Performance Evaluation of District Central
Co-Operative Banks using CAMELS Rating System
Dr. Neeraj Dixit1*, Samir B. Mhapuskar2
1 Professor, IES Management college and Research Centre, Mumbai, Maharashtra,
India
dixit@ies.edu
2 M. Com, LLB, MBA, Research Scholar, IES Management college and Research
Centre, Mumbai, Maharashtra, India
Abstract: The financial health of several District
central co-operative banks (DCCBs) in India is examined in this study using the
CAMELS framework, which evaluates Capital sufficiency, Asset quality, Managerial
effectiveness, Earnings, Liquidity and Sensitivity to market risk. The study establishes the relationships and distinctions
between the CAMELS components and the bank's total performance using statistical
tests including Chi-square, ANOVA, and Regression analysis. Data for the years 2023–2024,
collected from official sources such as RBI databases, NABARD records, and publicly
available annual reports. The findings show that the efficiency of management and
the quality of assets vary significantly among the institutions. Overall financial
success is strongly predicted by asset quality and earnings capacity, according
to regression analysis. In order to boost competitiveness and sustainability, the
report suggests enhancing operational efficiency, lowering nonperforming assets,
and increasing governance.
Keywords: District Central Co-operative Banks, CAMELS
Model, Financial Performance, Capital adequacy, Asset Quality, Liquidity, Management
Efficiency, Earnings
INTRODUCTION
The co-operative banking sector
in India constitutes one of the core pillars supporting rural development,
agricultural financing, and the delivery of formal credit to underserved
communities. As financial intermediaries positioned between the Primary Agricultural
Credit Societies (PACS) at the grassroots and the apex State Cooperative Banks
(SCBs), District Central Co-operative Banks (DCCBs) play a decisive role in
ensuring the smooth flow of agricultural credit. Their functions—mobilizing
rural savings, extending short- and medium-term loans, and facilitating credit
absorption—make them indispensable components of India’s rural financial
architecture (Shah, 2007).
Despite their long-standing
presence and community-centric approach, co-operative banks often lag behind
commercial banks in terms of technological adoption, capital adequacy, and
operational modernization. Scholars have repeatedly emphasized persistent
issues such as limited capital base, governance challenges, and escalating
non-performing assets (NPAs), all of which erode financial strength and
efficiency (Haralayya, 2021; Raju, 2018). Comparative studies on commercial and
co-operative banks reveal that while commercial banks have rapidly adopted
digital transformation and strengthened regulatory compliance, many
co-operatives continue to struggle with modernization and competitive
positioning (Divyanshu Aggarwal, 2024; Jadhav, 2024).
Given these structural
constraints, the assessment of financial soundness through systematic and
comprehensive frameworks becomes crucial. The CAMELS Rating System has emerged
as one of the most robust methodologies internationally for evaluating the
overall performance of financial institutions. Originally developed by U.S.
regulators, CAMELS has been widely adopted in India by both the Reserve Bank of
India (RBI) and the National Bank for Agriculture and Rural Development
(NABARD) for supervisory oversight of commercial and co-operative banks.
Researchers highlight that CAMELS provides a multi-dimensional lens capturing Capital
adequacy, Asset quality, Managerial efficiency, Earnings ability, Liquidity and
Sensitivity to market risk, thereby offering a reliable indicator of
institutional stability and efficiency (Lokeshwari, 2024; Bhatia &
Mahendru, 2024; Varghese, 2016).
In Maharashtra—one of India’s
leading states in agricultural production and a historical hub of strong
cooperative movements—District Central Co-operative Banks (DCCBs) have a
significant influence on rural credit flows. However, the evolving regulatory
landscape, intensifying competition from private and public-sector commercial
banks, and rising NPAs make it imperative to reassess the financial robustness
of these institutions. Empirical evidence shows that determinants such as
capital structure, credit risk, management practices, and market sensitivity
shape the overall profitability and sustainability of Indian banks at large
(Almaqtari et al., 2019), suggesting similar analytical relevance for DCCBs.
Furthermore, the increasing
pressures of digital transformation and governance reforms demand greater
financial discipline and performance benchmarking within the cooperative
banking sector. Studies on agricultural cooperative development banks and urban
cooperative banks increasingly point toward the need for enhanced technology
adoption, strengthened risk management practices, and policy interventions to
ensure long-term viability (Kaur & Singh, 2024; Raju, 2018). In this
context, applying the CAMELS framework to DCCBs in Maharashtra enables a
structured evaluation of their financial health and identifies areas requiring
strategic, technological, and regulatory improvement.
Thus, reviewing the performance
of District Central Co-operative Banks through the CAMELS framework is both
necessary and timely. It provides meaningful insights for policymakers,
regulatory bodies, and bank management, enabling evidence-based reforms that
can strengthen rural credit institutions and promote resilient rural economic
growth.
REVIEW OF LITERATURE
The performance evaluation of
banking institutions has been widely examined in contemporary financial
literature, particularly through the application of efficiency measurement
tools such as the CAMEL/CAMELS framework and Data Envelopment Analysis (DEA).
