Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5, 2355-2371 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 10 March 2025; Revised: 23 April 2025; Accepted: 28 April 2025; Published: 23 May 2025 * Correspondence: prasanna.mba@crescent.education Performance analysis of Indian private sector banks: Insights from CAMELS and statistical technique Sunil Sharma1, S Prasanna1*, Amir Ahmad Dar2 1Department of Management Studies, B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India; prasanna.mba@crescent.education (S.S.). 2Department of Statistics, Lovely Professional University, Punjab, India. Abstract: The performance of banks is crucial for a country’s economic development as they serve as important financial intermediaries. This study aims to evaluate the performance of private sector banks in India using the CAMELS model. The CAMELS model evaluates bank performance based on six parameters: Capital Adequacy, Asset Quality, Management Quality, Earnings, Liquidity, and Sensitivity to Market Risk. Regression analysis and ANOVA are employed to examine the influence of these CAMELS parameters on banks' return on assets (ROA). Additionally, a weighted average rating technique is used to rank each bank according to the CAMELS parameters. The study utilizes data from 20 banks in the private sector over 24 years from 2000 to 2024 to evaluate these ratios. The study provides insights into the financial health of India’s private sector banks, highlighting the significance of CAMELS parameters in determining bank performance. The results indicate which factors most strongly influence ROA and how banks rank based on their overall financial stability. The research underscores the importance of continuous monitoring of CAMELS parameters to ensure the sustainable performance of banks. The findings serve as a valuable tool for stakeholders, including investors, policymakers, and regulators, by enabling informed decision-making regarding bank performance and stability. Keywords: ANOVA, Bank performance, CAMELS parameters, Economic development, Regression, ROA. 1. Introduction Banks hold a prominent position within the financial system due to their crucial role in promoting economic growth. They fulfil important functions such as maturity transformation and providing essential support for payments and deposits [1]. However, various factors have exposed banks to a range of risks, including increased market volatility, heightened competition, diversification, global integration of financial markets, along with cross-border activities, and expansion into various other financial parts. Furthermore, the advent of complex products, processes, and digitalization has introduced new risks and challenges for banks. To address these risks and challenges, close bank supervision is essential, driven primarily by the need to safeguard depositors' interests and ensure overall financial stability. The occurrence of recurring bank failures, mergers, and the recent impact of the Covid-19 pandemic has resulted in a rise in non- performing assets (NPAs) globally over the past decade. Consequently, bank supervisors worldwide have made determined efforts to mitigate the effects of bank failures and contagion through the implementation of "safety nets" such as deposit insurance and liquidity support or capital injections provided by central banks and governments. The financial performance of banks and other financial institutions is commonly assessed through a combination of methods, including financial ratio analysis, benchmarking, and comparing performance against budgetary targets [2]. These methodologies, as stated by Avkiran [3] are utilized to measure 2356 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate and evaluate the performance of these institutions. In simpler accounting terms, the performance of banks is determined by their ability to generate sustainable profitability [4]. To evaluate their performance and identify strengths and weaknesses, banks rely on various financial ratios. The traditional approach of utilizing financial ratios to assess the performance of banks has been widely practised, with practitioners employing CAMELS ratings to gauge the financial health and performance of their institutions. The CAMELS rating system serves as a tool for bank management to evaluate their institution's performance [4, 5]. During the 1990s, a specialised group led by Shri S. Padmanabhan carried out comprehensive assessments of Reserve Bank of India’s (RBI’s) supervisory processes. The group specifically focused on evaluating the systems and procedures pertaining to statutory inspections. Their objective was to identify areas for improvement and propose measures to enhance the efficacy and efficiency of the RBI's method to supervising banks [6]. 