Credit Default Prediction Using a Support Vector Machine and a Probabilistic Neural Network

27 Pages Posted: 9 May 2018

See all articles by Mohammad Zoynul Abedin

Mohammad Zoynul Abedin

affiliation not provided to SSRN

Guotai Chi

Dalian University of Technology

Sisira Colombage

Federation University Australia

Fahmida-E Moula

Dalian University of Technology

Date Written: May 9, 2018

Abstract

The design of consistent classifiers to forecast credit-granting choices is critical for many financial decision-making practices. Although a number of artificial and statistical techniques have been developed to predict customer insolvency, how to provide an inclusive appraisal of prediction models and recommend adequate classifiers is still an imperative and understudied area in credit default prediction (CDP) modeling. Previous evidence demonstrates that the ranking of classifiers varies for different criteria with measures under different circumstances. In this study, we address this methodological flaw by proposing the simultaneous application of support vector machine and probabilistic neural network (PNN)-based CDP algorithms, together with frequently used high-performance models. We fill the gap by introducing a set of multidimensional evaluation measures combined with some novel metrics that are helpful in discovering unseen features of the model’s performance. For effectiveness and feasibility purposes, six real-world credit data sets have been applied. Our empirical study shows that the PNN model is more robust than its rivals, and traditional performance evaluations are more or less consistent with their original counterparts. With these contributions, therefore, our investigations offer several advantages to practitioners of financial risk management.

Keywords: financial risk management, credit default prediction (CDP), support vector machine (SVM), probabilistic neural network (PNN), performance criteria, discovering unseen features

Suggested Citation

Abedin, Mohammad Zoynul and Chi, Guotai and Colombage, Sisira and Moula, Fahmida-E, Credit Default Prediction Using a Support Vector Machine and a Probabilistic Neural Network (May 9, 2018). Journal of Credit Risk, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3175776

Mohammad Zoynul Abedin (Contact Author)

affiliation not provided to SSRN

Guotai Chi

Dalian University of Technology ( email )

Huiying Rd
DaLian, LiaoNing, 116024
China

Sisira Colombage

Federation University Australia ( email )

University Dr.
Mt Helen, Victoria 3350
Australia

Fahmida-E Moula

Dalian University of Technology ( email )

Huiying Rd
DaLian, LiaoNing, 116024
China

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