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23 

 

                       FINANCE AND BANKING 

                                                               IJFB VOL 13 NO 2 (2023) P-ISSN 2574-6081  E-ISSN 2574-609X 
                                                  

        Available online at https://www.cribfb.com 

         Journal homepage: https://www.cribfb.com/journal/index.php/ijfb 
                         Published by CRIBFB, USA 

CAMEL MODEL ANALYSIS AND DISCRIMINANT ANALYSIS OF 

COMMERCIAL BANKS’ PERFORMANCE IN GUYANA, SOUTH 

AMERICA                                                                      
 

 Vincent A. Raja (a)1   Sukrishnalall Pasha (b)    Kumar Ganapathy (c)    Maria Christy (d) 

 

(a) Senior Lecturer, Department of Mathematics, Physics and Statistics, Faculty of Natural Sciences, University of Guyana, Georgetown, Guyana, South 

America; E-mail: vincent.anthonisamy@uog.edu.gy 
(b) Finance Secretary, Ministry of Finance, Georgetown, Guyana, South America; E-mail: spasha@finance.gov.gy  
(c) Assistant Professor of Statistics, Department of Mathematics, SRM Arts and Science College, Kattankulathur, India; E-mail: kumarmat@srmasc.ac.in 
(d) Lecturer, Department of Accounting and Finance, School of Entrepreneurship and Business Innovation, University of Guyana, Georgetown, Guyana, 

South America; E-mail: maria.christy@uog.edu.gy  

 

 
A R T I C L E I N F O 
 

 

Article History: 
 

Received: 1st October 2023 

Revised: 2nd December 2023 

Accepted: 15th December 2023 
Published: 22nd December 2023 

 
Keywords: 

 

CAMEL Model, Prudential Ratios,  
Discriminant Analysis, Indigenous  

Banks, and Foreign Banks 

 
JEL Classification Codes: 
 

G21, G24, L25 
 

 
  

 
A B S T R A C T 

 
The study evaluates the performance of commercial banks in Guyana using prudential ratios that capture 
the five essential dimensions of a bank’s operation. It applies the CAMEL rating system and Linear 

Discriminant Analysis on quarterly prudential ratios of all the commercial banks that operated in 

Guyana between 2017 and 2021. The CAMEL analysis reveals that Demerara Bank Limited (DBL) is 

the best-performing bank, and the Guyana Bank for Trade and Industry (GBTI) is the worst-performing 

bank. However, the one-way ANOVA technique suggests no significant differences between the average 

values of the prudential ratios in the CAMEL model. Based on the Linear Discriminant Analysis, only 

four ratios differentiate between good-performing and poor-performing banks. These findings provide 

valuable insights to regulators that employ these tools to identify poor-performing banks to safeguard 
the stability and soundness of their domestic banking system. By applying the CAMEL rating system and 

Linear Discriminant Analysis simultaneously in Guyana, an emerging economy in the Caribbean, for 

the first time, the study contributes to the literature that utilizes these tools to assess the performance of 

commercial banks. 

 
 

© 2023 by the authors. Licensee CRIBFB, USA. This article is an open-access article distributed 

under the terms and conditions of the Creative Commons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

The primary objective of the study is to evaluate the performance of the commercial banks in Guyana by applying the 

CAMEL rating system and Linear Discriminant Analysis to selected quarterly prudential ratios for the period 2017-2021. 

These institutions are the main source of finance for private businesses, with assets accounting for more than sixty (60) 

percent of the total assets of the financial sector (Pasha, 2016). In this regard, commercial banks play a crucial role in the 

financial services sector by mobilizing savings and allocating them to high-return investments in the private sector that boost 

economic activities. Historically, these financial institutions have consistently reported capital adequacy ratios above the 

prudential requirement of eight (8) percent of risk-their weighted assets, relatively high profitability, and excess liquidity 

and reserves due to the relatively low level of financial intermediation. Consequently, it is understandable that the Guyanese 

banking system is described as highly capitalized, liquid, and profitable (Pasha, 2016). 