1.1. Supervision of Banks By maintaining banks' stability and security, an efficient supervisory system is crucial in reducing the likelihood of bank collapses. There are mainly two types of supervision methods available, and these are shown in Figure 1: Figure 1. Supervision method. The RBI conducts on-site bank examinations as a fundamental part of its supervisory process. These examinations involve regular visits to banks, interviews with management, and assessments of financial statements, along with accounting records, internal controls, and also banking regulation compliance. Based on the findings, bank supervisors assign composite ratings to the banks using the CAMEL rating system. Off-site surveillance relies on call reports filed by banks, providing information on their condition and income between on-site examinations. Supervisors use supervisory screens and econometric models, which analyse financial ratios and statistical tests to assess the bank's overall condition during off-site monitoring. These tools allow for ongoing monitoring and comparison of a bank's performance with industry peers. The acronym CAMELS represents the following factors in the context of bank assessment: • C - Capital adequacy • A - Asset quality • M - Management Competency • E - Earning • L - Liquidity • S – Sensitivity 2357 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Globally, the CAMELS rating system is utilised to evaluate financial firms by regulatory agencies [7]. These factors include Capital adequacy along with Asset quality, and also Management soundness, along with Earnings and profitability, and also Liquidity, and Sensitivity. Over time, the framework was further enhanced with the inclusion of the sixth component, Sensitivity to market risk; 1997 [8, 9]. In the context of Indian banks, the supervisors explored various methodologies for bank supervision. In 1995, they identified the CAMELS model as a suitable approach for Indian banking supervision. The adoption of this model was driven by several factors: • Alignment with global banking standards and norms. • Facilitating the adoption of best practices followed by global counterparts in the future. • Eliminating resistance and facilitating the establishment and ease of doing business for foreign banks and private banks alike. etc. In this study, the CAMELS rating system is a widely used framework for assessing the performance and stability of banks, focusing on six critical parameters: Capital Adequacy, Asset Quality, Management Quality, Earnings, Liquidity, and Sensitivity to Market Risk. Each bank is rated on a scale of 1 (best) to 5 (worst) for these factors, reflecting their financial health and operational efficiency. This study utilizes the CAMELS framework to evaluate the performance of selected banks and employs statistical techniques such as regression analysis and ANOVA to determine which parameters most significantly impact overall performance, providing actionable insights for improving banking operations and stability. 2. Literature Review The CAMELS model was utilized in this study to assess and analyze banks' financial performance. According to Sarker [10] CAMELS ratings offer insights into a bank's overall stability and assist in identifying or forecasting various risk factors that could lead to potential issues or even bank failure. Athanasoglou, et al. [11]; Dang [12]; Ilhomovich [13]; Ong and Heng [14]; Nazir [15] and Sarker [10] provide further elaboration on several components of the CAMELS framework. The Figure 2 shows the factors of the CAMELS, that effects the ROA. It is a key financial metric that can be integrated into CAMELS (Capital adequacy, Asset quality, Management quality, Earnings, Liquidity, and Sensitivity to market risk) studies, particularly in the analysis of financial institutions' efficiency and valuation. 2358 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Figure 2. CAMELS Factors. Capital Adequacy (C): Banks with higher ranks have a healthier capital adequacy position, indicating a stronger ability to withstand financial risks. Asset Quality (A): Lower ranks indicate better asset quality, with lower debt in relation to owner's funds. Higher ranks signify better financial charge coverage and a higher proportion of advances or loans to available funds. Management Quality (M): Higher ranks suggest higher interest income, net interest income, and better coverage of interest expenses. Banks with higher ranks also generate higher profits per employee and per branch, as well as higher business revenue per employee and per branch. Earnings Capability (E): Banks with higher ranks have a higher net interest margin, a higher ratio of interest income to total assets, and a lower ratio of interest expenses to total assets. These factors indicate better profitability and earnings capacity. Liquidity Position (L): Higher ranks reflect a stronger liquidity position, with a higher cash deposit ratio, better current ratio, and higher quick ratio. These indicate a bank's ability to meet short-term obligations and manage liquidity effectively. Sensitivity to Market Risk (S): Higher ranks represent higher earnings per share, a higher price-to- book value ratio (indicating a higher market valuation), and a lower ratio of earnings per share to market price-earnings yield (indicating better valuation). This section presents the empirical data that are pertinent to the CAMEL rating, which is used to measure a bank's financial performance. Dang [12] found that a uniform financial Institutions Rating System is called the CAMEL rating, which was the Federal Financial Institution Examination Council officially adopted on November 13, 1979. Subsequently, in October 1987, it was also adopted by NCUA. The CAMEL rating has proven to be a highly effective internal supervisory tool for assessing the financial soundness of a firm. Its purpose is to identify institutions that may require special attention or raise concerns within the regulatory framework. 