Machine Learning in Banking Risk Management: A Literature Review

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.

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

“…Several risk prediction models are based on statistical methods, including nearest neighbor, discriminant analysis, and logistic regression [3]. With the advancement of machine learning and artificial intelligence techniques, classification, and regression models were additionally being utilized to predict credit risk [4]. Credit risk here means the likelihood of a postponement in the reimbursement of the credit granted [5].…”

“…Limited work was done on resampling techniques for data balancing in this domain because only a few resampling techniques were employed and also obtained less efficient results [2]. 4. Lastly, the interpretable model is also deployed on the web to ease the different stakeholders.…”

Variations of Particle Swarm Optimization for Obtaining Classification Rules Applied to Credit Risk in Financial Institutions of Ecuador

“…There are hybrid models that combine fuzzy logic with SVM (Wang et al 2005) or fuzzy logic with optimization techniques (Chen 2006). There are also hybrid models that select the most salient previous features and reduce the dimension of the input space, which in turn enhance the results (Leo et al 2019;Malhotra and Malhotra 2003;Oreski and Oreski 2014). The authors in Leo et al (2019) provide an excellent review on the risk management techniques used by financial institutions.…”

“…There are also hybrid models that select the most salient previous features and reduce the dimension of the input space, which in turn enhance the results (Leo et al 2019;Malhotra and Malhotra 2003;Oreski and Oreski 2014). The authors in Leo et al (2019) provide an excellent review on the risk management techniques used by financial institutions. In spite of the fact that these machine learning models present good accuracy, they are not considered particularly useful, as explaining the response obtained is difficult (Baesens et al 2003).…”

The Terminator of Social Welfare? The Economic Consequences of Algorithmic Discrimination

“…Relatedly, there is increasing use of ML algorithms in the banking sector, where AI systems enable the accurate detection and management of risks (Leo et al, 2019). On an individual level, for instance, ML algorithms make use of historic customer data to predict applicants' risk of credit default, classify them as good or bad, and ultimately decide about granting a credit (Wang et al, 2015).…”