Book chapter · 2022
Exploratory Review of Applications of Machine Learning in the Finance Sector
In Advances in Data Science and Management: Proceedings of ICDSM 2021, Lecture Notes on Data Engineering and Communications Technologies, pp. 119–125, Springer Nature Singapore · Published
Summary
Rakshit et al. (2022) conducted an exploratory literature review of the machine learning applications, algorithms and techniques used in the finance sector, where institutions process large volumes of heterogeneous data. The review broadly discusses various machine learning algorithms and techniques used in the sector and offers suggestions on how machine learning can maximise productivity in finance. The short chapter does not follow a systematic review protocol and reports no empirical results.
Practical implication. Researchers and practitioners can orient themselves with a concise review of the machine learning algorithms in use in the finance sector before deeper study.
- The Problem
- Financial institutions process large volumes of heterogeneous data, and an overview of which machine learning algorithms and techniques the sector applies was lacking.
- The Methodology
- Exploratory literature review of state-of-the-art machine learning applications, algorithms and techniques in the finance sector.
- The Core Finding
- The exploratory review surveys state-of-the-art machine learning applications, algorithms and techniques used in finance and sets out how machine learning can maximise productivity in financial institutions.
- The Citation
- Rakshit, S., Clement, N., & Vajjhala, N. R. (2022). Exploratory Review of Applications of Machine Learning in the Finance Sector. In S. Borah, S. K. Mishra, B. K. Mishra, V. E. Balas, & Z. Polkowski (Eds.), Advances in Data Science and Management: Proceedings of ICDSM 2021 (pp. 119–125). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-5685-9_12
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the chapter, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. With big data and rapid technology advances, finance-sector institutions process significant amounts of heterogeneous data and increasingly use machine learning algorithms to do so.
- Finding 2. The chapter reviews state-of-the-art machine learning applications in the finance sector in an exploratory manner.
- Finding 3. Various machine learning algorithms and techniques used in the finance sector are broadly discussed.
- Finding 4. The chapter offers suggestions on how machine learning can maximise productivity in the finance sector.
Source: Rakshit et al. (2022), In Advances in Data Science and Management: Proceedings of ICDSM 2021, Lecture Notes on Data Engineering and Communications Technologies, pp. 119–125, Springer Nature Singapore. DOI: 10.1007/978-981-16-5685-9_12
The chapter in detail
What question does this chapter answer?
Which machine learning algorithms and techniques are applied in the finance sector, and how can machine learning maximise productivity in financial institutions processing large, heterogeneous data?
What did the chapter find, in detail?
The exploratory review provides an in-depth look at state-of-the-art machine learning applications in the finance sector. Its primary research question was to explore the algorithms and techniques applied in finance; various machine learning algorithms and techniques used in the sector are broadly discussed, and the chapter offers suggestions on how machine learning can maximise productivity in finance.
Why does it matter?
The finance sector is a pillar of national economies and increasingly processes heterogeneous big data with machine learning. A concise review of the algorithms in use helps researchers and practitioners orient themselves before deeper study.
Methods and data
| Question | Which machine learning algorithms and techniques are applied to finance-sector applications? |
|---|---|
| Design | Exploratory literature review (short chapter) |
| Scope | State-of-the-art machine learning applications in the finance sector |
| Main result | Broad discussion of the algorithms and techniques used in finance |
| Implication | Suggestions on how machine learning can maximise productivity in the finance sector |
| Citation | Rakshit et al. (2022) · DOI 10.1007/978-981-16-5685-9_12 |
Abstract
The finance sector is one of the key pillars of any nation’s economy. However, with the emergence of big data and rapid advancements in technology, the finance sector is processing significant amounts of heterogenous data. Institutions in the finance sector are increasingly using machine learning algorithms and techniques to process these heterogenous data. This exploratory review provides an in-depth look at the machine learning applications in the finance sector. The state-of-the-art machine learning applications in the finance sector were reviewed in this exploratory study. The primary research question addressed in this study was to explore the machine learning algorithms and techniques applied to the applications in the finance sector. Various machine learning algorithms and techniques used in finance sector were broadly discussed in this study. This study also provides some suggestions about how machine learning can maximize productivity in the finance sector.
Abstract as published in Advances in Data Science and Management: Proceedings of ICDSM 2021.
