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Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis

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  • Ajitha Kumari Vijayappan Nair Biju

    (University of Kerala)

  • Ann Susan Thomas

    (University of Kerala)

  • J Thasneem

    (University of Kerala)

Abstract

This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research’s status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance.

Suggested Citation

  • Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:1:d:10.1007_s11135-023-01673-0
    DOI: 10.1007/s11135-023-01673-0
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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    3. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    4. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    5. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments," Papers 2102.12694, arXiv.org.
    6. Jerry W. Lin & Mark I. Hwang & Jack D. Becker, 2003. "A fuzzy neural network for assessing the risk of fraudulent financial reporting," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 18(8), pages 657-665, November.
    7. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
    8. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    9. Domingo Docampo & Lawrence Cram, 2019. "Highly cited researchers: a moving target," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1011-1025, March.
    10. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
    11. Kai Li & Jason Rollins & Erjia Yan, 2018. "Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 1-20, April.
    12. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    13. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
    14. Erjia Yan, 2014. "Finding knowledge paths among scientific disciplines," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(11), pages 2331-2347, November.
    15. Shorouq Fathi Eletter & Saad Ghaleb Yaseen & Ghaleb Awad Elrefae, 2010. "Neuro-Based Artificial Intelligence Model for Loan Decisions," American Journal of Economics and Business Administration, Science Publications, vol. 2(1), pages 27-34, March.
    16. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    17. Adrián Kovács & Bart Looy & Bruno Cassiman, 2015. "Exploring the scope of open innovation: a bibliometric review of a decade of research," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 951-983, September.
    18. Gaviria-Marin, Magaly & Merigó, José M. & Baier-Fuentes, Hugo, 2019. "Knowledge management: A global examination based on bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 194-220.
    19. Paulo Reis Mourao & Vítor Domingues Martinho, 2021. "Choosing the best socioeconomic nutrients for the best trees: a discussion about the distribution of Portuguese Trees of Public Interest," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5985-6001, April.
    20. M. Bee & J. Hambuckers & L. Trapin, 2021. "Estimating large losses in insurance analytics and operational risk using the g-and-h distribution," Quantitative Finance, Taylor & Francis Journals, vol. 21(7), pages 1207-1221, July.
    21. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    22. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    23. H. Kent Baker & Satish Kumar & Debidutta Pattnaik, 2020. "Twenty‐five years of Review of Financial Economics: A bibliometric overview," Review of Financial Economics, John Wiley & Sons, vol. 38(1), pages 3-23, January.
    24. Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    25. Andreas G. F. Hoepner & David McMillan & Andrew Vivian & Chardin Wese Simen, 2021. "Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 1-7, January.
    26. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
    27. Dominique Guegan & Peter Martey Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Post-Print halshs-01835164, HAL.
    28. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    29. Yan Zhang & Peter Trubey, 2019. "Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1043-1063, October.
    30. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    31. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    32. Fetscherin, Marc & Heinrich, Daniel, 2015. "Consumer brand relationships research: A bibliometric citation meta-analysis," Journal of Business Research, Elsevier, vol. 68(2), pages 380-390.
    33. Ole Ellegaard & Johan A. Wallin, 2015. "The bibliometric analysis of scholarly production: How great is the impact?," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1809-1831, December.
    34. Alexandre Carbonneau & Frédéric Godin, 2021. "Equal risk pricing of derivatives with deep hedging," Quantitative Finance, Taylor & Francis Journals, vol. 21(4), pages 593-608, April.
    35. Donthu, Naveen & Kumar, Satish & Pattnaik, Debidutta, 2020. "Forty-five years of Journal of Business Research: A bibliometric analysis," Journal of Business Research, Elsevier, vol. 109(C), pages 1-14.
    36. Kexing Ding & Baruch Lev & Xuan Peng & Ting Sun & Miklos A. Vasarhelyi, 2020. "Machine learning improves accounting estimates: evidence from insurance payments," Review of Accounting Studies, Springer, vol. 25(3), pages 1098-1134, September.
    37. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    38. Xiaojun Li & Pan Tang, 2020. "Stock index prediction based on wavelet transform and FCD‐MLGRU," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1229-1237, December.
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