PRIORITY SECTOR & NPA MANAGEMENT LENDING BY THE INDIAN BANKS Abstract The matter of NPA Management as drivers to financial stability in the Banking Sector has been attracting grave concern by the regulators and is judgmental in assessing performance of the Banks. In the recent years, Non-performing Asset (NPA) has grown as a threat sending distressing signals on the sustainability and durability of the affected banks. A high level of NPAs suggests high probability of a large number of credit defaults that affect the profitability and net-worth of banks and also erodes the value of the asset. The cost of the intermediation by the banks has raised brows for controlling the interest rates and identification of benchmarks for the identification and resolution of N.P.A.s.The problem of NPAs is not only affecting the banks but also the whole economy.. If the bank could reduce the cost of Non-Performing Assets, cost will reduce and the profit and return on equity and assets will increase. It is not possible to eliminate totally the Non-Performing Assets in the banking business but can only be minimized. It is always wise to follow the proper policy appraisal, supervision and follow up of advances to avoid creation of Non- Performing Assets. The banks should take steps for reducing present non-performing assets, but necessary precaution should also be taken to avoid future Non-Performing Assets. To test the significant impact of priority sector lending on total NPAs of the Public and Private sector banks, the authors have done a trend analysis of NPAs using Linear Regression Analysis. To substantiate the results, a case test of Public Sector vs Private Sector Banks viz. Union Bank of India, United Commercial Bank, HDFC Bank and Yes Bank has been done.the authors have concluded that there is no significant impact of priority sector lending on total NPAs of Public sector banks. But a very significant impact of priority sector lending on total NPAs of Private sector banks. KEY WORDS: N.P.A. (Non-Performing Assets), SARFAESI, Priority Sector Lending, Asset Classification, Provisioning, Prudential Norms JEL Classification: G21, E51, G11, C23
1. Introduction Non-Performing Asset means any debt obligation where the borrower has not paid any previously agreed upon interest and principal repayments to the designated lender for an extended period of time. The nonperforming asset is therefore not yielding any income to the lender in the form of principal and interest payments. With effect from March 31, 2004, a non-performing asset (NPA) shall be a declared as a loan or an advance where; Instalment of principal or interest remain overdue for a period exceeding 90 days in respect of a Term Loan The account remains 'out of order' for a period of more than 90 days, relating to Cash Credit or Bank Overdraft The bill remains unsettled for a period of more than 90 days in respect of a purchased or discounted bill. Types of NPA: There are three major types of NPA: 1
1. Sub-standard: The account holder belonging to this category doesn t pay three instalments continuously after 90 days and up to 1year. Bank has made 10% provision of funds for this category to meet the losses generated from NPA from their profit. 2. Doubtful NPA : Doubtful NPA are classified into three sub categories : 20% provision is made by the banks for D1 i.e. up to 1 year 30% provision is made by the bank for D2 i.e. up to 2 year 100% provision is made by the bank for D3 i.e. up to 3 year. 3. Loss Assets: When account holder belongs to this category 100% provision is made by the banks to write off their accounts. After this the assets are delivered to recovery agents for the purpose of sale. The classification of assets and provisioning norms as per the extant guidelines and as detailed above could be summarized as depicted in the following table. Reasons behind NPA: Default of a loan intentionally Frequent shuffle of govt. policies leads to NPA. Customer has taken the loan for non-performance of business Most of the loan sanctioned for agricultural purposes Negligent pre-enquiry by the bank for sanctioning the loan to a customer. Effects of NPA on banks & FI: Continuous draining of profit and Negative impact on goodwill. Adverse growth of equity value. Restricted cash flow by bank due to provision of fund created against NPA. Gross NPA and Net NPA: Gross NPA is advance which is considered irrecoverable, for whom the bank has made provisions, and which is still held in banks' books of account. It is calculated as: Gross NPAs Ratio = Gross NPAs ------------------------ Gross Advances 2