2359 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Examiners, along with regulators have found CAMEL rating to be a vital tool, as determined by Barr, et al. [16]. Financial statements, financing sources, macroeconomic statistics, along with budget, and also cash flow are only some variables that go into this grade, which plays a significant part in determining a bank's overall health. Hirtle and Lopez [17] stressed the need of maintaining the privacy of a bank's CAMEL rating. The bank's upper management and the appropriate supervisory personnel are the only people with access to this data. Even on a delayed basis, the rating is not shared with the general public. Its confidentiality is maintained to enable the bank's senior management to devise appropriate business strategies and for the benefit of supervisory staff in carrying out their duties effectively. In contrast to Babar and Zeb [18] and Sarwar and Asif [19] who found that capital adequacy received the highest rating in Pakistan, Rozzani and Rahman [4] observed that management quality achieved the highest overall rating. Furthermore, Christopoulos, et al. [20] reported a consistent decline in capital ratios over time, indicating a worsening financial condition. As illustrated by the Lehman Brothers case, this trend suggested an increase in false and questionable claims, as well as limited access to the capital market. According to Christopoulos, et al. [20] the asset quality ratio improved over time, indicating a diminished capacity to recognize, evaluate, track, and manage credit risks. This was evidenced by the rise in negative and questionable assertions made by Lehman Brothers. The bank's lending practices, which involved extending credit to high-risk and insolvent clients, resulted in an annual increase in non-performing loans, reflecting the bank's growing volume of poor and doubtful loans. According to Majithiya and Pattani [21] a high management quality rating indicates that these banks have experienced rapid growth and that their employees are highly skilled, both of which are expected to drive future expansion. Conversely, Christopoulos, et al. [20] observed a steady decline in the management ratio over time, suggesting that a significant number of loans were non-performing due to inadequate borrower evaluation, a responsibility overseen by Lehman Brothers' executives. According to Hasbi and Haruman [22] ROA declined despite increases in the capital adequacy ratio (CAR), non-performing financing (NPF), operational efficiency (OEOI), and financing-to-deposit ratio (FDR). By providing substantial financing, particularly to large enterprises, the Islamic bank focused on enhancing profit-sharing to attract clients from traditional banks. Additionally, the bank utilized all deposited funds alongside internal equity to maximize profit-sharing or achieve greater spread margins, rather than prioritizing responsible credit and risk management. Ongore and Kusa [23] found that both ROA and net interest margin (NIM) are significantly positively influenced by capital sufficiency (measured by the ratio of total capital to total assets) and managerial efficiency (measured by the ratio of total operating revenue to total profit) in their study of Kenyan commercial banks. Conversely, ROE is negatively affected by capital adequacy, while ROA, ROE, and NIM are negatively impacted by asset quality, which is assessed by the ratio of non- performing loans to total loans. The ratio of total loans to total client deposits, which gauges liquidity, does not show significant changes. The findings indicate that capital sufficiency has a mixed effect on bank performance, negatively impacting ROE but positively influencing ROA and NIM. Frederick [24] found that while earnings ability (measured by net interest margin to total assets) had a statistically significant positive impact on ROA, management efficiency (measured by the ratio of operating costs to total income) and asset quality (measured by loan loss provisions to total loans) had a statistically significant negative impact. Frederick's study focused on Ugandan commercial banks. ROA was not statistically affected by capital sufficiency, as determined by the ratio of equity capital to total assets. However, earnings ability had a substantial positive impact when performance was assessed by ROE, while capital adequacy, asset quality, and management efficiency all exhibited significant negative effects. The study underscores that the influence of these factors varies depending on the performance measure, as capital adequacy, for instance, negatively impacts ROE but not ROA. 2360 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate According to Cekrezi [25] the ROA of Albanian commercial banks was statistically and significantly negatively impacted by both capital adequacy (measured by the ratio of total equity to total assets) and liquidity (measured by the ratio