Application of Artificial Neural Network in the Ratio Prediction of Axis Bank

The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on different models. Second, Inspired by the traditional credit risk models developed, we propose novel indicators for the NN system. Thereafter, this paper using the tailored back-propagation neural network endeavors to predict the financial ratios expressing the position of a firm to regulate the bankruptcy and assess the credit risks. It first estimates the financial ratio for a firm from 2001- 2008 to the train the BPNN and uses the estimates of the year 2009 and 2010 values for the validation process. Finally it dwells to draw predictions for the period 2011-2015 and emphasizes the growing role of BPNN application based prediction models for banking sector with a case study of AXIS bank. We conclude with practical suggestions on how best to integrate models and research into policy making decisions.