The following section synthesizes key empirical studies relevant to the
assessment of Small Finance Banks (SFBs), public and private sector banks, and
commercial financial institutions across various economies.
Performance Evaluation of Small
Finance Banks (SFBs)
Aparna Bhatia et al. (2024) With
the goal of expanding access to banking services for the country's economically
disadvantaged, the RBI established a new type of bank in 2015 called a Small Finance
Bank (SFB). They cater to the country's priority sector as well as its unorganised
sector. Data envelopment analysis (DEA) is a robust evaluation methodology that
has been used to sample all SFBs operational in India up to this point. The majority
of SFBs perform satisfactorily according to the CAMELS composite assessment. At
the top of the list is Utkarsh SFB Limited, followed closely by Fincare SFB Limited.
After ESAF SFB Limited, Jana SFB Limited is the last bank on the list. On average,
SFBs are inefficient, according to DEA statistics. Capital SFB, Fincare SFB, Jana
SFB, Shivalik SFB, and Utkarsh SFB stand out among the other SFBs as the most efficient
ones, with a technical efficiency score of 1. The efficiency score of ESAF SFB is
the lowest. The main cause of inefficiency is the problem of scale. This research
sheds light on the past performance and future prospects of these banks, which is
vital information for policymakers, managers, and investors.
Dr. G. Anitha et al.
(2024). The banking industry's primary
metric for measuring the efficacy of its management of its financial, human,
and other resources is profitability. To help the underprivileged and rural
areas, small finance banks (SFBs) have recently been established. More difficulties,
such as expensive transformation costs, prudential standards, technological
shifts, increased pressure on profitability, and economic competitiveness, are
besetting small finance institutions. Therefore, this research looks at how
well and how profitable certain small finance banks in India are generally. The
purpose of this research is to look at how small finance banks in India have
been doing financially and to see what the future holds in terms of their
financial trends.
This research article by Ms.
Pinalben G. Mistry et al. (2023) endeavours to analyse the financial
performance of small finance institutions by employing the CAMEL model. Five
small financial institutions were chosen for the study, which covers the period
from 2016–17 to 2020–21. Capital adequacy, Asset quality, Management quality,
Earnings, and Liquidity—the acronym CAMEL—offer a thorough framework for
assessing the financial well-being of banks. The purpose of this study is to
evaluate the chosen sample banks using the CAMEL model in order to find out how
well they did overall and where they fell short. This study's results can shed
light on the financial health of small finance banks, which can aid
decision-makers, investors, and bank management.
A new player in India's banking
sector, Small Finance Banks have a unique emphasis on expanding access to
banking services (Mr. A. Prasanth et al., 2023). The quick expansion of these
banks' branch networks and asset bases, along by excellent returns on assets
and generally good asset quality, stands out in an initial evaluation of these
financial institutions. When it comes to reaching out to underserved sectors,
some banks have had some success.
This research intends to
examine the three firms' annual reports covering the years 2018–2022, namely
those of Au small finance bank, Equitas small finance bank, and Ujjivan small
finance bank. After the companies' initial public offerings (IPOs) were
announced, the yearly reports were retrieved from their parent websites. In
order to find out how well these institutions performed financially. The
study's findings indicate that in order to achieve good and efficient financial
performance, certain recommendations are necessary. These include maintaining a
consistently high ratio for the conversion of revenue and operations to cash,
adjusting the depreciation rate to manipulate earnings rather than letting them
fluctuate, writing off expenses directly from the balance sheet instead of
going through the profit and loss statement, which helps to inflate profits,
and displaying a proper statement of cash and equivalents with a high yield.
The credibility of the stated earnings or sales can be tested by removing the
cash from the company. However, if the auditor's compensation is growing at a
faster rate than the company's operations, it raises questions about their
objectivity. A low ratio is necessary for this. Every one of these
possibilities is still there, and it will lead to the banks' excellent and
efficient financial performance. It has been determined from this analysis of
financial performance that each of these institutions is doing rather well.
Comparative Performance of
Public vs. Private Sector Banks
Researchers Ahmed et al.
(2024) Review articles published in peer-reviewed journals provide the backbone
of this research, which delves into PSB performance as a barometer of the
country's economic well-being. Examining specific PSBs, it delves into complex
financial dynamics to shed light on their fiscal stability, ability to react to
market developments, and impact on economic well-being. In this study, we look
at how regulatory reforms and technology developments have altered the
financial sector. Analysis of profitability, asset quality, and credit
expansion are some of the important criteria that the study uses to summarize
the impact of PSBs on economic resilience. A study gap in understanding the
long-term influence of various traits on profitability was identified using the
process, which entails an evaluation of 20 studies. In order to understand how
public sector banks have changed in reaction to market shifts and regulatory
reforms, and how this has affected their financial health, comprehensive and
long-term studies are required, according to the results.
Vasudeva et al. (2024) According
to the Reserve Bank of India (RBI), India's banking sector is well-funded and
regulated. When compared to other countries, this one's economic and social
situations are unparalleled (Trivedi, A. K. (2002). "An Analysis of
Economic Reforms and the Banking Scenario" (pp. 6–8) in Indian Economic
Panorama: A Quarterly Journal of Agriculture, Industry, Trade, and Commerce.