Net NPA is obtained by deducting items like interest due but not recovered, part payment received and other income kept in suspense account from Gross NPA. Calculated as: Net NPAs = Gross NPAs Provisions ----------------------------------- Gross Advances Provisions In India, when the loan is sanctioned against any security, provision has to be created. Further, Indian Banks have to make a100 per cent provision which is treated as a doubtful advance, while in some countries; it is 50 per cent or just 75 per cent. According to the RBI, "Reduction of NPAs in the Indian banking sector should be treated as a national priority item to make the system stronger, resilient and geared to meet the challenges of globalization. It is necessary that a public debate is started soon on the problem of NPAs and their resolution. " 1.1 Present Scenario of NPA RBI has been insisting on banks to utilise various measures on recovery of bad loans and strengthen due diligence. Gross non-performing assets (NPAs) have increased sharply in public sector banks (PSBs) in the first quarter of the current financial year as a rapidly slowing economy is resulting in a quantum leap in bad loans. Gross NPAs as a percentage of advances stood at a two-andhalf year high in several leading PSBs in the April-June quarter. State Bank of India (SBI), the country's largest lender, topped the list of banks with the highest gross NPAs (in percentage terms) during the quarter among BSE-Bankex constituents. The gross NPA to advances for SBI, which has seen a steady increase in bad loans, surged to 5.56% in April-June, the highest since the quarter ending March 2011. Gross NPAs have increased 81 basis points (0.81%) for SBI during the quarter, data with the Centre for Monitoring Indian Economy (CMIE) showed. The rise of bad loans is across the board. The growth has lowered, manufacturing is not doing that well and interest rates are going up instead of moving down. In such an environment, NPAs are expected to move up. Gross NPA to advances surged to the highest in 10 quarters for Bank of Baroda, Canara Bank and Punjab National Bank. Incidentally, global ratings agency Moody's downgraded the bank finance strength ratings of Bank of Baroda, Canara Bank and Union Bank of India 3
on August 16. Moody's says PSBs would find it difficult to respond to slower economic growth, deteriorating asset quality and declining profit margins. 2. Review of Literature K.J. Taori (June 2000) in their study entitled, Problems and issued relating to management of NPAs of banks in India studies the growth of NPAs of banks in India. The analysis revealed about priority sector and Non priority sector NPAs and set up the guidelines for bankers and borrowers. It was concluded that an effective legal framework will be needed to bring recovery suits to their logical conclusion and effect recoveries, within a reasonable time frame. Shri T.C.G.Namboodiri (March 2002) in his study entitled NPA - Prevention is better than cure, he pointed out the problem of Non-Performing Assets in Indian Banks. He observed certain simple but important basic points that a banker has to apply while appraising a credit proposal. He suggested to several points such as 5Cs (Character, Capital, Capacity, Conditions, Collateral), 6Ms (Man, Money, Machine, Material, Market, Men), and 7Ps (Product, Project, Purpose, Place, People, Policies, Profit). These 18 points mentioned play considerable role in credit risk management and can be used for a SWOT analysis of the venture before financing. Michael et al (2006) emphasized that NPA in loan portfolio affect operational efficiency which in turn affects profitability, liquidity and solvency position of banks. Batra(2003) noted that in addition to the influence on profitability, liquidity and competitive functioning, NPA also affect the psychology of bankers in respect of their disposition of funds towards credit delivery and credit expansion 3. Objectives This report aims at analysis of various prospects of NPA in banks, detailing those: To study present NPA status of for public sector banks &private sector banks in India and analyse which bank is performing best To regress gross NPA and gross advances and to find for which sector of banks the relation is significant To analyse the trends of NPAs in Priority and Non priority sector. 4. Research Methodology 3.1 Regression Analysis Regression Analysis is done using Regression Analysis for analysing the financial performance w.r.t. NPA for the 4banks 4
