Machine Learning for Financial Risk Management: A Survey
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 machin...
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doaj-823a90cffe3a41de88e31e6ced31931f2021-03-30T04:33:53ZengIEEEIEEE Access2169-35362020-01-01820320320322310.1109/ACCESS.2020.30363229249416Machine Learning for Financial Risk Management: A SurveyAkib Mashrur0https://orcid.org/0000-0002-4404-7471Wei Luo1https://orcid.org/0000-0002-4711-7543Nayyar A. Zaidi2https://orcid.org/0000-0003-4024-2517Antonio Robles-Kelly3https://orcid.org/0000-0002-2465-5971School of Information Technology, Deakin University, Geelong, VIC, AustraliaSchool of Information Technology, Deakin University, Geelong, VIC, AustraliaSchool of Information Technology, Deakin University, Geelong, VIC, AustraliaSchool of Information Technology, Deakin University, Geelong, VIC, AustraliaFinancial 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.https://ieeexplore.ieee.org/document/9249416/Machine learningdeep learningfinancial risk managementfinancial risk management taxonomyrisk analysisartificial intelligence in finance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Akib Mashrur Wei Luo Nayyar A. Zaidi Antonio Robles-Kelly |
spellingShingle |
Akib Mashrur Wei Luo Nayyar A. Zaidi Antonio Robles-Kelly Machine Learning for Financial Risk Management: A Survey IEEE Access Machine learning deep learning financial risk management financial risk management taxonomy risk analysis artificial intelligence in finance |
author_facet |
Akib Mashrur Wei Luo Nayyar A. Zaidi Antonio Robles-Kelly |
author_sort |
Akib Mashrur |
title |
Machine Learning for Financial Risk Management: A Survey |
title_short |
Machine Learning for Financial Risk Management: A Survey |
title_full |
Machine Learning for Financial Risk Management: A Survey |
title_fullStr |
Machine Learning for Financial Risk Management: A Survey |
title_full_unstemmed |
Machine Learning for Financial Risk Management: A Survey |
title_sort |
machine learning for financial risk management: a survey |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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. |
topic |
Machine learning deep learning financial risk management financial risk management taxonomy risk analysis artificial intelligence in finance |
url |
https://ieeexplore.ieee.org/document/9249416/ |
work_keys_str_mv |
AT akibmashrur machinelearningforfinancialriskmanagementasurvey AT weiluo machinelearningforfinancialriskmanagementasurvey AT nayyarazaidi machinelearningforfinancialriskmanagementasurvey AT antoniorobleskelly machinelearningforfinancialriskmanagementasurvey |
_version_ |
1724181556294057984 |