Dr. (Prof.) R. Ganesan, Dr. Dhirendra Kumar, AIMS Spillover effect, twin balance sheet, economy etc. 1-8 Volume 8, Issue2 (July 2018 - Dec 2018) July 2018 - Dec 2018 The Indian Banking sector from 2004-05 to 2008-09 lend aggressively to different sectors of the economy in order to take advantage of a robust economic growth. The slowing of the world economy afterwards and its impact on the Indian economy led to slowing of different sectors. This not only led to huge losses for the big corporate houses which created a spillover effect on the balance sheets of the Indian banking sectors also. Thus it created twin balance sheets problem. This research paper is an attempt by the researcher to understand the important credit risks profile of the banks and its impact on profitability. 1. IntroductionA strong banking sector is important for flourishing economy. The failure of the banking sector has an adverse impact on other sectors. In the recent years the high level of NPA's has been major cause of concern for Indian economy. A high level of NPA's indicates high probability of a large number of credit defaults that affect the profitability and net worth of the banks and also erodes the quality of the assets. Gross non performing advances (GNPA) of SCBs as a proportion of gross advances increased to 5.1 percent from 4.6 per cent between March and September 2015. PSBs had the highest level of stressed assets (gross plus restructured assets) at 14.0 per cent of the total, followed by private sector banks (PVB) at 4.6 percent and foreign banks (FB) at 3.4 per cent at end-September 2015. The contribution of five sub-sectors, namely mining, iron and steel, textiles, infrastructure and aviation (which together accounted for 24.2 per cent of the total advances of SCBs as of June 2015) to the total stressed advances was 53.0 percent. The performance of these sectors and their impact on the asset quality of banks continue to be a cause for concern in India.2. Objectives of the Studya. To study the structure of Indian Banking sector.b. To examine the risks profile of scheduled commercial banks in Indiac. To study the different credit risks variables and their impact on profitability indicators i.e. ROA and ROEduring the period of study.3. Research MethodologyThe specific research design selected for this study is descriptive research design. Descriptive study is undertaken in many circumstances when there is interest in knowledge. The study purports to present facts concerning the nature and status of current banking sector, their associated risks and their impact on performance and profitability of the banks. The study has been conducted considering the regulator, the banks and various businesses point of view. The secondary data has been collected and analysed. The population of the study comprises of all the scheduled commercial banks in India and the data pertaining to all the scheduled commercial banks has been studied leaving no scope for sampling. The period of the study has been from 2009-10 to 2016-17. The following multiple regression model is used for the analysis of impact of various factors on the profitability and performance of the selected banks. Y= a + b1X1+ b2X2+b3X3+b4X4 + b5X5 + b6X6 +b7X74. Data Analysis and Interpretationa) Purpose: To study if priority sector lending to total advances, gross NPA, Net NPA, Net NPA as % of total Assets predict Return on Assets (ROA).Statistical test: Step wise multiple regression analysis H0: Priority sector lending to total advances, gross NPA, Net NPA, Net NPA as % of total assets, are not the predictors of Return on Assets (ROA).H1: Priority sector lending to total advances, gross NPA, Net NPA, net NPA as a % of total assets are significant predictors of Return on Assets (ROA).Level of significance = 0.05Variables Entered/RemovedModel VariablesEnteredVariablesRemovedMethod1Net NPA as a %of net advances. Stepwise (Criteria: Probability-of-F-to-enter = .100).a. Dependent Variable: ROAModel SummaryModelR R Square Adjusted RSquareStd. Error of theEstimate1 .888a .789 .754 .16140a. Predictors: (Constant), Net NPA as a % of net advancesANOVAaModel Sum ofSquaresdf MeanSquareF Sig.1 Regression .584 1 .584 22.427 .003bResidual .156 6 .026Total .740 7a. Dependent Variable: ROAb. Predictors: (Constant), Net NPA as a % of net advancesCoefficientsaModel UnstandardizedCoefficientsStandardizedCoefficientst Sig.B Std. Error Beta1(Constant) 1.233 .109 11.272 .000Net NPA as a % ofnet advances-.182 .038 -.888 -4.736 .003CoefficientsaModel Collinearity StatisticsTolerance VIF1(Constant)Net NPA as a % of net advances 1.000 1.000a. Dependent Variable: ROAExcluded VariablesaModel Beta In t Sig. PartialCorrelationCollinearity StatisticsTolerance1Net NPA as a %total assets.135b .499 .639 .218 .553Gross NPA as % ofGross advances2.638b 1.249 .267 .488 .007Priority sectoradvances to totaladvances.130b .620 .562 .267 .889Excluded VariablesaModel Collinearity StatisticsVIF Minimum Tolerance1 Net NPA as a % total assets 1.810b .553Gross NPA as % of Gross advances 138.663b .007Priority sector advances to totaladvances1.125b .889a. Dependent Variable: ROAb. Predictors in the Model: (Constant), Net NPA as a % of net advancesCollinearity DiagnosticsaModelDimensionEigenvalue ConditionIndexVariance Proportions(Constant)Net NPA as a % of net advances11 1.853 1.000 .07 .072 .147 3.554 .93 a. Dependent Variable: ROAInterpretation: The Model Summary presents the R-Square and Adjusted R-Square values for each step along with the amount of R Square Change. The model summary shows that R= .888, R Square = 0.789 (78.9% of the variance in ROA is accounted for ratio of priority sector to total advances, net NPA to total assets, Gross NPA, Net NPA).The researcher referred to the table coefficient to examine the contribution of predictors to the model. From the model it is observed that net NPA as a % of net advances is the significant predictors of return on assets. Thus the null hypothesis is rejected (P a) dbie.rbi.org.inb) Puri Y, (2012), the study of financial performance of nationalized banks during 2006-2010:International journal of research in commerce and management, 3 (9), 42-52.c) Muraleedharan D., Modern Banking, Theory and Practice, Second Edition, Jan 2014, Prentice HallIndia Learning Private Limited.d) Economic Survey 2016-17, GOIe) Hanumantha Rao, N. G. (2013): Determinants of Return on Assets of Public Sector, Pacific BusinessReview International, 5 (11), 23-28.