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Machine learning for financial risk management: A survey

Mashrur, Akib, Luo, Wei, Zaidi, Nayyar Abbas and Robles-Kelly, Antonio 2020, Machine learning for financial risk management: A survey, IEEE Access, vol. 8, pp. 203203-203223, doi: 10.1109/access.2020.3036322.

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Title Machine learning for financial risk management: A survey
Author(s) Mashrur, Akib
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Zaidi, Nayyar AbbasORCID iD for Zaidi, Nayyar Abbas orcid.org/0000-0003-4024-2517
Robles-Kelly, AntonioORCID iD for Robles-Kelly, Antonio orcid.org/0000-0002-2465-5971
Journal name IEEE Access
Volume number 8
Start page 203203
End page 203223
Total pages 21
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, N.J.
Publication date 2020-11-05
ISSN 2169-3536
Keyword(s) machine learning
deep learning
financial risk management
financial risk management taxonomy
risk analysis
artificial intelligence in finance
Summary Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.
Language eng
DOI 10.1109/access.2020.3036322
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2020, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145646

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.