Hypothesis 1: H 0 : Variation in NPA attributable to advances in public and private sector banks. H 1 :Variation in NPA is not attributable to public and private sector banks. Hypothesis 2: H 0 : Priority sector lending is independent of total NPAs of Public sector banks. H 1 : Priority sector lending is dependent of total NPAs of Public sector banks. Hypothesis 3: H 0 : Priority sector lending is independent of total NPAs of Private sector banks. H 1 : Priority sector lending is dependent of total NPAs of Private sector banks. 3.2 Research and Sample Design; Sampling technique Four banks (two each from public sector and private sector) vizhdfc, Yes Bank, Union Bank of India and UCOBank has been taken into consideration on basis of stratified judgmental sampling. 3.3 Data Collection and Sample Data The data required for the analysis was collected from different sources and was collated together to carry out analysis. NPAs public sector banks, old and new private sector banks and foreign banks have been used from the information listed in the Second Schedule of the Reserve Bank of India Act, 1934. The RBI publications like, Report on Trend and Progress of Banking in India, Annual Report of RBI, and Reports on Currency and Finance are the major sources for this analysis. 5. Analysis 4.1 To study present NPA status of for public sector banks & private sector banks in India and analyse which bank is performing best The graphs mentioned below has been used to analyse the present status of NPA in various Indian Banks 5
Figure 1: NPA ratio comparison for various Public Sector Banks As per the graph shown above, it can be clearly interpreted that among the Public Sector Banks the best performing bank is Corporation Bank with a minimum NPA ratio of 1.26 followed by Punjab & Sind Bank with NPA ratio of 1.65.The worst performing bank being Central Bank of India with maximum NPA ratio of 4.83. Figure 2: NPA ratio comparison for various Old Private Sector Banks 6
As per the above mentioned graph, it is fairly evident that the best performing banks among the old private sector banks is ING Vysya Bank with minimum NPA ratio of 0.52 followed by Ratnakar Bank. The worst performing bank is Federal Bank Ltd with maximum NPA of 3.35. Figure 3: NPA ratio comparison for various New Private Sector Banks From the above graph, it can be clearly interpreted that the best performing bank is Yes Bank with minimum NPA ratio of only 0.22 and worst performing bank is ICI ICI Bank Ltd with an NPA ratio of 4.83. 4.2To regress gross NPA and gross advances and to find for which sector of banks the relation is significant The financial performance for the 4 banks for the years 2008 09 to 2011 12 has been given in Table 5and mentions the Advances and NPA. We have taken Dependent variable: NPA Independent variable: Advances The summary of the R and R 2 for the 4 banks is given in Table 6. ANOVA tables are Tables 6, 7, 8 and 9for HDFC, Yes Bank, Union Bank of India and UCO Bank respectively. HDFC Bank We see from the final values that R-square value is only 0.05 i.e. only 5 percent NPA variation is dependent on advances. P-value is more than 0.05. Hence result is insignificant. 7
Yes Bank We see from the final values that R-square value is only 0.027 i.e. only 2.7% percent NPA variation is dependent on advances. P-value is more than 0.05. Hence result is insignificant Union Bank of India We see from the final values that R-square value is 0.926i.e. 92.6% percent NPA variation is dependent on advances. P-value is less than 0.05. Hence result is significant UCO Bank We see from the final values that R-square value is 0.9765i.e. 97.65% percent NPA variation is dependent on advances. P-value is less than 0.05. Hence result is significant We can clearly see that for Public sector Banks results are statistically significant whereas for private sector banks results are statistically insignificant. This means that for public sector banks NPA variation is dependent on advances while for private sector banks they are independent. 8
