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A Systematic Review of Machine Learning and Explainable Artificial Intelligence (XAI) in Credit Risk Modelling

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 559))

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Abstract

The emergence of machine learning and artificial intelligence has created new opportunities for data-intensive science within the financial industry. The implementation of machine learning algorithms still faces doubt and distrust, mainly in the credit risk domain due to the lack of transparency in terms of decision making. This paper presents a comprehensive review of research dedicated to the application of machine learning in credit risk modelling and how Explainable Artificial Intelligence (XAI) could increase the robustness of a predictive model. In addition to that, some fully developed credit risk software available in the market is also reviewed. It is evident that adopting complex machine learning models produced high performance but had limited interpretability. Thus, the review also studies some XAI techniques that helps to overcome this problem whilst breaking out from the nature of the ‘black-box’ concept. XAI models mitigate the bias and establish trust and compliance with the regulators to ensure fairness in loan lending in the financial industry.

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Author information

Authors and Affiliations

  1. School of Computing, Asia Pacific University, Technology Park Malaysia, 57000, Kuala Lumpur, Malaysia

    Yi Sheng Heng & Preethi Subramanian

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  1. Yi Sheng Heng
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  2. Preethi Subramanian
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Correspondence to Yi Sheng Heng .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Heng, Y.S., Subramanian, P. (2023). A Systematic Review of Machine Learning and Explainable Artificial Intelligence (XAI) in Credit Risk Modelling. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_39

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