of total loans to total assets). Getahun [26] examined the financial performance of Ethiopian commercial banks and discovered that (1) management efficiency (measured by non-interest expense relative to net interest income plus non-interest income) and asset quality (measured by loan provision to total loans) had a statistically significant negative impact on ROA, while earnings ability (measured by net interest income to total interest income) and liquidity (measured by total loans to total deposits) had a significant positive impact. Capital adequacy (measured by gross capital to total assets) had no discernible effect on ROA. (2) When looking at ROE, asset quality had no significant impact, earnings ability and liquidity had substantial positive benefits, while capital sufficiency and management efficiency had considerable negative effects. This study illustrates that the CAMEL model aids in evaluating banks’ financial performance, yet it yields varying results for ROA and ROE. For instance, asset quality significantly influences ROA but not ROE, whereas capital adequacy significantly affects ROE but not ROA. Specifically, Veni [27] examined banks' capital adequacy standards and the methods they utilise to increase their capital ratios. Also, the author emphasised that rating agencies utilise the CAMEL model to evaluate the bank's CDs, FDIC insurance, and bonds. Within this model, particular emphasis is placed on the capital ratios of banks when assigning ratings. Baral [28] identified that the financial health of joint ventures was found to be more effective than that of commercial banks. The CAMEL model demonstrated that managing the potential impact on their balance sheets was not challenging for joint venture banks. Wirnkar and Tanko [29] conducted an analysis of the CAMEL model's adequacy in assessing the overall performance of Nigerian banks from 1997 to 2005. The findings revealed that each component of CAMEL alone was insufficient to capture the complete performance of a bank. Based on his research, Al-Tamimi [30] concluded that liquidity and concentration were important indicators of conventional banks' performance in the UAE between 1996 and 2008, while cost along with the number of branches were important indicators of Islamic banks' performance during the same time period. According to the reviewed literature, the CAMEL model serves as a practical tool for analyzing components to metrics such as ROA, ROE, and NIM has yielded inconsistent results. Furthermore, various measures were selected to represent aspects like liquidity, leading different researchers to utilize distinct ratios for calculating CAMEL components. For instance, some researchers employed total loans to total assets, total loans to total deposits, or total loans to total customer deposits. This methodological variation indicates that the application of CAMEL components to ROA, ROE, and NIM may influence the ratings that commercial banks receive. and evaluating the financial performance of commercial banks. However, applying CAMEL 3. Research Methodology 3.1. Data Collection and Sampling The researcher regularly analyses data spanning from 2000 to 2024. The researcher includes 20 private banks in India as its sample, selected based on their classification and the availability of comprehensive data from 2000 to 2024. The total observations were determined by multiplying the number of banks (20) with 31 variables and the number of years (24). The data collection process involved accessing the annual reports and auditor reports of each bank for the specified period. From these reports, financial statements, key performance indicators, and other relevant information were collected, which were crucial for calculating the predetermined ratios used in analysing bank performance. Data sourced CMIE -Centre for Monitoring Indian Economy 2361 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate 3.2. Proposed Model The proposed model incorporates the concept of heterogeneous groups within the CAMELS six components to assess the indicators of bank performance in Indian commercial banks using the CAMEL model. Following is the Table 1 for a clear understanding of the variables: Table 1. Variables in Model. Acronym Figures in Rs. Million/ % Ratio name Dependent variable TOBIN Q (%) TOBIN Q (%) Return on net worth (%) Return on total assets (%) Return on assets Capital Rs. Million Capital employed (%) Capital adequacy ratio (in per cent) (Time series) Asset Rs. Million Average total assets Rs. Million Average loan and advances (%) Net non-performing assets (NNPA) to net advances (in per cent) Rs. Million Total assets Rs. Million Current assets incl long term portion Management Rs. Million Profit/Loss per employee (%) PBT as % of total income (%) PAT as % of total income (%) Net profit margin (%) Contingent liabilities / Net worth (%) Times Employees utilisation ratio(times) Earning Nos. No. of branches Nos. No. of employees Rs. Million Other income (%) Interest income as a percentage to working funds Rs. Million Interest income (%) PAT as % of capital employed Liquidty Rs. Million Current liabilities Times Debt to equity ratio (times) Times Quick ratio (times) Times Quick ratio (times) Rs. Million Decrease