Skip to main content

Research Repository

Advanced Search

Would two-stage scoring models alleviate bank exposure to bad debt?

Abdou, Hussein A; Mitra, Shatarupa; Fry, John; El Amer, Ahmed

Authors

Hussein A Abdou

Shatarupa Mitra

Profile image of John Fry

Dr John Fry J.M.Fry@hull.ac.uk
Senior Lecturer in Applied Mathematics

Ahmed El Amer



Abstract

The main aim of this paper is to investigate how far applying suitably conceived and designed credit scoring models can properly account for the incidence of default and help improve the decision-making process. Four statistical modelling techniques, namely, discriminant analysis, logistic regression, multi-layer feed-forward neural network and probabilistic neural network are used in building credit scoring models for the Indian banking sector. Notably actual misclassification costs are analysed in preference to estimated misclassification costs. Our first-stage scoring models show that sophisticated credit scoring models, in particular probabilistic neural networks, can help to strengthen the decision-making processes by reducing default rates by over 14%. The second-stage of our analysis focuses upon the default cases and substantiates the significance of the timing of default. Moreover, our results reveal that State of residence, equated monthly instalment, net annual income, marital status and loan amount, are the most important predictive variables. The practical implications of this study are that our scoring models could help banks avoid high default rates, rising bad debts, shrinking cash flows and punitive cost-cutting measures.

Citation

Abdou, H. A., Mitra, S., Fry, J., & El Amer, A. (2019). Would two-stage scoring models alleviate bank exposure to bad debt?. Expert Systems with Applications, 128, 1-13. https://doi.org/10.1016/j.eswa.2019.03.028

Journal Article Type Article
Acceptance Date Mar 15, 2019
Online Publication Date Mar 15, 2019
Publication Date Aug 15, 2019
Deposit Date Feb 4, 2022
Publicly Available Date Feb 7, 2022
Journal Expert Systems with Applications
Print ISSN 0957-4174
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 128
Pages 1-13
DOI https://doi.org/10.1016/j.eswa.2019.03.028
Keywords Credit; Indian banks; Neural networks; Actual misclassification costs; Timing of Default
Public URL https://hull-repository.worktribe.com/output/3921064

Files





You might also like



Downloadable Citations