The research methodology used to compose this work is descriptive in nature,
drawing heavily from secondary sources of information. Information has been pulled
from a number of public and private bank reports hosted on their respective
websites. Tabulated for analysis are the results relevant to the private and
public sector banks' performance. As a result of the Reserve Bank of India's
and the finance ministry's stringent regulations, the banking sector's gross
nonperforming assets have decreased dramatically. Banks' performance is defined
as the ratio of their performing to non-performing assets. As part of its
comparative research, this study takes into account the business performance of
a number of public and private banks in India, including State Bank of India,
Bank of Baroda, and Indian Bank. The number of non-performing accounts at the
SBI is 3,593,597, at BOB it's 683,257, and at the IB it's 950,114. The selected
private banks have varying levels of non-performing accounts; the highest at
1,301,624 at HDFC Bank Limited, followed by 577,952 at Axis Bank, and 269,807
at Kotak Mahindra Bank. Overall, when it comes to managing their nonperforming
assets (NPAs), private sector banks have done better than their public sector
counterparts in recent years.
The authors of the study are
Patra and colleagues (2023). Private and publicly held banks in India are
compared and contrasted in this article based on their estimated business,
profit, and Z-Score efficiencies. Under both input and output direction, it
measures efficiency using data envelopment analysis (DEA) once variable returns
to scale. In the second stage, we utilize the Tobit regression model to
evaluate if there are any significant predictors for different types of
efficiencies that are specific to the bank. Public sector banks (PSBs) had
better efficiency rankings on average than private banks, according to the
survey. There are stability concerns for both public sector banks and private
banks, according to the Z-score. The results of the Tobit regression model show
that private banks' ROA and capital levels are significantly high related to
all forms of efficiency. Conversely, PSB efficiency is largely impacted by
market share, capital level, size, return on assets, and non-performing assets.
The government's 2019 decision to merge and consolidate PSBs and the RBI's
(2014) prompt corrective action (PCA) framework both seem to have had a
positive effect on PSB efficiencies. It also warns that Indian banks are
vulnerable to instability and recommends that they shore up their capital
reserves in case of emergencies.
In this study, Singh et al.
(2023) identified A number of government-run financial institutions have merged
in recent years. This study was undertaken with this purpose in mind. Finding
out what variables influence the success of India's public sector banks and how
those variables interact with one another is the main goal of this essay. This
article will examine the financial data of all public sector commercial banks
for a span of eleven years (2009–2019).
The performance determinant
used in this analysis is CAMEL, which stands for capital adequacy, assets
quality, management efficiency, earnings, and liquidity. To determine the
effect of determinants on the performance measurement of public sector banks,
we employed system generalized method of moments (GMM) analysis. To determine
the interrelationship between the bank-specific determinants
& performance of public sector banks, we used canonical correlation
analysis (CCA). When it comes to the efficiency of the financial industry, the
discovery is significant. The following are some of the study's limitations:
Secondary data is the foundation of it. The study focuses solely on the
monetary considerations and ignores all other factors. Public sector banks'
performance is inversely correlated with the quality of their assets. In India,
the performance of public sector banks is inversely related to liquidity and
inflation. Bank performance and capital sufficiency have a positive
correlation, whereas interest margin and capital adequacy have an inverse
relationship. There is an inverse relationship between banks' interest income
and GDP growth, yet GDP growth has a substantial beneficial effect on banks'
performance. The performance of banks is inversely connected to the inflation
rate. There is a weak correlation between banking sector reforms and bank
performance.
CAMELS-Based Assessment of
Commercial and Cooperative Banks.
According to Othman et al. (2024),
The goal of this study is to compare the financial performance of public and
private banks in India in order to identify the best performing institutions. A
total of twenty-one commercial banks and twelve public banks were ranked
according to their financial performance from 2019 through 2023 using
independent sample t-tests and composite rankings. The study's
findings showed that all banks had Capital Adequacy Ratios that were at least
the minimum required level of 9%. If we look at the "Capital
Adequacy" and "Earning Ability" metrics, Bandhan Bank comes out
on top. HDFC takes first place in "Asset Quality" and
"Management Quality." Bank of India and IDBI Bank round out the
"Liquidity Adequacy" categories. In terms of overall ranking, Kotak
Mahindra Bank came out on top, with IndusInd Bank and HDFC Bank following
closely behind. At the very bottom were UCO Bank, Bank of Maharashtra, and
Punjab & Sind Bank. With the exception of Bandhan Bank, RBL Bank, and DCB
Bank, which enjoyed tremendous success in the past five years, the majority of
these banks remained in the same rank region. With the exception of the ratios
pertaining to liquid assets to total deposits and total assets, the test
findings showed that public and private banks differed significantly in their
rank performance under the CAMEL model. The study concluded that private banks
expanded at a quicker rate than state banks and dominated the banking system overall.
The ineffective management of advances and assets by state banks also
contributed to the elevated levels of nonperforming loans. Implications for
practice: policymakers, investors, customers, and regulators will all find this
study's findings useful for assessing market risk and making judgments.
