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The Impact of Artificial Intelligence, Machine Learning, and Big Data on Finance Analysis
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Abstract
Some scholars believe our society has progressed into The Fourth Industrial Revolution as the digital revolution that is based on the confusion of the physical and digital world. The innovation of networks, Big Data, and Artificial Intelligence technology promote the digital revolution. Fin-Tech, the interdisciplinary in Finance and Technology, is being stimulated at the same time. Using the technology, many problems in the traditional financial industry can be improved, for example, the risk management with information mismatch, low upgrade speed, and high labor cost as well as the individualization services. By providing personalized, higher-quality products, and leveraging data to inform investment strategies, Fin-Tech can benefit consumers with limited credit history through credit analysis. This paper analyzes the application and impact of Artificial Intelligence, Machine learning, and Big Data in Finance. To be more specific, how to help the financial industry reduce costs and enhance productivity with improved services.
Keywords
artificial intelligence, Big Data; machine learning, financial industry, Fin-Tech
[1]. Goodell, J., Kumar, S., Lim, W., & Pattnaik, D. (n.d.). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Science Direct. https://www.sciencedirect.com/science/article/pii/S2214635021001210
[2]. Mahalakshmi, V., Kulkarni, N., Kumar, K., Sree, D., & Durga, S.: The Role of implementing Artificial Intelligence and Machine Learning Technologies in the financial services Industry for creating Competitive Intelligence. Science Direct. (2021) https://www.sciencedirect.com/science/article/pii/S221478532107601X
[3]. OECD, Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers, (2021) https://www.oecd.org/finance/artificial-intelligence-machine-learningbig-data-in-finance.htm.
[4]. Donepudi, P. K. Machine Learning and Artificial Intelligence in Banking. (2017). file:///Users/paris/Downloads/490-Article%20Text-1066-2-10-20210306.pdf. https://file:///Users/paris/Downloads/490-Article%20Text-1066-2-10-20210306.pdf
[5]. Bauguess, Scott W., The Role of Big Data, Machine Learning, and A.I. in Assessing Risks: A Regulatory Perspective. SEC Keynote Address: OpRisk North America 2017. (June 21, 2017). http://dx.doi.org/10.2139/ssrn.3226514
[6]. Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2022, April 28). Operational research and artificial intelligence methods in banking. Science Direct. https://www.sciencedirect.com/science/article/pii/S037722172200337X
[7]. Paruchuri, H. The Impact of Machine Learning on the Future of Insurance Industry. ABC Research House. (2022). https://file:///Users/paris/Downloads/490-Article%20Text-1066-2-10-20210306.pdf
[8]. Kumar, N., Srivastava, J. D., & Bisht, H. Artificial Intelligence in Insurance Sector. Research Gate | Find and share research. (2019). https://www.researchgate.net/profile/Naman-Kumar-3/publication/337305024_Artificial_Intelligence_in_Insurance_Sector/links/5dd00e33a6fdcc7e138761cc/Artificial-Intelligence-in-Insurance-Sector.pdf
[9]. Nti, I., Weyori, B., & Adekoya, A. A systematic review of fundamental and technical analysis of stock market predictions. Science Direct. (2020). https://www.researchgate.net/profile/Benjamin-Weyori/publication/335274959_A_systematic_review_of_fundamental_and_technical_analysis_of_stock_market_predictions/links/5d696bd6299bf1808d59b73a/A-systematic-review-of-fundamental-and-technical-analysis-of-stock-market-predictions.pdf?_sg%5B0%5D=started_experiment_milestone&_sg%5B1%5D=started_experiment_milestone&origin=journalDetail
[10]. Sharma, D. Bhuriya and U. Singh, "Survey of stock market prediction using machine learning approach," 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), pp. 506-509 (2017). doi: 10.1109/ICECA.2017.8212715.
Cite this article
Hong,J. (2023). The Impact of Artificial Intelligence, Machine Learning, and Big Data on Finance Analysis. Advances in Economics, Management and Political Sciences,27,39-43.
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