Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4, 2055-2062 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i4.1581 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: jithinbosco@gmail.com Loan delinquency in private sector banks in Kerala Jithin Bosco K1*, B. Sudha2 1,2Dept of Bank Management, Alagappa University Karaikudy Tamilnadu India; jithinbosco@gmail.com (J.B.K.) sudhab@alagappauniversity.ac.in (B.S.) Abstract: The Indian Banking Sector faces significant difficulties in recovering debt. The banker must understand the underlying causes of loan delinquency to address the problem efficiently. A delinquent loan becomes a NPL (Non Performing Loan) when the borrower failed to repay the loan for a period of 90 days or more. Delinquency is the sign of increased credit risk, warnings of willful default, poor asset quality management and operational inefficiency. It may help to predict the loan loss provision for the sub standard, doubtful and loss assets. This article explores the various causes of loan delinquencies of private-sector banks operating in Kerala. The analysis revealed that the retails and SMEs registered high level of loan delinquency. A significant difference was found between the old and new generation private sector banks for the Poor loan portfolio management, inadequate collateral, inadequate monitoring and follow-up. From the mean score it is evident that the NPA management in new generation private sector banks is appreciable when compared to old generation private sector banks. Keywords: Bank, Delinquency, NPA, NPL. 1. Introduction The Indian Banking system was dominated by the private players since the beginning of the banking system. During that period the banks which were operating in the Indian economy was on private in character and mostly controlled by the business groups. The first Private Sector Bank in India was The Nedungadi Bank, which was established in the year 1899 (the bank was merged with Punjab National Bank in 2003). The later years witnessed the formation of many local Banks in India, especially in the states of Kerala and Tamilnadu. The structured forms of banks were formed in this state, mostly in private sectors. In the meantime, many other Banks were formed by the business communities also. The year 1935 witnessed the formation of Reserve Bank of India (Reserve bank of India Act 1934) which later becomes the regulator of Indian Banking. The major changes of the Indian banking came in the year 1969 when the Government took the decision to nationalize major banking players of India. The entire picture began to change during the 90s when the government opens the Indian banking to new players. The new generation banks started their operation with high capital, new technology and they have started attracting the traditional as well as the new customers to their branches. The high level of competition compelled the old private sector banks to come out of their traditional way of doing business. Meanwhile the capacities of the traditional customers to absorb the liquidity of the banks are getting low and the banks are compelled to lend to the growing business which was outside the purview of the traditional banking. This new step to the business started the curse of the NPA in the banks. Non-performing assets are the most important issue faced by the banks in India. The higher the ratio of the NPA, the lower is the profit potentials of the banks. The Government policies, strategies of the banks, the changing economic and industrial atmosphere caused an increase in the level of NPA of the Banks. The total GNPAs of Private Sector Banks amounted to Rs.1,27,958 crore during 2022-23. https://orcid.org/0000-0002-0785-4020 2056 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 2055-2062, 2024 DOI: 10.55214/25768484.v8i4.1581 © 2024 by the author; licensee Learning Gate 1.1. Statement of the Problem Loan Delinquency occurs when borrowers fail to repay the debt as per agreed terms. In recent past the private sector banks are experiencing mounting NPAs. From 2010-11 to 2022-23 the average gross NPAs of private sector banks stood at Rs.1,05,420 crore. The book value of bad loans acquired by the asset reconstruction companies reached at Rs 7,66,915 as of March 2023 from Rs 5,88,706 crore in March 2022, registering a growth of nearly 30 percent. Against this backdrop the researcher has analysed the three major causes of loan delinquency namely internal, external and borrower related causes. Understanding and addressing these three causes will mitigate the NPA issues in private sector banks in Kerala. The researcher has compared the old generation private sector banks and new generation private sector banks with respect to the various causes of loan delinquency. This comparison will be helpful to identify the efficiency of banks in NPA Management. 