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Machine Learning in Credit Risk Modeling: Empirical Application of Neural Network Approaches

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The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success

Part of the book series: Studies in Computational Intelligence ((SCI,volume 935))

Abstract

Motivated by massive development and the ongoing practice of machine learning (ML) approaches in credit risk modeling, this chapter addresses theoretical aspects of machine learning and credit default prediction. This chapter also discusses the properties of mostly used and robust machine learning approaches in credit default prediction. The objective of the chapter does not do empirical analysis, however, to show the practical application of ML approaches in credit default prediction, to the end, this chapter presents an empirical example of trendy classifier neural network approaches on real-world credit datasets.

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Authors and Affiliations

  1. School of Economics and Management, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Mohammad Shamsu Uddin

  2. Department of Business Administration, School of Business and Economics, Metropolitan University, Bateshwar, Sylhet-3103, Bangladesh

    Mohammad Shamsu Uddin

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  1. Mohammad Shamsu Uddin
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Correspondence to Mohammad Shamsu Uddin .

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Editors and Affiliations

  1. College of Business and Finance, Ahlia University, Manama, Bahrain

    Allam Hamdan

  2. Information Technology Department, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. College of Business and Finance, Ahlia University, Manama, Bahrain

    Anjum Razzaque

  4. Middle East Technical University - Northern Cyprus Campus, Kalkanlı, Güzelyurt, KKTC, Turkey

    Bahaaeddin Alareeni

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Uddin, M.S. (2021). Machine Learning in Credit Risk Modeling: Empirical Application of Neural Network Approaches. In: Hamdan, A., Hassanien, A.E., Razzaque, A., Alareeni, B. (eds) The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success. Studies in Computational Intelligence, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-62796-6_25

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