# Transformational application of Artificial Intelligence and Machine learning in Financial Technologies and Financial services: A bibliometric review

arXiv: https://arxiv.org/abs/2401.15710 · HTML: https://mlsanity.com/paper/2401.15710/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2024.

## Built on by (most cited first, 7 of 7)

- [Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews](https://mlsanity.com/paper/2502.05439/) (2025)
- [Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis](https://mlsanity.com/paper/2402.02623/) (2024)
- [The Evolution of Football Betting- A Machine Learning Approach to Match Outcome Forecasting and Bookmaker Odds Estimation](https://mlsanity.com/paper/2403.16282/) (2024)
- [Analyzing Currency Fluctuations: A Comparative Study of GARCH, EWMA, and IV Models for GBP/USD and EUR/GBP Pairs](https://mlsanity.com/paper/2402.07435/) (2024)
- [Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting](https://mlsanity.com/paper/2403.00796/) (2024)
- [Exploring Quantum-Enhanced Machine Learning for Computer Vision: Applications and Insights on Noisy Intermediate-Scale Quantum Devices](https://mlsanity.com/paper/2404.02177/) (2024)
- [MEMS Gyroscope Multi-Feature Calibration Using Machine Learning Technique](https://mlsanity.com/paper/2410.07519/) (2024)

## Abstract (arXiv)

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.