1.2. Objectives of the Study 1. To analyse the various causes of loan delinquency. 2. To compare the causes of loan delinquency between old and new generation private sector banks. 1.3. Hypotheses of the Study 1. H01: There is no significant difference between old and new generation private sector banks on the internal cause of loan delinquency. 2. H02: There is no significant difference between old and new generation private sector banks on the external cause of loan delinquency. 3. H03: There is no significant difference between old and new-generation private sector banks on the borrower-related causes of loan delinquency. 2. Research Methodology 2.1. Data Sources The study requires both Primary and Secondary data. The Primary Data is collected from the 400 bankers of Private Sector Banks in Kerala. The structured questionnaire is framed for collecting the primary data. The secondary data is collected from the various publications of RBI, IBA and from the financial reports of respective banks. 2.2. Sample Size There is 2440 Private Sector bank branches operating in Kerala, 400 bank branches is selected proportionately. The sample size 331 is arrived by using Krejcie & Morgan table (1970). 2.3. Sampling Technique A Multistage sampling technique is employed. In the first stage the population is divided into three regions Northern Kerala, Central Kerala and Southern Kerala. In the second stage the number of districts in each region and number of bank branches in each district were identified. There are four districts in North Kerala viz., Kasaragod, Kannur, Wayanad, and Kozhikode; six districts in the Central Kerala viz., Palakkad, Thrissur, Ernakulam, Idukki, Malappuram, and Kottayam; and four district in the South Kerala namely, Thiruvananthapuram, Kollam, Alappuzha and Pathanamthitta. The total number of private sector bank branches operating in Kerala is 2440 bank branches. There are 888 private sector bank branches in south Kerala, 1212 branches in central Kerala and 340 branches in north Kerala. In the third stage, the sample size of each private sector bank is determined proportionately in three regions and the individual private sector bank branch in each district is chosen based on the intensity of NPA. 2057 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 2055-2062, 2024 DOI: 10.55214/25768484.v8i4.1581 © 2024 by the author; licensee Learning Gate 3. Sample Characteristics To determine the sample characteristics, a percentage analysis on demographic data was performed. Table 1. Demographic profile. Variables Groups Number Percentage Branch locality Urban 197 59 Rural 138 41 Type of bank Old generation 188 56 New generation 147 44 Experience 2-5 years 112 34 5-10 years 145 43 >10 years 78 23 Designation Manager 87 26 Chief manager 75 22 Senior manager 89 27 Credit officer 84 25 Source: Primary data The structure of the sample based on branch locality, type of bank, designation, and the number of years spent in the job is shown in Table 1. The majority of the bank branches 59% contacted for this study are located in urban areas, signifying a potential focus on urban branches and 41% located in rural areas. The private sector banks are categorized into old and new generation banks, 56% of the bank branches are old generation banks and 44% are new generation banks indicating a slight dominance of old generation banks over new generation banks. The work experience of the respondents revealed that 43% having 5-10 years of experience, followed by 34 % with 2-5 years of experience and a smaller proportion 23% having more than 10 years of experience. The researcher contacted a diverse designation of the respondents with a slight predominance of Senior Managers (27%), followed closely by Managers (26%), Credit Officers (25%) and Chief Managers (22%). 