Early identification of ‘weak’ banks is essential for safety and soundness of the banking sector. Longer the delay in such identification, heavier the cost on an economy. In this article, we apply multiclass classification to classify banks operating in the Indian banking sector as ‘strong’ and ‘weak’ banks. Such classification is expected to provide direction to bankers for bank management. Using average return on equity (ROE) as basis for classification, we apply five machine learning models to the bank data set, namely Naïve Bayes, support vector machine, k-nearest neighbours, random forest and average neural networks. We find that all five models are able to predict the bank classes with a very high degree of accuracy. Ratio of non-performing assets to net advances turned out to be the most important variable in classifying banks as ‘weak’, followed by inflation and real exchange rate. The study is the first of its kind that successfully applies machine learning models in the Indian banking sector.

及早识别“弱”银行对于银行业的安全和稳健至关重要。这种识别的延迟时间越长，经济的成本就越高。在本文中，我们应用多类分类将在印度银行业运营的银行分为“强”和“弱”银行。这种分类有望为银行家的银行管理提供方向。以平均股本回报率（ROE）为分类基础，我们将五种机器学习模型应用于银行数据集，即朴素贝叶斯、支持向量机、k-最近邻、随机森林和平均神经网络。我们发现所有五个模型都能够以非常高的准确度预测银行类别。不良资产与垫款净额的比率被证明是将银行归类为“弱”的最重要变量，其次是通货膨胀和实际汇率。该研究是首次在印度银行业成功应用机器学习模型的研究。