While earlier studies have focused excessively on bankruptcy prediction of banks, this study classifies banks based on their financial strength from the perspective of retail depositors who currently do not have an authentic guiding framework that helps them identify banks with higher risk profiles. Using machine learning techniques, we classify 44 Indian banks into distinct categories of financial health based on 12-year data from 2005 to 2017. We first use unsupervised learning to identify a pattern leading to logical groups in terms of financial health and then move to supervised learning for prediction. Using linear discriminant analysis (LDA), Classification and Regression Tree (CART) and Random Forest methods, we predict the cluster membership with the associated explanatory power alongside. We also compare our classification with the credit ratings awarded by rating agencies and highlight certain discrepancies that exist between what is predicted by our models and the credit rating awards. JEL Codes: C53; M10
Under core banking, one is not a customer of a branch but is considered as customer of the bank. All the commercial banks have interconnectivity through a common operating system. The interconnectivity between commercial banks are not utilized properly. If the banks could follow a single Loan Originating system (LOS), the credit worthiness of the consumer can be ascertained before a loan is sanctioned. In India, every bank has its own loan originating system which results in loans being dispersed well above the credit limit. A survey conducted among salaried class is a pointer to this. By making a single window LOS, the problem of NPA among the salaried class can be well addressed.
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