4. Garret Ranking To find out the extent of the influence of the factors, Garrett's ranking technique is adopted. The method is applied to rank the respondents' preferences based on many parameters. The respondents were asked to rank the given segment of the customers who get the maximum number of defaulters from 1 to 5, giving 1 to the highest defaulter and 5 to the least high. The merit ranking provided by the respondents was transformed into a percentage position. Percentage position = 100( 𝑅𝑖𝑗 − 0.5) Nj Where Rij = Rank given for the ith variable by jth respondents Nj = Number of variables ranked by jth respondents. Garret and Woodworth's (1969) Table, known as Garrett's Table, converts the projected percentage position into scores. Next, the mean values of the scores with the total value of the scores are calculated, adding the individual scores for each of the items. The components with the greatest mean value are considered the most significant. The result is provided in the following table. 2058 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 2055-2062, 2024 DOI: 10.55214/25768484.v8i4.1581 © 2024 by the author; licensee Learning Gate Table 2. Segment of the customers with maximum defaulters -garret ranking. Percent positions and garret values Formula Percent Score Mean Rank Individual 100(1-0.5)/5 10 75 61.3024 2 Retails and SME 100(2-0.5)/5 30 65 67.09581 1 Corporate sector 100(3-0.5)/5 50 50 57.69461 3 Service sector 100(4-0.5)/5 70 39 31.00599 5 Farmers 100(5-0.5)/5 90 24 31.67964 4 Source: SPSS output computed by the researcher using primary data. Table 2 identifies the customers segment with maximum defaulters using Garret’s Ranking Technique. Based on the result it is understood that ‘Retails and SMEs’ are the major defaulters with the highest Garret score of 65 and an average score of 67.096. Accordingly, ‘Individual’ with scores of 75 and an average score of 61.3024 is signified second. The calculation with an average score of 50 ranked ‘Corporate Sector’ third. ‘Farmers with an average score of 29 come 4th and a score of 39 with an average score of 31.00599 service sector is the least. 5. Testing of Hypothesis To examine if there is any difference between old and new generation private sector banks on reasons for loan delinquency, debt recovery procedures, debt recovery challenges and measures taken to increase debt recovery, an independent sample t-test has been used. 5.1. Internal Cause of Loan Delinquency - Variables Considered for The Analysis Lack of knowledge about credit policy and procedures (IC1), Inadequate staff training (IC2) For poor loan portfolio management (IC3) Inadequate monitoring and follow-up (IC4) Inadequate collateral (IC5) Table 3. Independent sample t-test on the internal cause of loan delinquency of private sector banks. Independent samples test Items Type of bank N Mean SD Levene's test for equality of variances t-test for equality of means F Sig. t df Sig. (2- tailed) IC1 Old generation 188 4.0638 0.72847 5.133 0.024 -1.286 333 0.199 New generation 147 4.1565 0.54522 -1.331 332.40 0.184 IC2 Old generation 188 4.0372 0.68887 0.127 0.722 -1.404 333 0.161 New generation 147 4.1361 0.56906 -1.437 331.97 0.152 IC3 Old generation 188 4.0319 0.70827 0.002 0.965 -2.217 333 0.027 New generation 147 4.1905 0.56536 -2.279 332.84 0.023 IC4 Old generation 188 4.0053 0.68986 0.009 0.926 -2.344 333 0.020 New generation 147 4.1701 0.56586 -2.401 332.21 0.017 IC5 Old generation 188 4.0479 0.68821 0.008 0.925 -2.492 333 0.023 New generation 147 4.0816 0.53006 -2.507 332.93 0.023 Source: SPSS output computed by the researcher using primary data. 2059 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 2055-2062, 2024 DOI: 10.55214/25768484.v8i4.1581 © 2024 by the author; licensee Learning Gate An independent samples t-test is performed to compare the scores for several factors on the internal cause of loan delinquency between the old and new generation private sector banks on the internal cause of loan delinquency and the results are given in the Table 3. For the lack of knowledge about credit policy and procedures (IC1), there was no significant difference in scores between old-generation (M = 4.06, SD = 0.73) and new-generation banks (M = 4.16, SD = 0.55); t(333) = −1.29, p = .199. Similarly, no significant difference was found in the scores for inadequate staff training between old generation (IC2) (M = 4.04, SD = 0.69) and new generation private sector banks (M = 4.14, SD = 0.57); t(333)=−1.40 p=.199. However, significant differences were observed in three other factors. For poor loan portfolio management (IC3), scores were significantly different between old-generation (M = 4.03, SD = 0.71) and new-generation private sector banks (M = 4.19, SD = 0.57); t(333) = −2. p = .027. In terms of inadequate monitoring and follow-up (IC4), there was a significant difference in scores between old generation (M = 4.01, SD = 0.69) and new generation private sector banks (M = 4.17, SD = 0.57); t(333)=−2.34, p=.020. Finally, for inadequate collateral (IC5), a significant difference was found between old-generation (M = 4.05, SD = 0.69) and new-generation private sector banks (M = 4.08, SD = 0.53); t(333)=−2.49, p=.023. 