Published February 28, 2024
| Version CC-BY-NC-ND 4.0
Journal article
Open
Transformational Application of Artificial Intelligence and Machine Learning in Financial Technologies and Financial Services: A Bibliometric Review
Creators
- 1. Senior Software Engineering, Microsoft, Northlake, Texas, USA
Description
Abstract: In this study, I employ a multifaceted comprehensive scientometric approach to explore the intellectual underpinnings of AI and ML in financial research by examining the publication patterns of articles, journals, authors, institutions, and nations by leveraging quantitative techniques, that transcend conventional systematic literature reviews, enabling the effective analysis of vast scientometric and bibliographic data. By applying these approaches, I identify influential works, seminal contributions, thought leaders, topical clusters, research streams, and new research frontiers, ultimately fostering a deeper understanding of the knowledge structure in AI and ML finance research by considering publication records from 2010 to 2022 from several search engines and database sources. The present study finds a marked increase in publications from 2017 to 2022, which highlights a growing interest and expanding research activity in the field, indicating its potential significance and relevance in the contemporary academic landscape.
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Additional details
Identifiers
- DOI
- 10.35940/ijeat.D4393.13030224
- EISSN
- 2249-8958
Dates
- Accepted
-
2024-02-15Manuscript received on 01 January 2024 | Revised Manuscript received on 12 February 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024.
References
- A. I. Canhoto,. "Leveraging Machine Learning in the Global Fight against Money Laundering and Terrorism Financing: An Affordances Perspective," Journal of Business Research 131: 441–52, 2021, https://doi.org/10.1016/j.jbusres.2020.10.012. https://doi.org/10.1016/j.jbusres.2020.10.012
- E. Mogaji, and N. P. Nguyen, "Managers' Understanding of Artificial Intelligence in Relation to Marketing Financial Services: Insights from a Cross-Country Study," International Journal of Bank Marketing 40 (6): 1272–98, 2022, https://doi.org/10.1108/IJBM092021-0440. https://doi.org/10.1108/IJBM-09-2021-0440
- J. Jagtiani, and C. Lemieux, "The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform," Financial Management 48 (4): 1009–29, 2019, https://doi.org/10.1111/fima.12295. https://doi.org/10.1111/fima.12295
- K. N. A. Arthur, and R Owen, "A Micro-Ethnographic Study of Big Data-Based Innovation in the Financial Services Sector: Governance, Ethics and Organisational Practices," Journal of Business Ethics 160 (2): 363–75, 2019, https://doi.org/10.1007/s10551-019-04203-x. https://doi.org/10.1007/s10551-019-04203-x
- Z. Liu, G. Du, S. Zhou, H. Lu, and H. Ji, "Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network," Computational Economics 59 (4): 1481–99, 2022, https://doi.org/10.1007/s10614-021-10229-z. https://doi.org/10.1007/s10614-021-10229-z
- F. Khalil, and G. Pipa, "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics 60 (1): 147–71, 2022, https://doi.org/10.1007/s10614-021-10145-2. https://doi.org/10.1007/s10614-021-10145-2
- M.-L.Tseng, T.-D. Bui, M. K. Lim, F. M. Tsai, and R. R. Tan.. "Comparing World Regional Sustainable Supply Chain Finance Using Big Data Analytics: A Bibliometric Analysis," Industrial Management and Data Systems 121 (3): 657–700, 2021, https://doi.org/10.1108/IMDS-09-2020-0521. https://doi.org/10.1108/IMDS-09-2020-0521
- M. Cao, R. Chychyla, and T. Stewart, "Big Data Analytics in Financial Statement Audits," Accounting Horizons 29 (2): 423–29, 2015, https://doi.org/10.2308/acch-51068. https://doi.org/10.2308/acch51068
- N. Naveed, S. Munawar, and A. Usman, "Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain," Journal of Cases on Information Technology 25 (1), 2023, https://doi.org/10.4018/JCIT.316665. https://doi.org/10.4018/JCIT.316665
- A. Prisznyák, "Bankrobotics: Artificial Intelligence and Machine Learning Powered Banking Risk Management Prevention of Money Laundering and Terrorist Financing," Public Finance Quarterly 67 (2): 288–303, 2022, https://doi.org/10.35551/PFQ_2022_2_8. https://doi.org/10.35551/PFQ_2022_2_8