4.3 To analyse the trends of NPAs in Priority and Non priority sector 4.3.1 Public Sector Banks Priority Sector Advances and NPAs Next, we would look at the priority sector advances and NPAs in priority sector by public sector banks during the ten years. The total priority sector credit of public sector banks have raised from Rs. 1,71,484 crore to Rs. 10,22,925 crore. The priority sector NPAs of public sector banks have raised from Rs. 25,139.34 crore at the end of March, 2003 to Rs. 41,245 crore at the end of March, 2012. It is observed that there was an increase in credit and NPAs in absolute terms, but in terms of Priority Sector NPA ratio to Priority Sector Advances there is a decline from 14.66 to 4.03. It is understood from the above observation that the public sector banks have recovered the priority sector credit in time and they managed the priority sector credit effectively to safeguard themselves from the evils of NPAs. (Shown in Table 1) H 0 : Priority sector lending is independent of total NPAs of Public sector banks. To test the significant impact of priority sector lending on total NPAs of Public sector banks, we have used Linear Regression Analysis. Beta coefficient of priority sector lending of Public sector banks is 0.0118. It shows that priority sector lending has minimal impact on total NPAs of Public sector banks. At 95% confidence interval, the t value at 5% level of significance is 1.833. Since, calculated t = 1.051 < 1.833, H 0 is accepted. Hence, there is no significant impact of priority sector lending on total NPAs of Public sector banks.(mentioned in Table No 2) 4.3.2 Private Sector Banks Priority Sector Advances and NPAs The priority sector advances and its NPAs by public sector banks can be seen next. The total priority sector credit of private sector banks have increased from Rs. 24,184 crore to Rs. 2,49,139 crore i.e., increased10 times. The priority sector NPAs of private sector banks have raised from Rs. 2,546.35 crore at the end of March, 2003 to Rs. 4,823 crore at the end of March, 2012 i.e., raised 2 times. It is observed that there was an increase in credit and NPAs in absolute terms, but in terms of Priority Sector NPA ratio to Priority Sector Advances there is a decline during the study period. It is observed that the private sector banks also recovered the priority sector credit in time. (Depicted in Table 3) H 0 : Priority sector lending is independent of total NPAs of Private sector banks. 9
To test the significant impact of priority sector lending on total NPAs of Private sector banks, we used Linear Regression Analysis. Since the coefficient is positive (0.034), there exists some linear relationship between priority sector lending and total NPAs of Private sector banks. At 95% confidence interval, the t value at 5% level of significance is 1.833. Since, calculated t = 3.047 > 1.833, H 0 is rejected. Hence, there is a significant impact of priority sector lending on total NPAs of Private sector banks. (Depicted in Table 4) Using regression analysis it is clear that priority sector lending has significant impact on total NPAs of private sector banks, whereas in public sector banks priority sector lending has no significant impact on total NPAs. 5. Tools for Recovery of NPA 10
