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Default Risk Prediction Based on Support Vector Machine and Logit Support Vector Machine

In: Novel Financial Applications of Machine Learning and Deep Learning

Author

Listed:
  • Fahmida-E-Moula

    (Dalian University of Technology)

  • Nusrat Afrin Shilpa

    (Hajee Mohammad Danesh Science and Technology University)

  • Preity Shaha

    (Hajee Mohammad Danesh Science and Technology University)

  • Petr Hajek

    (University of Pardubice)

  • Mohammad Zoynul Abedin

    (Teesside University)

Abstract

This chapter aims to predict the credit customer default risk. We propose a machine learning algorithm such as Support Vector Machine and a hybrid default risk prediction model such as Logistic Regression and Support Vector Machine being known as LogitSVM (LSVM) to access the credit default risk. We apply three real-world credit databases to validate the probability and value of the proposed risk appraisal hybrid approaches. This chapter uses Type-I Error, Type-II Error, and Root Mean Squared Error (RMSE) to evaluate the performance of the algorithms. Empirical findings show that hybrid model experimentation (LogitSVM) maximizes overall accuracy and minimizes RMSE, Type-I error, and Type-II error. This study is useful for stakeholders to develop a wide variety of approaches to predict risk of default of the credit customer.

Suggested Citation

  • Fahmida-E-Moula & Nusrat Afrin Shilpa & Preity Shaha & Petr Hajek & Mohammad Zoynul Abedin, 2023. "Default Risk Prediction Based on Support Vector Machine and Logit Support Vector Machine," International Series in Operations Research & Management Science, in: Mohammad Zoynul Abedin & Petr Hajek (ed.), Novel Financial Applications of Machine Learning and Deep Learning, pages 93-106, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-18552-6_6
    DOI: 10.1007/978-3-031-18552-6_6
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