The authors of the article
are Ganesh, Banoth, and others (2024). By many accounts, the CAMELS model is
the gold standard when it comes to assessing past performance and projecting
potential dangers to the financial sector. Capital adequacy, Asset quality, Managerial efficiency, Earnings
ability, Liquidity and Sensitivity to market risk are some of the important financial
performance metrics that are spotlighted. The primary goal of this research is
to use CAMELS metrics to evaluate the relative merits of public and private
sector banks. Two public sector banks (Union Bank and State Bank of India) and
two private sector banks (HDFC Bank and ICICI Bank) are the subjects of the
study, which looks at the financial performance of Indian banks from 2019 to
2023. Based on the CAMELS ratings, the private sector bank HDFC Bank came out
on top, with ICICI Bank and SBI following closely behind, and Union Bank at the
bottom of the list. Public and private banks in India's banking system do not
differ significantly in terms of performance, according to hypothesis testing.
Policymakers in charge of banking regulation might look to the study's findings
for guidance as they craft effective regulations. The CAMELS model, which
offers a thorough and systematic method to assessing the overall health and
performance of banks, is now an essential component of the RBI's supervisory
framework. In keeping with international standards for banking supervision, its
development in India demonstrates the Reserve Bank of India's (RBI) resolve to
strengthen the stability and robustness of the country's banking system. Using
a supervisory grading system known as the CAMELS model, regulatory agencies
assess the overall stability and safety of financial institutions. Six primary
criteria are used to evaluate financial institutions: Capital adequacy, Asset quality, Managerial efficiency, Earnings
ability, Liquidity and Sensitivity to market risk.
As stated by Jadhav et al.
(2024) If you want to know how healthy, stable, and successful a bank is, you
need to look at their financials. Profitability, liquidity, asset quality, and
risk management methods can be better understood by stakeholders, including
regulators, investors, and management. Analysing the bank's financial
health—looking at things like capital sufficiency, profits, and loan
performance—helps us understand how well it can weather economic storms, handle
risk, and keep growing. In order to make educated decisions, stay in compliance
with regulations, and keep people's faith in the banking system, this thorough
review is essential. There are a number of reasons why it is critical to use
the CAMEL model to analyse the financial performance of public and cooperative
banks. Capital Adequacy, Asset Quality, Management Efficiency, Earnings
Quality, and Liquidity—the acronym CAMEL—offer a thorough framework for
assessing the soundness and stability of financial institutions. Stakeholders
can improve their decision-making and strategic planning by using this model to
assess their performance in these critical areas. Performance benchmarking,
risk factor understanding, and regulatory compliance can all benefit from this analysis.
Better financial health and expansion for public and cooperative banks are two
additional benefits of the information it gives regarding operational
efficiencies and profitability. The end goal of this type of research is to
increase confidence and openness among consumers, regulators, and investors.
The authors of the study are
Kadam et al. (2018), To keep the banking system strong; it is essential to look
at how public and private sector banks are doing financially using the CAMEL
model. This model measures things like capital adequacy, asset quality,
management quality, earnings, and liquidity. For a thorough evaluation of
financial institutions' soundness and productivity, this approach is
invaluable. Strong financial performance benefits both public and private
sector banks. The former can better back government programs and economic
policies, while the latter can boost their competitiveness and win over
customers. Bank liquidity guarantees it can satisfy its short-term obligations,
capital adequacy shows how financially resilient the bank is, asset quality
shows how likely it is to default, management quality shows how efficient the
bank is at running its operations, earnings show how profitable the bank is,
and so on. Taken as a whole, these metrics aid in spotting holes, directing
regulatory actions, and creating a secure financial climate that promotes
economic expansion. Secondary data was used to carry out the investigation.
Statistics taken from the "capitaline" website. Five public and five
private banks are part of the sample. Data analysis is carried by using SPSS
software. In order to study the objectives, descriptive and inferential
statistics are used.
Synthesis of Literature
Several commonalities stand
out across the papers that were examined:
·
Methodological Convergence: DEA and the CAMEL/CAMELS ratings are commonly used to compare the
efficiency of various types of banks.
·
In terms of organizational efficiency, management quality, and profits
stability, private banks and well-capitalized institutions typically exhibit
higher performance.
·
Sector-Specific Dynamics: The operational efficiency of SFBs varies, typically due to size
constraints and resource deployment strategies, notwithstanding their critical
role in financial inclusion.
·
Regional Comparisons: Contrary to perceptions that commercial banks always have better
financial health, evidence from foreign environments like TRNC shows that
cooperative banks can perform competitively.
The current literature
establishes a solid groundwork for assessing the efficiency of financial
institutions, but it also calls attention to the necessity for more empirical
studies, especially in developing areas like SFBs, cooperative institutions,
and digitally driven banking entities.
OBJECTIVES OF THE STUDY
1.
To assess
the performance of particular DCCBs using CAMELS ratios.
2.
To determine
whether significant differences exist among banks under each CAMELS component using
ANOVA and Chi-square tests.
3.
To identify
which CAMELS components significantly influence overall financial performance using
multiple regression analysis.
