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

Fahmida-E-Moula, Nusrat Afrin Shilpa, Preity Shaha, Petr Hajek () and Mohammad Zoynul Abedin ()
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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

A chapter in Novel Financial Applications of Machine Learning and Deep Learning, 2023, pp 93-106 from Springer

Abstract: 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.

Keywords: Credit default prediction; Support vector machine; Logistic regression; Hybrid methodology (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-18552-6_6

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