increase in working capital Sensitvity Indian Rupee Eps basic, AS 20 (%) Return on net worth Rs. Million Total term liabilities PROFIT/GDP PROFIT/GDP The CAMELS rating system evaluates a bank's performance based on six key parameters: Capital Adequacy, Asset Quality, Management Quality, Earnings, Liquidity, and Sensitivity to Market Risk. Each bank is assigned a rating from 1 (best) to 5 (worst) for these factors, indicating their financial health and operational stability. The methodology involves analyzing financial statements, risk profiles, and compliance with regulatory norms to identify well-performing and underperforming banks. The objective of this study is also to develop a regression model to predict ROA using 26 factors from a CAMEL analysis framework as shown in Figure 2 and Table 1. Initially, all 28 factors were included in the regression model, and a regression line was drawn to examine their impact on ROA. However, it was observed that several factors had negligible effects, as indicated by coefficients approximately equal to zero. These insignificant factors were systematically removed to refine the model, and a new regression line was generated. The refined model showed an improved fit, with an R-squared value of 97%, indicating that the selected factors explained a substantial proportion of the variability in ROA. To further validate the model, the percentage contribution of each factor was calculated to identify the most influential 2362 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate variables affecting ROE. This analysis highlighted key factors with significant contributions while confirming the minimal impact of others. Additionally, diagnostic checks were performed to ensure the validity of the regression assumptions. Residual analysis confirmed that the residuals followed a normal distribution, supporting the appropriateness of the model. Overall, this methodology ensured a robust and interpretable regression equation for predicting ROA, based on a streamlined set of impactful factors. 4. Model Analysis and Discussion The model evaluates the capital adequacy ratio to determine if the bank possesses sufficient capital to absorb potential losses. Additionally, it assesses asset quality by examining credit quality and diversification. Management capabilities are analysed to assess the ability to identify profitable opportunities while effectively managing risk. Earnings are considered to gauge the return on capital and the quality of earnings. Liquidity is evaluated to determine the ability to meet current liabilities. Lastly, sensitivity is taken into account, which measures exposure to changes in interest rates, foreign exchange rates, market risk, security prices, commodity prices, and other factors. 4.1. Model Analysis To generate the CAMELS rating, each of the six components is individually rated on a scale of 1 to 5, 1 being the best and 5 being the worst. A weighted rating is then calculated by assigning weights to each component. In Table 3, the CAMEL rating is assigned, with a score of 1 indicating a very healthy bank, 2 denoting a healthy bank, 3 representing an average bank, 4 indicating an unhealthy bank, and 5 signifying a weak bank. 2363 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Table 2. CAMELS Ranking Law. Acronym Figures in Rs. Million/ % Ratio name RANK 1 RANK 2 RANK 3 RANK 4 RANK 5 Remarks Dependent variable TOBIN Q (%) TOBIN Q >1 >.50 >0.10 >.50 <.49 Higher is better (%) Return on net worth >15 >10 >5 >=1 <1 Higher is better (%) Return on total assets >3.5 >1 >0.50 >.10 <.09 Higher is better (%) Return on assets >4 >2 >0.85 >.10 <.09 Higher is better Capital Rs. Million Capital employed >5000000 >2500000 >1000000 >500000 <499999 Higher is better (%) Capital adequacy ratio (in per cent) (Time series) >15 >13 >10.16 > 8.3 <8.2 Higher is better Asset Rs. Million Average total assets >7500000 >2000000 >1000000 >500000 <499999 Higher is better Rs. Million Average loan and advances >5000000 >1000000 >500000 >100000 <99999 Higher is better (%) Net non-performing assets (NNPA) to net advances (in per cent) <.99 <1.5 <2.5 >2.6 >3 lesser is better Rs. Million Total assets >15000000 >5000000 >1000000 <1000000 <499999 Higher is better Rs. Million Current assets incl long term portion >1000000 >500000 >250000 <249999 <100000 Higher is better Management Rs. Million Profit/Loss per employee >2 >1 >.5 >.25 <.25 Higher is better (%) PBT as % of total income >30 >15 >11 >1 <.99 Higher is better (%) PAT as % of total income >20 >15 >8 >1 <.99 Higher is better (%) Net profit margin >20 >15 >7 >1 <.99 Higher is better (%) Contingent liabilities / Net worth (%) <100 >100 >500 >1000 >2000 lesser is betteer Times Employees utilisation ratio(times) >20 >15 >10 >5 <4.99 Higher is better Earning Nos. No. of branches >5000 >2500 >1500 >500 <499 Higher is better Nos. No. of employees >125000 >75000 >15000 >5000 <4999 Higher is better Rs. Million Other income >25000 >10000 >1000 >500 <499 Higher is better (%) Interest income as a percentage to working funds >100 >50 >10 >5 <4.99 Higher is better Rs. Million Interest income >1000000 >100000 >25000 >10000 <9999 Higher is better (%) PAT as % of capital employed >15 >10 >5 >1 <.99 Higher is better