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Data science and AI in FinTech: An overview

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  • Longbing Cao
  • Qiang Yang
  • Philip S. Yu

Abstract

Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and new-generation AI and (DSAI) techniques. Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems. The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence enhancement. Here, we present a highly dense research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions of smart FinTech futures to the DSAI communities.

Suggested Citation

  • Longbing Cao & Qiang Yang & Philip S. Yu, 2020. "Data science and AI in FinTech: An overview," Papers 2007.12681, arXiv.org, revised Jul 2021.
    • Handle: RePEc:arx:papers:2007.12681
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    References listed on IDEAS

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    1. Gilli, Manfred & Maringer, Dietmar & Schumann, Enrico, 2011. "Numerical Methods and Optimization in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780123756626.
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    3. Bernardo Nicoletti, 2017. "The Future of FinTech," Palgrave Studies in Financial Services Technology, Palgrave Macmillan, number 978-3-319-51415-4, December.
    4. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    5. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
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    Cited by:

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    2. Krzysztof Waliszewski & Ewa Cichowicz & £ukasz Gêbski & Filip Kliber & Jakub Kubiczek & Pawe³ Niedzió³ka & Ma³gorzata Solarz & Anna Warchlewska, 2023. "The role of the Lendtech sector in the consumer credit market in the context of household financial exclusion," Oeconomia Copernicana, Institute of Economic Research, vol. 14(2), pages 609-643, June.
    3. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    4. Haris Alibašić, 2023. "Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis," FinTech, MDPI, vol. 2(3), pages 1-14, July.
    5. Edgar Cambaza, 2023. "The Role of FinTech in Sustainable Healthcare Development in Sub-Saharan Africa: A Narrative Review," FinTech, MDPI, vol. 2(3), pages 1-17, July.
    6. Aleksandrina Aleksandrova & Valentina Ninova & Zhelyo Zhelev, 2023. "A Survey on AI Implementation in Finance, (Cyber) Insurance and Financial Controlling," Risks, MDPI, vol. 11(5), pages 1-16, May.

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