Financial Investment Business Regulations, "Financial Commission", ~ (2016)
- Hitoshi H., "Servitization, IT-ization and innovation models" (2013), Routledege
- IDC, "Worldwide Semiannual Cognitive", Artificial Intelligence Systems Spending Guide, 2016
- "ICT Technology Level Report" (2017)[URL]
- 정다운, "사회 연결망 분석을 활용한 공공데이터 간 연관성에 관한 연구", 한국측량학회지, 35(6), pp.461~470 (2017), 한국측량학회[DOI]
- 김대식, "소셜네트워크분석 접근법을 활용한 글로벌 금융시장 네트워크 분석", 한국경영과학회지, 38(4), pp.11~33 (2013), 한국경영과학회[DOI]
- 김재필, "미국의 핀테크 산업에서 인공지능 활용(핀테크 인공지능)의 현황과 과제", 지급결제학회지, 10(2), pp.185~214 (2018), 한국지급결제학회
- Kim, Y., "Impacts of A. I on FinTech", Korea Institute of Information Technology Magazine, 14(1), pp.23~28 (2016)
- Artificial Intelligence and its Implications for Income Distribution and Unemployment, "Korinek, A.", The National Bureau of Economic Research, ~, pp., JEL No. D63, E64, O3 (2017)
- "Evaluating the interactivity of ubiquitous service: an integrated metric based on UCD (User-Centered Design) principles" (2008)[URL]
- 임혜진, "금융시장 로보어드바이저 산업에 대한 고찰: 현황과 개선방안", 경영학연구, 47(3), pp.725~749 (2018), 한국경영학회[DOI]
- "IDC's Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide Taxonomy" (2018)[URL]
- Made in the World Revisited, "Miroudot, S.", Robert Schuman Centre for Advanced Studies Research, ~, pp.Paper No. RSCAS 2019 (2019), 84, 33
- McAuley, D., "What is FinTech", Wharton FinTech, ~ (2015)
- Moon, J., "Technology Predicting the Future using Data Mining Techniques", International Journal of Recent Technology and Engineering, 8(5), pp.4204~4208 (2020)
- Muhammad Mustafa, "Financial Inclusion and Per Capita Real GSP Growth across Fifty US States and the District of Columbia: Evidences from Panel Cointegration and GMM Estimates", Global Business and Finance Review, 20(1), pp.87~94 (2015), 사람과세계경영학회[DOI]
- "The Rise of the Digital Bank" (2014)[URL]
- 박대민, "뉴스 기사의 빅데이터 분석 방법으로서뉴스정보원연결망분석", 한국언론학보, 57(6), pp.234~262 (2013), 한국언론학회
- Robert, G., "The Rise of FinTech-New York’s Opportunity for tech Leadership" (2014), Accenture
- Rowley, T. J., "Moving beyond dyadic ties : A network theory of stakeholder influences", Academy of management Review, 22(4), pp.887~910 (1997)
- Schwab, K., "The Fourth Industrial Revolution, Portfolio", Penguin. Geneva, Switzerland: Crown Publishing Group, 184, 2017, pp.26(2), 12~15
- "Artificial Intelligence for Enterprise Applications: Deep Learning, Predictive Computing, Image Recognition, Speech Recognition, and Other AI Technologies for Enterprise Markets-Global Market Analysis and Forecasts" (2015)[URL]