6. Findings The findings from this study are: 1) The best performing bank among all public sector banks in terms of NPA minimization is Corporation Bank and among old Private Sector Banks is ING Vysya Bank and among New Private Sector Bank is Yes Bank Ltd. 2) For public sector banks NPA variation is mostly dependent on advances while for private sector banks they are independent. 3) There is no significant impact of priority sector lending on total NPAs of Public sector banks. 4) There is a significant impact of priority sector lending on total NPAs of Private sector bank 7. Recommendations Indian banks have realized that a higher level of Non-Performing Assets in their credit portfolio is dangerous and will effect on their profitability which is already under strain. Quality of loan assets is the most important factor for the basic viability of the banking system. Lower level of Non-Performing assets helps the Indian banks in consolidating their position and gives credence to efficiency of the management. Indian banks can control this problem of reducing the Non-Performing Assets taking two measures namely; It is required to arrest the fresh inflow of Non-Performing Assets. Indian banks need to ensure that It accepts only genuine proposals and rejects projects having inherited weakness. It needs to upgrade the credit appraisal skills which are highly inadequate. Economic viability, technical feasibility, quality of management and financial position of the borrower should be evaluated properly. Pre credit and post credit appraisals are to be done by Indian bank more objectively. Close monitoring of borrower accounts, site visits, factory visits, etc. are to be done regularly. 11
8. Conclusions Non-Performing Assets have been a big worry for the banks in India. It is just not a problem for the banks; they are bad for the economy too. The money locked up in Non- Performing Assets is not available for productive use and hence they have an adverse effect on banks profitability. If the bank could reduce the cost of Non-Performing Assets, cost will reduce and the profit and return on equity and assets will increase. It is not possible to eliminate totally the Non-Performing Assets in the banking business but can only be minimized. It is always wise to follow the proper policy appraisal, supervision and follow up of advances to avoid creation of Non-Performing Assets. The banks should take steps for reducing present non-performing assets, but necessary precaution should also be taken to avoid future Non-Performing Assets. The banking industry is facing yet another period of change, perhaps greater than the one experienced in the immediate past and there is no doubt that Indian banks have to manage it function successfully and skillfully during the present era, replete with significant economic, competitive and technological challenges in order to improve its deposits, advances, profitability and to reduce the Non-Performing Assets. REFERENCES Aggarwal, S., & Mittal, P. (2012). Non-Performing Asset: Comparative Position of Public and Private Sector Banks in India, International Journal of Business and Management Tomorrow, Vol.2 (1). Batra, S. (2003). Developing the Asian Markets for Non-Performing Assets ; Developments in India, 3rd Forum on Asian Insolvency Reform, Seoul, Korea. Bhalla V. K. Financial Management & Policy IInd Edition, Anmol Publications, NewDelhi, 2001 Michael, JN., Vasanthi, G., &Selvaraju, R. (2006). Effect of Non-Performing Assets on Operational Efficiency of Central-Cooperative Banks, Indian Economic Panorama, Vol. 16(3).pp. 33-39. Namboodhiri, T.C.G. (2003). NPA: Prevention is better than Cure, Vinimaya, Vol.XXII (3), pp. 19-22. Reserve Bank of India, Trend and Progress of Banking in India, 2010 Sethi, J., & Bhatia, N. (2007).Elements of Banking and Insurance, 2nd Edition, Prentice Hall India Publications. S.N. Bidani. Managing Non-performing Assets in Banks, Vision BooksPublications, 2008. Taori, K.J. (2000). Problems and Issues Relating to Management of NPAs of Banking in India, The Journal of the Indian Institute of Bankers, April June 2000, pp.21 24. 12
Yadav, M.S. (2011), Impact of Non-Performing Assets on Profitability and Productivity of Public Sector Banks in India, AFBE Journal, Vol. 4(1). 13
Appendix Table 1 Year Total Priority Sector NPAs Total Priority Sector Advances Priority Sector NPA Ratio Gross NPA Ratio 2002-03 25139.34 171484 14.66 11.10 2003-04 24938.36 199786 12.48 9.40 2004-05 23840.33 244456 9.75 7.80 2005-06 23397.38 307046 7.62 5.50 2006-07 22373.74 409748 5.46 3.60 2007-08 22953.62 521376 4.40 2.70 2008-09 25286.67 610450 4.14 2.20 2009-10 24318 720083 3.38 2.00 2010-11 30848 864564 3.57 2.19 2011-12 41245 1022925 4.03 2.23 Table 2 SUMMARY OUTPUT Regression Statistics Multiple R 0.348490189 R Square 0.121445412 Adjusted R Square 0.011626088 Standard Error 9922.972969 Observations 10 14
