Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210900016-17.doi: 10.11896/jsjkx.210900016

• Artificial Intelligence • Previous Articles     Next Articles

Survey of Deep Learning Technologies for Financial Technology

ZHOU Fan, CHEN Xiao-die, ZHONG Ting, WU Jin   

  1. School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:ZHOU Fan,born in 1981,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include big data post analysis and data mining,machine learning and deep lear-ning.
    ZHONG Ting,born in 1977,Ph.D,associate professor.Her main research interests include cloud computing security and machine learning.
  • Supported by:
    National Natural Science Foundation of China(62072077,62176043),National Key Research and Development Program of China(2019YFB1406202) and Sichuan Science and Technology Program(2020GFWO68,2020ZHCG0058,2021YFQ0007).

Abstract: In recent years,deep learning techniques have been widely applied in addressing various problems in financial technology(Fintech) and have attracted increasing attention from both academia and business.Researchers utilize deep learning techniques for mining and analyzing financial data while finding the economic patterns behind tremendous data.Deep learning outperforms traditional statistical machine learning models in a range of crucial financial applications,including market movement prediction,trading strategy improvement,financial text processing,etc.To facilitate the development of Fintech and the deployment of new deep learning techniques,this paper provides a comprehensive survey of the deep learning-based Fintech studies published in recent years.Our survey focuses on the most recent advances in Fintech and provides a roadmap of financial problems as well as corresponding solutions.To this end,we investigate the widely used methodologies in finance data mining and summarize the popular deep models in Fintech data learning.Besides,we propose a taxonomy that categorizes existing Fintech research into ten well-studied applications in the literature.Subsequently,we systematically review the state-of-the-art deep learning methods and provide insights on the improvement for future endeavors.Finally,the pros and cons of existing research are summarized,followed by outlining the trend,open challenges,and opportunities in the Fintech research community.

Key words: Financial technology, Deep learning, Price prediction, Portfolio management, Trend forecast, Risk assessment

CLC Number: 

  • TP311
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