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Go, Eun Jung
Moon, Junhwan
Kim, Jinhwa
Year of Publication: 
[Journal:] Global Business & Finance Review (GBFR) [ISSN:] 2384-1648 [Volume:] 25 [Year:] 2020 [Issue:] 1 [Pages:] 102-117
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 governments 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.
Artificial Intelligence
Big Data
Social Network Analysis
Future Prediction
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