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

Hussein A Abdou,Shatarupa Mitra,John Fry,Ahmed A Elamer

doi:10.1016/j.eswa.2019.03.028

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.

Highlights

At a time when even the largest banks are not immune to distress, credit decision-making is crucially important
The Actual Misclassification Costs (AMC) results show that the overall means are 1.21 and 2.59 for years 2006 and 2011, respectively. These results demonstrate that our neural network models, namely Probabilistic Neural Network (PNN) and Multi-Layer Feed Forward Networks (MLFNs), can lead to further material reductions in default losses
The main aim of our paper is to use a two-stage analysis to investigate whether scoring models can efficiently distinguish the Indian banking clients’ creditworthiness, and reduce default rates

Summary

Introduction

At a time when even the largest banks are not immune to distress, credit decision-making is crucially important. The Reserve Bank of India (RBI) and the Finance Ministry has far externally controlled and regulated the banking sector. Deregulation and the decoupling of state control pose new challenges, and intense competition is placing the survival of all but the fittest and the most efficient in doubt. Commercial banks are striving to adjust to a new economic and technological environment. Sound credit scoring models form an integral part of this adjustment process. This motivates our present purpose which is to propose suitably conceived and designed credit scoring models for personal loans with due allowance for the incidence of default

Objectives

Methods

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Conclusion