Application of BPNN in the analysis of SBI’s Credit Capacity
Roli Pradhan1, KK Pathak2, VP Singh3
1Dr Roli Pradhan, DoMS, MANIT, Bhopal MP, India.
2Dr. K K Pathak, CADD CAM Centre, AMPRI , Bhopal, India.
3Prof. VP Singh, Director, Saagar Institute of technology, Bhopal, MP, India.
Manuscript received on June 18, 2011. | Revised Manuscript received on June 26, 2011. | Manuscript published on July 05, 2011. | PP: 47-54 | Volume-1 Issue-3, July 2011. | Retrieval Number: C054051311
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: During the existing business scenario much need exists for a system that can predict the failure of any firm with accuracy much before the bankruptcy actually occurs. Credit decisions by commercial banks are based to a large extent on the financial statements provided by corporate borrowers as monitored using financial ratios suggesting their financial position. This paper uses 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 State Bank of India. We conclude with practical suggestions on how best to integrate models and research into policy making decisions. Along with establishing the ratios, analysis regarding the bankruptcy status of the firm is also analysed. The basic Z Score value of the firm from 2001-2008 has been used to predict the Z Score values upto 2015.
Keywords: Neural Networks, Credit lending, Credit Capacity, BPNN.