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Machine Learning in Banking Risk Management: A Literature Review

Author

Listed:
  • Martin Leo

    (SP Jain School of Global Management, Sydney 2127, Australia)

  • Suneel Sharma

    (SP Jain School of Global Management, Sydney 2127, Australia)

  • K. Maddulety

    (SP Jain School of Global Management, Sydney 2127, Australia)

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.

Suggested Citation

  • Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:29-:d:211265
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    References listed on IDEAS

    as
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