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Predicting bank performance using machine learning: a case of troubled banks in India

Author

Listed:
  • Sumedha Tuteja
  • Punam Bhoyar
  • Krishna Kumar Singh
  • Aruna Dev Rroy

Abstract

The significance of a bank's financial stability for achieving a thriving and robust economy cannot be overstated. This study develops a predictive model using machine learning (ML) to categorise banks into low- or high-performance. The non-performing assets (NPA) levels of the Indian banking sector, notably for the public sector banks (PSBs), have increased significantly since 2015. Hence, the model has been created using bank performance data of PSBs, specifically for the period 2015-2020. The authors first identified logical groups by using the unsupervised K-means clustering method; and, subsequently, deployed a supervised algorithm for prediction: the classification and regression tree (CART). The model has an overall prediction accuracy of 0.9375, a sensitivity of 0.8571 and a specificity of 0.9600. This study is unique since it uses only data from banks with high NPA levels to create a predictive model for bank performance.

Suggested Citation

  • Sumedha Tuteja & Punam Bhoyar & Krishna Kumar Singh & Aruna Dev Rroy, 2023. "Predicting bank performance using machine learning: a case of troubled banks in India," International Journal of Pluralism and Economics Education, Inderscience Enterprises Ltd, vol. 14(1), pages 55-72.
    • Handle: RePEc:ids:ijplur:v:14:y:2023:i:1:p:55-72
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