5.2. External Causes of Loan Delinquency – Variables Considered EC1 Impact of government schemes like debt waiver and restructuring on loan delinquency EC2 Business failures due to natural disasters EC3 Low business activities and economic slowdowns EC4 Changes in laws and regulations EC5 Global economic conditions Table 4. Independent sample t-test on external causes of loan delinquency of private sector banks. Independent samples test Items Type of bank N Mean SD Levene's test for equality of variances t-test for equality of means F Sig. t df Sig.(2- tailed) EC1 Old generation 188 3.8670 0.73737 10.288 0.001 -1.576 333 0.116 New generation 147 3.9864 0.61917 -1.610 331.28 0.108 EC2 Old generation 188 3.8617 0.72541 0.819 0.366 -2.591 333 0.010 New generation 147 4.0612 0.66440 -2.619 324.74 0.009 EC3 Old generation 188 3.8670 0.73009 0.706 0.401 -1.865 333 0.063 New generation 147 4.0136 0.69229 -1.878 320.88 0.061 EC4 Old generation 188 3.5904 0.85738 17.109 0.000 -2.354 333 0.019 New generation 147 3.7959 0.70165 -2.412 332.29 0.016 EC5 Old generation 188 3.8777 0.74642 19.175 0.000 -1.578 333 0.115 New generation 147 3.9932 0.54265 -1.639 331.34 0.102 Source: SPSS output computed by the researcher using primary Data. The results in table 4 indicated that there is no significant differences between old (M = 3.867, SD = 0.737) and new generation banks (M = 3.9864, SD = 0.61917) regarding the impact of government schemes like debt waiver and restructuring on loan delinquency (t(333) = -1.576, p = 0.116). However, significant differences were found in perceptions of the impact of business failures due to natural disasters (t(333) = -2.591, p = 0.01) and low business activities and economic slowdowns (t(333) = - 2060 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 2055-2062, 2024 DOI: 10.55214/25768484.v8i4.1581 © 2024 by the author; licensee Learning Gate 2.354, p = 0.019) between old and new generation private sector banks. Conversely, no significant differences were observed in perceptions of changes in laws and regulations (t(333) = -1.865, p = 0.063) and global economic conditions (t(333) = -1.578, p = 0.115) on loan delinquency between the two types of banks. Overall, the findings suggest varying perceptions among banks regarding specific external factors influencing loan delinquency, highlighting potential areas for targeted risk management strategies. 5.3. Borrower Related Causes of Loan Delinquency – Variables Considered Business failure due to poor financial management and competition (BC1), Loan funds being diverted to other activities (BC2) Multiple borrowing (BC3) Job loss (BC4) Table 5. Independent sample t-test on borrower-related causes of loan delinquency in private sector banks. Independent samples test Items Type of bank N Mean SD Levene's test for equality of variances t-test for equality of means F Sig. t df Sig. (2- tailed) BC1 Old generation 188 0.98 0.73 1.85 .174 -3.16 333 .002 New generation 147 0.88 0.55 -3.20 327.31 .002 BC2 Old generation 188 0.94 0.69 5.83 .016 -5.20 333 .000 New generation 147 0.81 0.57 -5.30 330.19 .000 BC3 Old generation 188 0.97 0.71 15.57 .000 -5.10 333 .000 New generation 147 0.76 0.57 -5.26 332.99 .000 BC4 Old generation 188 0.86 0.69 17.10 .000 -2.35 333 .019 New generation 147 0.70 0.57 -2.41 332.29 .016 Source: SPSS output computed by the researcher using primary Data. The borrower-related causes of loan delinquency are compared and the result is given in Table 5. An independent samples t-test showed that the old banks and new generation private sector banks differ in all the borrower-related causes. Firstly, there is a notable discrepancy regarding business failure due to poor financial management and competition (BC1), with old-generation banks perceiving this factor (M = 0.98, SD = 0.73) as more influential compared to new-generation banks (M = 0.88, SD = 