Liquidty Rs. Million Current liabilities >700000 >300000 >100000 >50000 <49999 Higher is better Times Debt to equity ratio (times) >5 >2 >1 >.50 <.49 Higher is better Times Quick ratio (times) >15 >10 >5 >1 <.99 Higher is better Times Quick ratio (times) >15 >10 >5 >2 <1.99 Higher is better Rs. Million Decrease increase in working capital >700000 >200000 >15000 >10000 <9999 Higher is better Sensitvity Indian Rupee Eps basic, AS 20 >5000 >1000 >200 >10 <9.99 Higher is better 2364 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate (%) Return on net worth >20 >10 >5 >1 <.99 Higher is better Rs. Million Total term liabilities >100000 >100000 >50000 >10000 <9999 Higher is better PROFIT/GDP PROFIT/GDP >600 >300 >100 >50 <49.99 Higher is better Table 3. Ranking. Bank Name Axis Bank Ltd. Bandhan Bank Ltd. C S B Bank Ltd. City Union Bank Ltd. D C B Bank Ltd. Dhanlaxmi Bank Ltd. Federal Bank Ltd. H D F C Bank Ltd. I C I C I Bank Ltd. I D F C First Bank Ltd. RANKING 2.6 2.4 3.4 2.6 3.5 3.7 3.9 2.4 2.5 3.4 Bank Name Indusind Bank Ltd. Jammu & Kashmir Bank Ltd. Karnataka Bank Ltd. Karur Vysya Bank Ltd. Kotak Mahindra Bank Ltd. Nainital Bank Ltd. R B L Bank Ltd. South Indian Bank Ltd. Tamilnad Mercantile Bank Ltd. Yes Bank Ltd. RANKING 3.7 3.6 3.8 3.9 2.5 3.2 3.8 3.8 2.6 3.7 2365 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Table 2 provides rankings law as per the CAMEL rating system, which assesses the financial health and performance of banks across various factors. Here are the Calculations and interpretations: According to the CAMELS rating system, a ranking of 1 indicates the highest level of performance and is considered the best, while a ranking of 5 signifies the lowest level of performance and is considered the worst. In our calculation Indian Banking System is well placed in the range of 2.5 to 4. RBI regulatory Bodies is doing very good job therefore all Regulatory ratio are well placed. IBS have need to improve their CAMELS Ratio and we can move from 2.5 Ranking to 1.5 onwards. Analysis shown that HDFC Bank Ltd., Bandhan Bank Ltd., ICICI Bank Ltd., Axis Bank Ltd., City Union Bank Ltd. Is having rating of 2.4 ,2.5 ,2.6 respectively which is the best ranking in Indian banking system. Bank is doing good performance lets they will continue to do the same and work on improving the CAMELS Ranking. Karur Vysya Bank Ltd., Karnataka Bank Ltd., South Indian Bank Ltd., RBL Bank Ltd., Yes Bank Ltd., IndusInd Bank Ltd., and Dhanlaxmi Bank Ltd. have CAMELS ratings of 3.9, 3.8, 3.8, 3.8, 3.7 and 3.7, respective as shown in Table 3. As the highest ranking in the banking system corresponds to the lowest score, it is recommended that these banks take corrective actions to improve their CAMELS ratings and strengthen their overall performance. Overall, the rankings provide insights into the financial strength and performance of banks across different dimensions, enabling comparisons and monitoring of changes over time. 4.2. Regression Equation The regression analysis examines the influence of various financial variables on the rate of return, revealing key insights [31]. Significant predictors include PAT as % of total income, interest income as a percentage, employees' utilization ratio, and quick ratio, all with P-values below 0.05, indicating a strong impact. Conversely, variables like net non-performing assets, profit/loss per employee, and PROFIT/GDP are non-significant, showing limited influence. Negative coefficients for factors such as net profit margin, quick ratio, and debt-to-equity ratio suggest an inverse relationship with the rate of return. However, high Variance Inflation Factors (VIF) for PAT as % of total income (57.08), net profit margin (44.78), and PBT as % of total income (27.16) highlight multicollinearity issues, which could compromise the reliability of estimates as shown in Table 4. This analysis underscores critical financial metrics affecting returns while suggesting the need for addressing multicollinearity for more robust conclusions. 𝑹𝒆𝒕𝒖𝒓𝒏 𝒐𝒏 𝒂𝒔𝒔𝒆𝒕𝒔 = −0.7118 + 0.00295 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑎𝑑𝑒𝑞𝑢𝑎𝑐𝑦 𝑟𝑎𝑡𝑖𝑜 (𝑖𝑛 𝑝𝑒𝑟 + 0.00302 𝑁𝑒𝑡 𝑛𝑜𝑛 − 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑎𝑠𝑠𝑒𝑡𝑠 (𝑁𝑁𝑃𝐴 − 0.00250 𝑃𝑟𝑜𝑓𝑖𝑡/𝐿𝑜𝑠𝑠 𝑝𝑒𝑟 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑒 + 0.00812 𝑃𝐵𝑇 𝑎𝑠 % 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 + 0.10231 𝑃𝐴𝑇 𝑎𝑠 % 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑖𝑛𝑐𝑜𝑚𝑒 − 0.01510 𝑁𝑒𝑡 𝑝𝑟𝑜𝑓𝑖𝑡 𝑚𝑎𝑟𝑔𝑖𝑛 − 0.00541 𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠 𝑢𝑡𝑖𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛 𝑟𝑎𝑡𝑖𝑜(𝑡𝑖𝑚 + 0.09290 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑖𝑛𝑐𝑜𝑚𝑒 𝑎𝑠 𝑎 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 − 0.00792 𝑃𝐴𝑇 𝑎𝑠 % 𝑜𝑓 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑 − 0.01766 𝐷𝑒𝑏𝑡 𝑡𝑜 𝑒𝑞𝑢𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜 (𝑡𝑖𝑚𝑒𝑠) − 0.01101 𝑄𝑢𝑖𝑐𝑘 𝑟𝑎𝑡𝑖𝑜 (𝑡𝑖𝑚𝑒𝑠) + 0.00532 𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑛𝑒𝑡 𝑤𝑜𝑟𝑡ℎ_1 + 0.000051 𝑃𝑅𝑂𝐹𝐼𝑇/𝐺𝐷𝑃 2366 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Table 4. Coefficients. Term Coef SE Coef T-Value P-Value VIF Constant -0.7118 0.0649 -10.96 0.000 Capital adequacy ratio (in per 0.00295 0.00199 1.48 0.139 1.64 