# Exploratory Review of Applications of Machine Learning in the Finance Sector

**Authors:** Sandip Rakshit (American University of Nigeria, Yola, Nigeria) — ORCID 0000-0001-5735-983X; Nyior Clement (American University of Nigeria, Yola, Nigeria); Narasimha Rao Vajjhala (University of New York Tirana, Tirana, Albania) — ORCID 0000-0002-8260-2392
**Type:** Book chapter
**Source:** 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:** 2022-02-13
**DOI:** https://doi.org/10.1007/978-981-16-5685-9_12
**Canonical page:** https://www.narasimharao.net/research/machine-learning-applications-finance-sector-review/
**Indexing:** Scopus · EI Compendex
**Keywords:** Machine learning; Supervised learning; Unsupervised learning; Finance; Security; Algorithmic trading; Artificial intelligence; Data science
**Methodology:** Exploratory literature review of state-of-the-art machine learning applications, algorithms and techniques in the finance sector.

## Research summary

- **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 Samarjeet Borah, Sambit Kumar Mishra, Brojo Kishore Mishra, Valentina Emilia Balas, Zdzislaw 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

## Summary in detail

**Question.** 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?

**Finding.** 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 it matters.** 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.

## Key findings

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.
2. The chapter reviews state-of-the-art machine learning applications in the finance sector in an exploratory manner.
3. Various machine learning algorithms and techniques used in the finance sector are broadly discussed.
4. The chapter offers suggestions on how machine learning can maximise productivity in the finance sector.

## Study at a glance

| Item | Detail |
|---|---|
| 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 |

## 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.

## 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.

## Limitations

- A short (7-page) exploratory review; it does not follow a systematic review protocol and reports no empirical results.

## How to cite

Rakshit, S., Clement, N., & Vajjhala, N. R. (2022). Exploratory Review of Applications of Machine Learning in the Finance Sector. In Samarjeet Borah, Sambit Kumar Mishra, Brojo Kishore Mishra, Valentina Emilia Balas, Zdzislaw 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

```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 = {Samarjeet Borah and Sambit Kumar Mishra and Brojo Kishore Mishra and Valentina Emilia Balas and Zdzislaw Polkowski},
  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}
}
```