- Lin, Wangli, Li Sun, Qiwei Zhong, Can Liu, Jinghua Feng, Xiang Ao, and Hao Yang, "Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network," IJCAI International Joint Conference on Artificial Intelligence, 3670–76, 2021,https://doi.org/10.24963/ijcai.2021/505. https://doi.org/10.24963/ijcai.2021/505
- X. Tang, S. Li, M. Tan, and W. Shi, "Incorporating Textual and Management Factors into Financial Distress Prediction: A Comparative Study of Machine Learning Methods," Journal of Forecasting 39 (5): 769–87, 2020, https://doi.org/10.1002/for.2661. https://doi.org/10.1002/for.2661
- Z. Wu, "Using Machine Learning Approach to Evaluate the Excessive Financialization Risks of Trading Enterprises," Computational Economics 59 (4): 1607–25, 2022, https://doi.org/10.1007/s10614- 020-10090-6. https://doi.org/10.1007/s10614-020-10090-6
- U. Pasupulety, A. Abdullah Anees, S. Anmol, and B. R. Mohan. "Predicting Stock Prices Using Ensemble Learning and Sentiment Analysis," In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 215–22, 2019, https://doi.org/10.1109/AIKE.2019.00045. https://doi.org/10.1109/AIKE.2019.00045
- Nti Isaac Kofi, Adebayo Felix Adekoya, and Benjamin Asubam Weyori, "Predicting Stock Market Price Movement Using Sentiment Analysis : Evidence From Ghana," Applied Computer Systems 25 (1): 33–42, 20206 https://doi.org/10.2478/acss-2020-0004
- S. Wazir, G. S. Kashyap, and P. Saxena, "MLOps: A Review," arXiv.org, 2023. Available: https://arxiv.org/abs/2308.10908. [Accessed: Nov. 19, 2023]
- M. Kanojia, P. Kamani, G. S. Kashyap, S. Naz, S. Wazir, and A. Chauhan, "Alternative Agriculture Land-Use Transformation Pathways by Partial-Equilibrium Agricultural Sector Model: A Mathematical Approach," arXiv.org, 2023. Available: https://arxiv.org/abs/2308.11632. [Accessed: Nov. 19, 2023]
- N. Marwah, V. K. Singh, G. S. Kashyap, and S. Wazir, "An analysis of the robustness of UAV agriculture field coverage using multi-agent reinforcement learning," International Journal of Information Technology, vol. 15, no. 4, pp. 2317–2327, Apr. 2023, doi: https://doi.org/10.1007/s41870-023-01264-0. Available: https://link.springer.com/article/10.1007/s41870-023-01264-0. [Accessed: Nov. 19, 2023] https://doi.org/10.1007/s41870-023- 01264-0
- G. S. Kashyap, K. Malik, S. Wazir, and R. Khan, "Using Machine Learning to Quantify the Multimedia Risk Due to Fuzzing," Multimedia Tools and Applications, vol. 81, no. 25, pp. 36685–36698, Oct. 2021, doi: https://doi.org/10.1007/s11042-02111558-9. Available: https://link.springer.com/article/10.1007/s11042-021- 11558-9. [Accessed: Nov. 19, 2023] https://doi.org/10.1007/s11042- 021-11558-9
- G. S. Kashyap, A. E. I. Brownlee, O. C. Phukan, K. Malik, and S. Wazir, "Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows," arXiv.org, 2023. Available: https://arxiv.org/abs/2306.02308. [Accessed: Nov. 19, 2023]
- S. Wazir, G. S. Kashyap, K. Malik, and A. E. I. Brownlee, "Predicting the Infection Level of COVID-19 Virus Using Normal DistributionBased Approximation Model and PSO," Springer optimization and its applications, pp. 75–91, Jan. 2023, doi: https://doi.org/10.1007/978- 3-031-33183-1_5.Available: ttps://link.springer.com/chapter/10.1007/978-3-031-33183-1_5. [Accessed: Nov. 19, 2023] https://doi.org/10.1007/978-3-031-33183- 1_5
- Agarwal, P., Garg, N., & Singh, P. (2019). Predicting Poverty Index using Deep Learning on Remote Sensing and Household Data. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 164–168). https://doi.org/10.35940/ijrte.c3918.098319
- Kaur*, H., Kaur, Dr. N., & Neeru, Dr. N. (2019). Multiorgan Detection: Deep Learning Based Techniques and Research Directions. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 1, pp. 4590–4594). https://doi.org/10.35940/ijitee.a7116.119119
- Gautam, R. S., Kanoujiya, J., Bhimavarapu, V. M., & Rastogi, Dr. S. (2021). Financial Technology and Its Impact on Poverty in India. In International Journal of Management and Humanities (Vol. 6, Issue 3, pp. 1–10). https://doi.org/10.35940/ijmh.b1396.116321
- Kanani, P., & Padole, Dr. M. (2019). Deep Learning to Detect Skin Cancer using Google Colab. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 2176–2183). https://doi.org/10.35940/ijeat.f8587.088619
- Wanjau, S. K., Wambugu, G. M., & Oirere, A. M. (2022). Network Intrusion Detection Systems: A Systematic Literature Review o f Hybrid Deep Learning Approaches. In International Journal of Emerging Science and Engineering (Vol. 10, Issue 7, pp. 1–16). https://doi.org/10.35940/ijese.f2530.0610722