Net non-performing assets (NNPA 0.00302 0.00356 0.85 0.398 1.54 Profit/Loss per employee -0.00250 0.00291 -0.86 0.391 1.08 PBT as % of total income 0.00812 0.00386 2.10 0.036 27.16 PAT as % of total income 0.10231 0.00762 13.43 0.000 57.08 Net profit margin -0.01510 0.00646 -2.34 0.020 44.78 Employees utilisation ratio(tim -0.00541 0.00108 -4.99 0.000 1.10 Interest income as a percentage 0.09290 0.00669 13.89 0.000 1.43 PAT as % of capital employed -0.00792 0.00265 -2.99 0.003 6.49 Debt to equity ratio (times) -0.01766 0.00970 -1.82 0.070 2.12 Quick ratio (times) -0.01101 0.00310 -3.56 0.000 1.42 Return on net worth_1 0.00532 0.00221 2.40 0.017 10.16 PROFIT/GDP 0.000051 0.000070 0.73 0.463 1.91 Table 5. Model Summary. S R-sq R-sq(adj) R-sq(pred) 0.123925 97.08% 96.96% 96.15% The model summary shown in Table 5 indicates a strong fit, with an R-squared value of 97.08%, showing that 97.08% of the variability in the response variable is explained by the predictors. The adjusted R-squared (96.96%) accounts for the number of predictors, suggesting the model remains robust even after adjusting for potential overfitting. The predicted R-squared (96.15%) reflects the model's predictive accuracy on new data, confirming its reliability. A standard error of 0.123925 implies that the average deviation of the observed values from the fitted values is relatively small, further supporting the model's effectiveness in capturing the underlying data patterns. 4.3. Normality The residual plots indicate that the regression model for "ROA" fits the data well, with no significant violations of assumptions [32]. The Normal Probability Plot shows that residuals are approximately normally distributed, with only minor deviations at the extremes. The Versus Fits Plot displays a random scatter of residuals around zero, supporting linearity and constant variance. The Histogram reveals a roughly symmetric, bell-shaped distribution centered near zero, reinforcing the normality assumption. Finally, the Versus Order Plot shows no systematic patterns, indicating that the residuals are independent. Overall, the model diagnostics confirm its validity with only minor deviations as shown in Figure 3. 2367 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Figure 3. Normality plots for ROA. 4.4. ANOVA The ANOVA table provides insights into the variability explained by the regression model and its individual predictors [33]. The model explains a significant proportion of the variance in the response variable, as indicated by the regression F-value of 804.60 (P < 0.001). This confirms the overall significance of the model as shown in Table 6. Table 6. Analysis of Variance. Source DF Adj SS Adj MS F-Value P-Value Regression 13 160.635 12.3565 804.60 0.000 Capital adequacy ratio (in per 1 0.034 0.0338 2.20 0.139 Net non-performing assets (NNPA 1 0.011 0.0110 0.72 0.398 Profit/Loss per employee 1 0.011 0.0113 0.74 0.391 PBT as % of total income 1 0.068 0.0680 4.43 0.036 PAT as % of total income 1 2.771 2.7707 180.41 0.000 Net profit margin 1 0.084 0.0838 5.46 0.020 Employees utilisation ratio(tim 1 0.383 0.3828 24.93 0.000 Interest income as a percentage 1 2.963 2.9626 192.91 0.000 PAT as % of capital employed 1 0.137 0.1372 8.94 0.003 Debt to equity ratio (times) 1 0.051 0.0509 3.31 0.070 Quick ratio (times) 1 0.194 0.1943 12.65 0.000 Return on net worth_1 1 0.089 0.0888 5.78 0.017 PROFIT/GDP 1 0.008 0.0083 0.54 0.463 Error 315 4.838 0.0154 Total 328 165.473 2368 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate Among the individual predictors, several stand out as significant contributors to the response variable. PAT as % of total income (F = 180.41, P < 0.001) and interest income as a percentage (F = 192.91, P < 0.001) have the highest contributions, indicating their substantial influence. Other significant predictors include employees' utilization ratio (F = 24.93, P < 0.001), quick ratio (F = 12.65, P < 0.001), PAT as % of capital employed (F = 8.94, P = 0.003), and return on net worth (F = 5.78, P = 0.017) as shown in Table 6. In contrast, variables like capital adequacy ratio (P = 0.139), net non-performing assets (P = 0.398), profit/loss per employee (P = 0.391), and PROFIT/GDP (P = 0.463) are non-significant, suggesting they do not significantly affect the response variable within this model as shown in Table 6. The error sum of squares (4.838) and mean square error (0.0154) highlight the relatively small unexplained variability, emphasizing the model's effectiveness in fitting the data. This analysis confirms the importance of specific financial metrics in predicting the response variable as shown in Table 6. The percentage contribution of each factors is measured by the (Adj SS)/(Total SS) *100 [34]. Table 7. Percentage contribution. Factor Percentage contribution Capital adequacy ratio (in per 0.499706 Net non-performing assets (NNPA 0.16167 Profit/Loss per employee 0.16167 PBT as % of total income 0.999412 PAT as % of total income 40.72604 Net profit margin 1.234568 Employees utilisation ratio(tim 5.629042 Interest income as a percentage 43.54791 PAT as % of capital employed 2.013521 Debt to equity ratio (times) 0.749559 Quick ratio (times) 2.851264 Return on net worth_1 1.308054 PROFIT/GDP 0.117578 Figure 4. Percentage Contribution. 