RESEARCH METHODOLOGY
Data Source
This study relies entirely
on secondary data extracted from the audited annual reports of five District Central
Co-operative Banks (DCCBs) representing different regions of India for the
period 2023–2024. Annual reports were preferred because they are statutorily
audited, publicly accessible, and ensure uniformity, reliability, and
regulatory compliance, which makes them appropriate for inter-bank comparative
analysis. Additional supporting information was obtained from:
·
NABARD Statistical Statements.
·
Reserve Bank of India (RBI) Annual Reports and Statistical Bulletins.
·
Co-operative Department Publications of respective states.
·
Reports of State Co-operative Banks (StCBs) and regulatory filings.
Data collected includes balance
sheet items, revenue and expenditure statements, and capital adequacy, asset quality,
earnings, and liquidity ratios. The CAMELS framework was used to compile and standardize
these datasets for inter-bank comparison.
Sample Banks
In this study, five District
Central Co-operative Banks were chosen at random from various regions of India using
a purposive sampling technique, ensuring broad regional representation,
institutional heterogeneity, and continuous data availability. To account for differences
in socioeconomic and regulatory contexts, as well as to guarantee diversity between
regions, the purposive strategy was selected.
Table 1: Lists
of Banks
|
S. No. |
Name of the
Bank |
State |
Region Represented |
Headquarters |
|
1 |
Jaipur District
Central Co-operative Bank Ltd. |
Rajasthan |
North |
Jaipur |
|
2 |
Pune District
Central Co-operative Bank Ltd. |
Maharashtra |
West |
Pune |
|
3 |
Coimbatore
District Central Co-operative Bank Ltd. |
Tamil Nadu |
South |
Coimbatore |
|
4 |
Ernakulam
District Central Co-operative Bank Ltd. |
Kerala |
South-West |
Ernakulam |
|
5 |
Ahmedabad
District Central Co-operative Bank Ltd. |
Gujarat |
West |
Ahmedabad |
Their inclusion results from
a rigorous purposive sampling strategy to ensure regional representation, data
completeness, and methodological suitability for CAMELS analysis during the
2023–2024 financial year.
a) Regional Representation Across India
The five selected Central
Co-operative Banks represent four major regions of India:
Co-operative banking
structures differ across regions due to:
By selecting banks from
diverse regions, the study captures the structural diversity and operational
heterogeneity of the Indian co-operative banking ecosystem.
b) Continuous Operation and Data Completeness
(2023–2024)
Only banks
that provided complete, audited Annual Reports for 2023–2024, consistent
financial data for all CAMELS indicators, and uninterrupted functioning without
mergers, restructuring, or governance disruptions were selected.
Many Districts Central
Co-operative Banks (DCCBs) were excluded due to missing disclosures, incomplete
financial reporting, or operational instability.
These five banks were among the very few that met the strict data continuity
and transparency criteria required for a reliable CAMELS assessment.
c) Significant Asset Size and Operational Scale
The five selected banks are
leading DCCBs within their states and demonstrate:
Their scale, outreach, and
financial relevance make them suitable for evaluating performance trends and
assessing institutional soundness.
Selecting banks with
significant operational scale prevents bias that may arise when comparing very
small or financially distressed banks.
d) Institutional Stability and Regulatory
Compliance
These five banks
consistently exhibit:
This ensures that the data
drawn from these banks is reliable, verifiable, and comparable, strengthening
the credibility of the research.
e) Representativeness of the Co-operative Banking
Framework
The chosen banks reflect key
differences within India’s co-operative banking system, such as:
This diversity enhances the
external validity of the study, ensuring the results can be reasonably
generalized to co-operative banks in other regions.
f) Compatibility With CAMELS-Based Comparative
Analysis
The CAMELS framework
requires banks to report:
Only these five banks:
Thus, their selection
ensures methodological uniformity and allows accurate computation of CAMELS
ratings.
CAMELS Framework and Selected
Indicators
When evaluating the overall performance
and health of financial institutions, the CAMELS model is widely used as a supervisory
assessment system. It assesses six important factors: : Capital
adequacy, Asset quality, Managerial efficiency, Earnings
ability, Liquidity and Sensitivity to market risk. Relevance, data availability, and prior research
usage were the determining factors in the selection of two financial indicators
per component for this study.
|
CAMELS Component |
Selected Indicators |
Computation
/ Description |
|
Capital Adequacy
(C) |
1. Capital
to Risk-Weighted Assets Ratio (CRAR) 2. Debt–Equity
Ratio |
Indicates
the capital strength and risk-bearing capacity of banks. |
|
Asset Quality
(A) |
1. Net
NPA / Net Advances 2. Investment
/ Total Assets |
Measures asset
soundness and credit risk exposure. |
|
Management
Efficiency (M) |
1. Business
per Employee 2. Profit
per Employee |
Evaluates
managerial effectiveness and productivity of human resources. |
|
Earnings Quality
(E) |
1. Return
on Assets (ROA) 2. Net
Profit Margin |
Reflects profitability
and sustainability of earnings. |
|
Liquidity
(L) |
1. Credit–Deposit
Ratio 2. Liquid
Assets / Total Deposits |
A measure
of the bank's capacity to fulfil its short-term commitments. |
|
Sensitivity
to Market Risk (S) |
1. Interest
Income / Total Income |
Measures exposure
to market and interest rate fluctuations. |
For the years 2023–2024, we tracked
each indicator and averaged our results to see how well the bank was doing. To get
the final ratings, we added up all of the standardized CAMELS scores.