ANOVA df SS MS F Significance F Regression 1 108889490.2 1.09E+08 1.105866 0.323707233 Residual 8 787723140.3 98465393 Total 9 896612630.5 Standard Upper Lower Upper Coefficients Error t Stat P-value Lower 95% 95% 95.0% 95.0% Intercept 43918.29485 6519.349337 6.736607 0.000147 28884.64833 58951.94 28884.65 58951.94 Total Priority Sector Advances 0.011848307 0.011266918 1.051601 0.323707-0.014133252 0.037829-0.014133 0.0378299 Table 3 Year Total Priority Sector NPAs Total Priority Sector Advances Priority Sector NPA Ratio Gross NPA Ratio 2002-03 2,546.35 24,184.00 10.53 9.65 2003-04 2,445.40 36,648.00 6.67 8.10 2004-05 2,481.93 48,920.00 5.07 5.84 2005-06 2,188.46 69,886.00 3.13 4.44 2006-07 2,284.03 106,586.00 2.05 2.46 2007-08 2,884.18 144,549.00 2.00 2.20 2008-09 3,418.53 164,068.00 2.08 2.47 2009-10 3,640.00 190,207.00 1.91 2.90 2010-11 4,792.00 215,552.00 2.22 2.74 2011-12 4,823.00 249,139.00 1.94 2.25 15
Table 4 SUMMARY OUTPUT Regression Statistics Multiple R 0.732946235 R Square 0.537210183 Adjusted R Square 0.479361456 Standard Error 2690.160547 Observations 10 ANOVA Df SS MS F Significance F Regression 1 67205811.08 67205811.08 9.286465049 0.015884373 Residual 8 57895710.13 7236963.766 Total 9 125101521.2 Coeffici Standard Lower Upper Lower Upper ents Error t Stat P-value 95% 95% 95.0% 95.0% Intercept 8202.45 1646.203 4.98264 0.00107 4406.29 11998.6 4406.29 11998.6 Total Priority 0.03436 0.011277 3.04737 0.01588 0.00836 0.06037 0.00836 0.06037 Sector Advances 5844 213 0186 4373 0543 1145 0543 1145 Table 5 (in lakh)) Bank Name Year Gross NPAs Gross Advances Gross NPAs to Gross advances ratio 16
2008-09 198392 10023935 1.98 2009-10 180717 12528339 1.44 2010-11 228153 18052415 1.26 HDFC 2011-12 181495 19096897 0.95 2008-09 8493 1244686 0.68 2009-10 6020 2224034 0.27 2010-11 8053 3443501 0.23 Yes Bank Limited 2011-12 8394 3805505 0.22 2008-09 192335 9826485 1.96 2009-10 266387 11827270 2.25 Union Bank of India 2010-11 362282 15302246 2.37 2011-12 542224 17184965 3.16 2008-09 153951 6966905 2.21 2009-10 166502 7756826 2.15 2010-11 309017 9324624 3.31 UCO Bank 2011-12 401975 10783997 3.73 Table 6 HDFC Regression Summary SUMMARY OUTPUT Regression Statistics Multiple R 0.223866 R Square 0.050116 Adjusted R Square -0.42483 Standard Error 26493.22 Observations 4 17
ANOVA Df SS MS F Significance F Regression 1 74063344 74063344 0.10552 0.776134472 Residual 2 1.4E+09 7.02E+08 Total 3 1.48E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 180168.4032 54046.4396 3.3336 0.0794-52374.6579 412711.4642-52374.6579 412711.4642 X Variable 1 0.0011 0.0035 0.3248 0.7761-0.0140 0.0162-0.0140 0.0162 Table 7 Yes Bank Regression Summary SUMMARY OUTPUT Regression Statistics Multiple R 0.166131411 R Square 0.027599646 Adjusted R Square - 0.458600532 Standard Error 1403.439568 Observations 4 ANOVA Df SS MS F Significance F Regression 1 111808.759 111808.8 0.056766 0.833868589 Residual 2 3939285.241 1969643 Total 3 4051094 18
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 7298.4785 1981.5474 3.6832 0.0664-1227.4318 15824.3887-1227.43 15824.3887 Variable 0.0002 0.0007 0.2383 0.8339-0.0028 0.0031-0.0028 0.0031 Table 8 Union Bank of IndiaRegression Summary SUMMARY OUTPUT Regression Statistics Multiple R 0.962499236 R Square 0.926404778 Adjusted R Square 0.889607168 Standard Error 50247.06005 Observations 4 ANOVA Df SS MS F Significance F Regression 1 63562720611 63562720611 25.17567741 0.037500764 Residual 2 5049534087 2524767043 Total 3 68612254698 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -252194.7791 120826.6628-2.0872 0.1721-772069.95 267680.39-772069.95 267680.39 X Variable1 0.0438 0.0087 5.0175 0.0375 0.0062 0.0814 0.0062 0.0814 19
Table 9 UCO Bank Regression Summary SUMMARY OUTPUT Regression Statistics Multiple R 0.988188061 R Square 0.976515644 Adjusted R Square 0.964773467 Standard Error 22347.02164 Observations 4 ANOVA Df SS MS F Significance F Regression 1 41530757560 41530757560 83.16307781 0.011811939 Residual 2 998778752.6 499389376.3 Total 3 42529536313 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -346365.119 67192.935-5.1547847 0.0356344-635472.98-57257.25-635472.98-57257.25279 XVariable1 0.0693868 0.0076087 9.11937924 0.0118119 0.0366491 0.102124 0.0366492 0.10212448 20