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

尽管早期的研究过分关注银行的破产预测，但本研究从零售储户的角度根据其财务实力对银行进行了分类，而零售储户目前还没有能够帮助他们识别风险较高的银行的真实指导框架。我们使用机器学习技术，根据2005年至2017年的12年数据，将44家印度银行分为不同的财务健康类别。我们首先使用无监督学习来识别导致财务健康的逻辑群体的模式，然后转向受监督学习预测。使用线性判别分析（LDA），分类和回归树（CART）和随机森林方法，我们可以预测集群成员以及相关的解释力。

JEL代码： C53；M10