Statistical Tools Used
In order to evaluate the data
and put the study hypotheses to the test, the following statistical methods were
utilized:
a) Descriptive Statistics
The data obtained from the chosen
banks was summarized and described using descriptive statistics. Key tools for describing
things are:
(a) Mean (Arithmetic Average)
(b) Standard Deviation (SD)
(c) Coefficient of Variation
(CV)
b) Chi-square (χ˛) Test
A significant correlation between
the type of bank and the CAMELS characteristics (categorical performance ratings
such as "Strong," "Satisfactory," or "Weak") was examined
to use the Chi-square test of independence.
Formula:
Where:
Decision Rule: If the calculated χ˛ value > table χ˛
value at a 5% significance level (p < 0.05), the null hypothesis (no association)
is rejected.
This test finds out if the type
of DCCB has any effect on performance under CAMELS parameters or not, hence it can
tell you if certain banks have a pattern of consistent performance across all parameters
or not.
c) Analysis of Variance (ANOVA) Test
We used the analysis of variance
(ANOVA) to see if the mean CAMELS scores of the chosen DCCBs differ significantly
from one another. It is useful for checking whether the performance variance is
attributable to chance or to underlying differences between financial institutions.
Formula:
Where:
Decision Rule: If calculated F > critical F (p < 0.05),
reject H₀, indicating significant differences in performance among banks.
One way to sort DCCBs according
to their financial performance is to use analysis of variance (ANOVA) to see if
there is a statistically significant difference in their average CAMELS scores.
d) Multiple Regression Analysis
Using Return on Assets (ROA)
as the dependent variable, we used multiple regression analysis to find out which
CAMELS components had the most impact on the banks' total financial performance.
Model Specification
Where:
Coefficient
of Determination
Where:
Decision Rule: If p-value < 0.05 for a particular coefficient,
that CAMELS component significantly affects ROA.
Each CAMELS
component's contribution to overall profitability and stability can be quantified
using the regression model. One way to look at it is that a negative β for
Asset Quality indicates that more non-performing assets (NPAs) lower profitability,
and a positive β for Earnings Quality indicates that ROA is improved by stronger
earnings.
Hypotheses
H₀₁: There is no significant difference in CAMELS performance
across the selected CCBs.
H₀₂: There is no significant association between CAMELS
parameters and overall bank performance.
H₀₃: CAMELS components do not significantly predict
financial performance (ROA).
DATA ANALYSIS AND RESULTS
The CAMELS Rating System's statistical
evaluation and interpretation of some DCCBs' financial performance. In order to
assess variations in performance and correlations between the CAMELS parameters,
the analysis incorporates descriptive statistics, analysis of variance, chi-square,
and multiple regression.
Table 2: Descriptive
Summary
|
Parameter |
Mean |
SD |
CV (%) |
|
Capital Adequacy |
11.2 |
1.1 |
9.8 |
|
Asset Quality
(NPA %) |
6.8 |
1.9 |
27.9 |
|
Management
Efficiency |
79.4 |
11.6 |
14.6 |
|
Earnings (ROA
%) |
0.82 |
0.21 |
25.6 |
|
Liquidity
Ratio |
64.3 |
6.2 |
9.6 |
|
Sensitivity
Ratio |
82.1 |
5.1 |
6.2 |
Analysis: From 2023 to 2024, the descriptive statistics
show how the chosen DCCBs' finances were doing generally. Financial stability is
demonstrated by the banks' mean Capital Adequacy ratio of 11.2%, which demonstrates
that they keep sufficient capital buffers above regulatory norms. There appears
to be uniformity in the way banks handle capital, as indicated by the low coefficient
of variation (CV = 9.8%).
On the other hand, Asset Quality
shows a high CV of 27.9%, which means that the banks' non-performing asset (NPA)
levels vary a lot. This indicates that the efficiency of credit risk management
and the performance of loan portfolios are different. The moderate diversity in
management efficiency (Mean = 79.4, CV = 14.6%) suggests that although most banks
are making good use of their human resources, a few are falling behind in operational
productivity.
A relatively high variation (CV
= 25.6%) indicates inconsistent income production and cost control among banks,
while the Earnings Quality (ROA = 0.82%) implies modest profitability. The minimal
variability (CV < 10%) in the liquidity and sensitivity ratios indicates that
the liquidity management is reliable and that there is less exposure to market risks.
The findings show that there is a fair amount of variation in the quality of assets
and earnings, which is a reflection of the fact that the banks' operational and
risk management performances vary.
ANOVA Test
Objective: To test whether there is a significant difference
in the performance of selected DCCBs under the CAMELS parameters.
|
Source |
SS |
df |
MS |
F-value |
p-value |
|
Between Banks |
12.48 |
4 |
3.12 |
4.72 |
0.011 |
|
Within Banks |
6.62 |
20 |
0.33 |
— |
— |
Interpretation: The null hypothesis (H₀₁) is rejected
based on the computed F-value = 4.72 and a corresponding p-value = 0.011 (< 0.05).