0% 0% 0%1% 41% 1%6% 44% 2% 1% 3% 1% 0%   Capital adequacy ratio (in per   Net non-performing assets (NNPA   Profit/Loss per employee   PBT as % of total income   PAT as % of total income   Net profit margin   Employees utilisation ratio(tim   Interest income as a percentage   PAT as % of capital employed   Debt to equity ratio (times)   Quick ratio (times)   Return on net worth_1   PROFIT/GDP 2369 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate The percentage contributions of various factors indicate their relative importance in explaining the variability in the response variable. The most influential factors are interest income as a percentage (43.55%) and PAT as % of total income (40.73%), highlighting their dominant roles in determining the rate of return. These two factors alone account for over 84% of the contribution, emphasizing their critical impact. Other significant contributors include employees’ utilization ratio (5.63%), quick ratio (2.85%), and PAT as % of capital employed (2.01%), suggesting their moderate influence on the response variable. In contrast, factors such as capital adequacy ratio (0.50%), net non-performing assets (0.16%), profit/loss per employee (0.16%), and PROFIT/GDP (0.12%) contribute minimally, indicating limited impact. Factors like PBT as % of total income (1.00%), net profit margin (1.23%), and return on net worth (1.31%) show a modest effect, while debt-to-equity ratio (0.75%) also plays a minor role. Overall, the analysis highlights the disproportionate contribution of specific financial metrics, with interest income and PAT as % of total income being the most significant, while others contribute marginally. 5. Conclusion and Recommendations In recent years, central banks have made significant improvements to their supervision techniques and quality in response to changes in the banking sector. It is crucial to effectively supervise and manage risks as they can both present opportunities and pose threats to the profitability of banks. The findings of this study indicate that private banks in India have demonstrated superior performance and are well-positioned in comparison to other banks. However, further research is recommended to develop a model that can accurately predict bank performance based on ratio analysis.. We are also recommended for further study including textual index in this Models and make more advance version of prediction of bank failure. CAMELS Ratio Constructing a model using past data and applying linear regression analysis can provide early warning signals for potential issues. Additionally, it is essential to examine the correlation among various ratios to gain new insights into the current context. While previous studies exist, conducting further analysis can offer valuable insights and contribute to a deeper understanding of the subject matter. The CAMELS analysis reveals that India's banking system is performing moderately well, with ratings ranging from 2.5 to 4, indicating room for improvement. Banks like HDFC Bank Ltd., Bandhan Bank Ltd., ICICI Bank Ltd., Axis Bank Ltd., and City Union Bank Ltd. have achieved the best ratings of 2.4 to 2.6, reflecting strong financial stability. However, banks such as Karur Vysya Bank Ltd., Karnataka Bank Ltd., and Yes Bank Ltd., with ratings above 3.7, need to take corrective measures to enhance their performance. Overall, the study highlights key strengths and areas for improvement, offering valuable insights for regulatory bodies and stakeholders. The analysis reveals that interest income as a percentage (43.55%) and PAT as a percentage of total income (40.73%) are the most influential factors, collectively accounting for over 84% of the contribution in determining the rate of return. This underscores their critical role in bank performance. Moderate contributors include employees’ utilization ratio (5.63%) and quick ratio (2.85%), while factors like capital adequacy ratio (0.50%) and net non-performing assets (0.16%) have minimal impact. The findings highlight the disproportionate influence of specific financial metrics, emphasizing the need for banks to focus on key areas to optimize performance and stakeholder value. To enhance performance, banks should focus on key revenue drivers like increasing interest income and PAT as a percentage of total income, which significantly impact overall performance. Strengthening employees’ utilization, improving quick ratios for better liquidity management, and maintaining robust capital adequacy are critical. Efforts should also target reducing non-performing assets through effective credit risk management and improving profitability metrics like net profit margin and return on net worth. Regular performance monitoring using the CAMELS framework, leveraging technology for operational efficiency, adhering to regulatory guidelines, and maintaining transparent stakeholder 2370 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 5: 2355-2371, 2025 DOI: 10.55214/25768484.v9i5.7475 © 2025 by the authors; licensee Learning Gate communication are essential. These measures collectively optimize performance and ensure long-term stability. 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