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

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    Elamer_et_al_ESA_Final.pdf (1.164Mb)
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    Publication date
    2019-08-15
    Author
    Abdou, H.A.
    Mitra, S.
    Fry, John
    Elamer, Ahmed A.
    Keyword
    Credit
    Indian banks
    Neural networks
    Actual misclassification costs
    Timing of default
    Rights
    © 2019 The Authors. Published by Elsevier Ltd. Under a Creative Commons license - https://creativecommons.org/licenses/by/4.0/.
    Peer-Reviewed
    Yes
    
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    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.
    URI
    http://hdl.handle.net/10454/16908
    Version
    Published version
    Citation
    Abdou HA, Mitra S, Fry J et al (2019) Would two-stage scoring models alleviate bank exposure to bad debt? Expert Systems with Applications. 128: 1-13.
    Link to publisher’s version
    https://doi.org/10.1016/j.eswa.2019.03.028
    Type
    Article
    Collections
    Management and Law Publications

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