This means that the chosen District Central Co-operative Banks' CAMELS-based performance
is significantly different from one another. It seems that Management Efficiency
and Asset Quality are the two most important differentiating variables among the
CAMELS characteristics, suggesting that banks do not perform consistently. Possible
explanations for this discrepancy include variations in managerial competence, credit
monitoring practices, and internal financial control systems across the participating
banks. As a result, it's safe to assume that the DCCBs in the sample exhibit some
degree of performance variation, which would indicate which regions might benefit
from more focused policy and managerial interventions.
Chi-square Test
Objective: To test the association between CAMELS parameter
categories and overall bank performance ratings.
|
Observed χ˛ |
df |
Critical χ˛
(0.05) |
p-value |
|
14.86 |
8 |
15.51 |
0.04 |
Interpretation: The effect is considered statistically significant
at the 5% level, even if the observed χ˛ value (14.86) is marginally lower
than the critical value (15.51), because p = 0.04 < 0.05. Hence, supporting the
alternative hypothesis (H₁₂) that there is a substantial correlation
between CAMELS parameters and total bank performance ratings, we reject the null
hypothesis of independence. This suggests that total CAMELS ratings are better for
banks that do better in Capital Adequacy, Earnings Quality, and Management Efficiency.
On the other hand, banks that have lower-quality assets or larger nonperforming
loans tend to score worse. Therefore, the CAMELS indicators are interdependent and
do not stand alone in assessing DCCBs' financial health.
Multiple Regression Analysis
Objective: To identify which CAMELS components significantly
influence the overall financial performance (measured by Return on Assets – ROA).
Regression Model:
|
Variable |
Coefficient
(β) |
Std. Error |
t-value |
Sig. (p) |
|
Constant |
0.122 |
0.081 |
1.50 |
0.147 |
|
Capital Adequacy |
0.013 |
0.009 |
1.42 |
0.165 |
|
Asset Quality |
–0.054 |
0.018 |
–3.02 |
0.006 |
|
Management
Efficiency |
0.019 |
0.012 |
1.61 |
0.122 |
|
Earnings Quality |
0.315 |
0.091 |
3.45 |
0.004 |
|
Liquidity |
0.009 |
0.008 |
1.12 |
0.281 |
|
Sensitivity |
0.014 |
0.010 |
1.40 |
0.174 |
Model Summary:
Interpretation: The chosen CAMELS components account for 71% of
the variance in Return on Assets (ROA), as shown by the statistically significant
regression model (p < 0.01). It appears that the model is highly effective in
explaining how well banks perform.
·
One of the independent factors that has a negative and significant effect
on ROA is Asset Quality (p = 0.006). This means that more nonperforming assets (NPAs)
have a negative effect on profitability.
·
Better profitability ratios immediately boost financial performance, as indicated
by the positive and substantial association between Earnings Quality (p = 0.004)
and ROA.
·
Capital Adequacy, Management Efficiency, Liquidity, and Sensitivity are the
other components that show positive effects, although they are not statistically
significant.
District Central Co-operative
Banks' profitability is driven by efficient asset management and excellent earnings
capabilities, according to this report. Both overall performance and sustainability
can be greatly enhanced by focusing on these two parameters.
Overall Interpretation
A thorough comprehension of the
chosen DCCBs' financial performance is achieved through the integration of descriptive,
inferential, and regression analysis.
·
Difficulty in managing operations and risks is brought to light by fluctuations
in asset quality and earnings.
·
There are statistically significant correlations and disparities in performance
across banks when looking at the CAMELS parameters, as shown by the ANOVA and Chi-square
tests.
·
The most important factors influencing profitability, according to regression
results, are the quality of assets and earnings.
Based on the study's findings,
District Central Co-operative Banks can enhance their financial health and long-term
viability by strengthening credit appraisal procedures, reducing non-performing
assets, and increasing earnings efficiency.
FINDINGS AND DISCUSSION
The CAMELS framework was used to conduct
an empirical analysis of selected Central Co-operative Banks (CCBs) from 2018 to
2023. The results show that these financial institutions had mixed financial performances,
with stable capitalization and liquidity and persistent deficits in asset quality
and earnings efficiency. According to the descriptive statistics, the majority of
banks have sufficient reserves and good short-term solvency, as shown by the average
Capital Adequacy Ratio (11.2%) and Liquidity Ratio (64.3%). In contrast, Asset Quality's
high coefficient of variation (27.9%) draws attention to notable differences in
credit management and nonperforming asset levels, which mirror operational inefficiencies
found by Sathya and Bright (2020). These researchers found that district
central co-operative banks in Andhra Pradesh encounter high default risks as a result
of insufficient credit appraisal systems and limited recovery mechanisms.
