Next Article in Journal
Model-Free Stochastic Collocation for an Arbitrage-Free Implied Volatility, Part II
Previous Article in Journal
CEO Overconfidence and Shadow-Banking Life Insurer Performance Under Government Purchases of Distressed Assets
Article Menu

Export Article

Open AccessArticle
Risks 2019, 7(1), 29; https://doi.org/10.3390/risks7010029

Machine Learning in Banking Risk Management: A Literature Review

SP Jain School of Global Management, Sydney 2127, Australia
*
Author to whom correspondence should be addressed.
Received: 25 January 2019 / Revised: 23 February 2019 / Accepted: 27 February 2019 / Published: 5 March 2019
Full-Text   |   PDF [860 KB, uploaded 8 March 2019]   |  

Abstract

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn’t appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems. View Full-Text
Keywords: risk management; bank; machine learning; credit scoring; fraud risk management; bank; machine learning; credit scoring; fraud
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Leo, M.; Sharma, S.; Maddulety, K. Machine Learning in Banking Risk Management: A Literature Review. Risks 2019, 7, 29.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Risks EISSN 2227-9091 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top