Affiliations: [a] Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Nagpur, India | [b] Symbiosis School of Banking and Finance, Symbiosis International (Deemed University), Pune, India | [c] Institute of Artificial Intelligence, Dr Vishwanath Karad MIT World Peace University, Pune, India
Correspondence:
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Corresponding author: Anand J. Kulkarni, Institute of Artificial Intelligence, Dr Vishwanath Karad MIT World Peace University, Pune 411038, India. E-mail: [email protected].
Abstract: The aim of this study is to predict the profitability of Indian banks. Several factors both internal and external, affecting bank profitability were derived from extensive review of literature. We used Artificial Neural Network (ANN) with cross-validation technique to perform predictive analysis. ANN was chosen due to its flexibility and non-linear modelling capability. Several structures of ANN with a single and two hidden layers along with varying hidden neurons were implemented. Further, a comparison was made with the multiple linear regression (MLR) model. We found the models based on ANN to offer very accurate results in prediction and are marginally better as compared to the regression model. Higher accuracy of the model makes a significant difference due to the astronomically large size of the balance sheet of banks. This article is unique in the approach of handling the panel data for predictive analysis wherein the training of the model was done on a single bank’s data, thus, reducing the panel data to a time series data. This approach shows the ability to work with large panel data and make accurate predictions.