0.55), a statistically significant difference (t(333) = -3.16, p = 0.002). Similarly, perceptions diverge significantly on the issue of loan funds being diverted to other activities (BC2), where old-generation banks (M = 0.94, SD = 0.69) attribute more risk compared to new-generation banks (M = 0.81, SD = 0.57), with a strong effect size (t(333) = -5.20, p < 0.001). Additionally, there is a substantial difference in views concerning multiple borrowing (BC3), with old-generation banks (M = 0.97, SD = 0.71) viewing it as riskier than new-generation banks (M = 0.76, SD = 0.57), showing a significant statistical distinction (t(333) = -5.10, p < 0.001). Finally, perceptions of job loss (BC4) impacting loan delinquency differ significantly, as old-generation banks (M = 0.86, SD = 0.69) attribute higher risk compared to new- generation banks (M = 0.70, SD = 0.57), with a statistically significant finding (t(333) = -2.35, p = 0.019). These findings show varying risk assessments and possibly different approaches to borrower risk management strategies between old and new-generation banks, suggesting the need for tailored 2061 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 4: 2055-2062, 2024 DOI: 10.55214/25768484.v8i4.1581 © 2024 by the author; licensee Learning Gate risk mitigation approaches and borrower support initiatives to address these divergent perceptions effectively. 6. Results and Discussion The majority of the bank branches 59% are located in urban areas and 43% of the respondents having 5-10 years of experience. The Senior Managers (27%) are contacted more, Retail and SMEs registered major defaulters. A significant difference is found between the old and new generation private sector banks in the following factors viz., Poor loan portfolio management, inadequate monitoring and follow-up and inadequate collateral of internal causes of loan delinquency. In the case of external causes of loan delinquency, a significant difference were found in the perception of the impact of business failures due to natural disasters and low business activities and economic slowdowns between old and new generation private sector banks. The borrower-related causes of loan delinquency revealed a significant difference in the business failure due to poor financial management and competition, issue of loan funds being diverted to other activities, multiple borrowing, job loss and risk management strategies. It is revealed from the interaction with the bankers, the metro branches have given huge loans to construction sectors which are under stress now. Hence banks need to reconsider the sectoral portfolio of credit and re fix the margin and recovery measures. Also some banks are monopolized by gold loan portfolios’ RBI need to fix a sectoral exposure to such concentrating portfolio in order to avoid any kind of sectoral failures. Also banks need to advance on other retails products than concentrating on a single product in the name of profit generation. In case of unsecured loans if the defaulter made the dues intentionally, criminal action to be taken against the borrower than filing civil suit and revenue recovery actions since the banks credit is public money and non-repayment of dues is a crime against the society and nations well-being. 7. Conclusion It is always better to grant loans to those sectors which have regular cash flow and business income flow like salaried people and high rated companies/firms. Hence proper analysis of income generation sources both for present and future period is necessary. Adequate margin and exposure limit need to fix in case any risk is found to be occurred in future time. The urban branches have given huge loans to construction sector which is under stress now. Hence banks need to re-consider the sector-wise portfolio of credit and re fix the margin and recovery measures. Also some banks are monopolized by gold loan portfolios ’RBI need to fix a sector-wise exposure to such concentrating portfolio in order to avoid any kind of sector-wise failures. Also banks need to focus on diversified retail products than concentrating on a single product for earning high interest income. It is concluded that new generation banks are at better position in the NPA management and the old generation banks must adopt technology in credit management to mitigate the risk arise from NPA. Copyright: © 2024 by the authors. 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