Keywords: Machine learning; Supervised learning; Unsupervised learning; Finance; Security; Algorithmic trading; Artificial intelligence; Data science
Limitations
- A short (7-page) exploratory review; it does not follow a systematic review protocol and reports no empirical results.
Practical implications
- Researchers and practitioners approaching machine learning in finance, a sector that increasingly processes heterogeneous big data, can use the chapter as a concise review of the algorithms in use before deeper study.
- Financial institutions can consult the chapter’s suggestions on how machine learning can maximise productivity in the finance sector.
Drawn from Rakshit et al. (2022), In Advances in Data Science and Management: Proceedings of ICDSM 2021, Lecture Notes on Data Engineering and Communications Technologies, pp. 119–125, Springer Nature Singapore.
When this research may be relevant
This chapter may be relevant to literature searches on machine learning and AI applications in finance and fintech, algorithmic trading and financial security, and introductory reviews of data science in financial institutions.
Research topics addressed: machine learning in finance; financial technology; supervised learning; unsupervised learning; algorithmic trading; big data in finance; financial security; data science; exploratory review
How to cite
- APA 7
- Rakshit, S., Clement, N., & Vajjhala, N. R. (2022). Exploratory Review of Applications of Machine Learning in the Finance Sector. In S. Borah, S. K. Mishra, B. K. Mishra, V. E. Balas, & Z. Polkowski (Eds.), Advances in Data Science and Management: Proceedings of ICDSM 2021 (pp. 119–125). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-5685-9_12
- MLA 9
- Rakshit, Sandip, et al. “Exploratory Review of Applications of Machine Learning in the Finance Sector.” Advances in Data Science and Management: Proceedings of ICDSM 2021, edited by Samarjeet Borah et al., Springer Nature Singapore, 2022, pp. 119–25. https://doi.org/10.1007/978-981-16-5685-9_12.
- IEEE
- S. Rakshit, N. Clement, and N. R. Vajjhala, “Exploratory Review of Applications of Machine Learning in the Finance Sector,” in Advances in Data Science and Management: Proceedings of ICDSM 2021, S. Borah, S. K. Mishra, B. K. Mishra, V. E. Balas, and Z. Polkowski, Eds. Springer Nature Singapore, 2022, pp. 119–125, doi: 10.1007/978-981-16-5685-9_12.
BibTeX
@incollection{rakshit2022machine,
title = {Exploratory Review of Applications of Machine Learning in the Finance Sector},
author = {Rakshit, Sandip and Clement, Nyior and Vajjhala, Narasimha Rao},
booktitle = {Advances in Data Science and Management: Proceedings of ICDSM 2021},
series = {Lecture Notes on Data Engineering and Communications Technologies},
editor = {Borah, Samarjeet and Mishra, Sambit Kumar and Mishra, Brojo Kishore and Balas, Valentina Emilia and Polkowski, Zdzislaw},
pages = {119--125},
year = {2022},
publisher = {Springer Nature Singapore},
doi = {10.1007/978-981-16-5685-9_12},
url = {https://doi.org/10.1007/978-981-16-5685-9_12}
}RIS
TY - CHAP TI - Exploratory Review of Applications of Machine Learning in the Finance Sector AU - Rakshit, Sandip AU - Clement, Nyior AU - Vajjhala, Narasimha Rao ED - Borah, Samarjeet ED - Mishra, Sambit Kumar ED - Mishra, Brojo Kishore ED - Balas, Valentina Emilia ED - Polkowski, Zdzislaw T2 - Advances in Data Science and Management: Proceedings of ICDSM 2021 T3 - Lecture Notes on Data Engineering and Communications Technologies PY - 2022 DA - 2022/02/13 VL - 86 SP - 119 EP - 125 PB - Springer Nature Singapore SN - 2367-4512 DO - 10.1007/978-981-16-5685-9_12 UR - https://www.narasimharao.net/research/machine-learning-applications-finance-sector-review/ LA - en ER -
Links and files
- DOI: https://doi.org/10.1007/978-981-16-5685-9_12
- Publisher’s page: https://link.springer.com/chapter/10.1007/978-981-16-5685-9_12
- This page as text: https://www.narasimharao.net/research/machine-learning-applications-finance-sector-review/paper.md
- Permanent address of this page: https://www.narasimharao.net/research/machine-learning-applications-finance-sector-review/
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