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Credit Risk Modelling For Indian Debt Securities Using Machine Learning

Author

Listed:
  • Charumathi Balakrishnan

    (Pondicherry University)

  • Mangaiyarkarasi Thiagarajan

    (Vels Institute of Science, Technology & Advanced Studies)

Abstract

We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.

Suggested Citation

  • Charumathi Balakrishnan & Mangaiyarkarasi Thiagarajan, 2021. "Credit Risk Modelling For Indian Debt Securities Using Machine Learning," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 24(Special I), pages 107-128, January.
    • Handle: RePEc:idn:journl:v:24:y:2021:i:sph:p:107-128
    DOI: https://doi.org/10.21098/bemp.v24i0.1401
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    Cited by:

    1. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).

    More about this item

    Keywords

    Credit risk modelling; Credit rating prediction; Emerging market score model; Machine learning; Indian debt market;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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