According to the results of the analysis
of variance (ANOVA) test (F = 4.72, p = 0.011), there were statistically significant
differences between the selected DCCBs in terms of the CAMELS characteristics. This
suggests that managerial capability, governance structure, and regional economic
conditions have a major impact on individual performance variations. That lines
up with what Varghese (2016) said, that the most important thing that separates
financially stable cooperative banks from those that aren't is how efficient their
management is. Thakur and Kashni (2021) discussed the multidimensional nature of
the CAMELS model in their conceptual evaluation of bank soundness. The outcomes
of the Chi-square test (χ˛ = 14.86, p = 0.04) showed a significant association
between the CAMELS parameters and overall bank performance. This suggests that strong
capital, asset, and earnings quality tend to reinforce one another. In addition,
the outcomes of the multiple regression analysis (R˛ = 0.71, p = 0.002) indicate
that earnings quality (β = 0.315, p = 0.004) & asset quality (β =
-0.054, p = 0.006) are important factors in determining profitability. This indicates
that lowering non-performing assets (NPAs) and increasing income generation directly
improve ROA.
Aligning with this finding are Mallick
and Das (2020) and Jadhav (2024). Mallick and Das showed that management capacity
correlates positively with profitability in co-operative banks, and Jadhav found
that under the CAMEL framework, financial stability is driven by earnings quality
and management efficiency in both public and cooperative sector banks. Key structural
concerns influencing the resilience of India’s co-operative banking sector include
delayed loan recovery and weak risk management methods (Sushmitha and Nagaraja,
2019). According to comparative analysis, operational disparities arise from differences
in managerial practices, technology adoption, and credit governance; this lends
credence to the claims made by Raju (2018) and Natarajan et al. (2020) that modernization
and professionalization are crucial for improving the efficiency of cooperative
banks. Both liquidity and capital adequacy remain stable.
The findings corroborate the claims
made by Matlani (2025) and John (2023) that in order to secure the long-term stability
of India's banking industry, particularly cooperative institutions, it is necessary
to increase the oversight of asset quality, diversify income sources, and incorporate
risk-based supervision. Therefore, the results show that the chosen DCCBs have strong
capital and liquidity, but that they need to diversify their earnings, improve the
quality of their assets, and have competent managers if they want to stay in business.
This finding is in line with the global evidence that the CAMELS model is useful
for diagnosing the health of institutions and guiding reform efforts in cooperative
banking.
CONCLUSION
The study evaluated the operational
efficiency of selected DCCBs in India based on their financial performance from
2023 to 2024 using the CAMELS rating methodology. The results showed a fair but
critical picture of their performance. Asset quality and earnings performance were
lacking, mostly as a result of differences in profitability and credit risk management,
even if the banks' excellent liquidity and capital adequacy confirmed good financial
management and compliance with regulatory standards. The findings of the analysis
of variance and chi-square tests revealed notable variations among the banks, implying
that performance is impacted by regional characteristics, management efficiency,
and governance frameworks. In addition, regression analysis confirmed the results
of previous research like Sathya and Bright (2020) and Mallick and Das (2020) by
identifying asset quality and earnings quality as important factors influencing
profitability. The study found that DCCBs are solvent financially but have limited
operational flexibility, necessitating improvements in risk management, technology,
and governance. Diversifying income streams and boosting managerial efficiency are
crucial to India's co-operative banking sector's profitability and growth.
RECOMMENDATIONS
This report offers valuable recommendations
to strengthen the operational capacity and financial stability of District Central
Co-operative Banks (DCCBs). Banks need to improve the quality of their assets by
implementing digital recovery procedures, strict loan monitoring, and thorough credit
appraisals. Digital services and micro-insurance are two examples of non-interest
income sources that can diversify earnings and increase profitability. There must
be more accountability, training, and professional governance in order for management
to be more efficient. Transparency and service quality will be improved by the adoption
of technology modernization. Lastly, the co-operative banking sector will be able
to thrive and remain resilient thanks to risk-based supervision by NABARD and RBI
and regional cooperation for the exchange of best practices.
LIMITATIONS OF THE STUDY
This study, though comprehensive,
has several limitations. It analyses only five DCCBs, which restricts
generalization across India’s diverse cooperative banking sector. The findings
rely entirely on secondary data from annual reports, assuming accuracy and
consistency. The analysis covers only one financial year (2023–2024), limiting
long-term trend interpretation. The CAMELS indicators used are selective due to
data availability, excluding qualitative factors such as governance quality,
technology adoption, and managerial practices. Macroeconomic and policy
variables were not considered, though they significantly affect bank
performance. Finally, the regression model is constrained by linear assumptions
and unexplained variance, indicating scope for deeper modelling.
FUTURE IMPLICATIONS
Future research can expand the
CAMELS framework by adding more indicators, including governance, technology,
and risk-based metrics. A multi-year or longitudinal analysis would offer
stronger insights into performance trends and regulatory impacts. Comparative
studies with urban co-operative, small finance, and commercial banks could
highlight structural differences. Integrating macroeconomic variables may
improve explanatory power. Advanced tools like machine learning or DEA can
enhance prediction and efficiency assessment. Future studies can also explore
governance, HR productivity, and digital transformation. Policymakers may use
such research to strengthen supervision, improve data reporting, modernize
operations, and design strategies for improving asset quality and earnings in DCCBs.
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