Since the discovery of hydrocarbon by Exxon Mobil in 2015, however, the level of financial intermediation has 

increased, as reflected by the notable growth of credit to the private sector by commercial banks. The exponential growth 

in lending by commercial banks that is inspired by the rapid transformation of the country’s economy may not only create 

more investment opportunities for commercial banks but also increase the risk exposure, stability, and soundness of these 

financial institutions. There is a bevy of studies that found that rapid credit growth is associated with banking fragility since 

it amplifies moral hazard and adverse selection problems (Billings et al., 1996; Fielding & Rewilak, 2015; Ghosh, 2010; 

                                                      
1Corresponding Author: ORCID ID: 0009-0001-7850-7763 
© 2023 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/ijfb.v13i2.2155 

 
To cite this article: Raja, V. A., Pasha, S., Ganapathy, K., & Christy, M. (2023). CAMEL MODEL ANALYSIS AND DISCRIMINANT ANALYSIS OF 

COMMERCIAL BANKS’ PERFORMANCE IN GUYANA, SOUTH AMERICA. Indian Journal of Finance and Banking, 13(2), 23-35. 

https://doi.org/10.46281/ijfb.v13i2.2155 

https://orcid.org/0009-0001-7850-7763
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/ijfb.v13i2.2155
https://orcid.org/0009-0008-4487-8524
https://orcid.org/0000-0002-0380-9900
https://orcid.org/0009-0005-2082-8520


Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

24 

Schularick & Taylor, 2012). Rapid credit growth also adversely impacts the performance of individual banks and manifests 

in relatively higher non-performing loans. Khemraj and Pasha (2016) established a positive relationship between rapid credit 

growth and non-performing loans in Guyana. Similar evidence was found in other countries (Vithessonthi, 2016). It is, 

therefore, important that the regulatory authority expands its arsenal of supervisory tools to strengthen the supervision of 

commercial banks, given the unprecedented expansion in credit by the local banking system. By evaluating commercial 

banks with the CAMEL rating system and Linear Discriminant Analysis, the study will provide valuable insights to 

regulators in Guyana about the application and robustness of these tools for assessing the performance of these financial 

institutions.  

Currently, there are six (6) commercial banks with numerous branches, of which three (3) are foreign-owned, and 

three (3) are indigenous banks. Like in other countries, the central bank regulates these institutions through on-site and off-

site examinations to protect depositors and ensure the stability and soundness of the financial services sector. The CAMEL 

rating system is a supervisory tool that has proven useful since the 2008 crisis. This tool was developed in the US in 1979 

by the three federal regulatory banking authorities (Daboh & Duramany-Lakkoh, 2023; Kumar & Malhorta, 2017; Roman 

& Sargu, 2013). According to Bodla and Verma (2006), the CAMEL rating system may help regulators identify banks 

needing special attention. For the first time in Guyana, this study employs the CAMEL rating tool and Linear Discriminant 

Analysis to assess the performance of Guyanese commercial banks using selected quarterly prudential ratios of these 

institutions for the period 2017-2021. The prudential ratios capture the five essential aspects of the commercial banks’ 

performance, often called component factors. These are Capital Adequacy, Asset Quality, Management Efficiency, Earning 

quality, and Liquidity position.  

Since no similar work was done previously in Guyana or the Caribbean, the study will also contribute to the 

literature. The findings may also be useful for the regulatory authorities responsible for on-site and off-site examinations of 

commercial banks in the Caribbean. Another important contribution of the paper is that it utilizes the CAMEL approach and 

Linear Discriminant Analysis simultaneously to evaluate the performance of commercial banks. Since very few studies 

employ both techniques together, this study will enhance the extant literature. 

The remainder of the study is organised as follows. The second section discusses the relevant literature. A 

description of the objective of the research and methodology follows in the third section. The results of the analysis are 

presented in the fourth section, and section five concludes with suggestions for future research. 

 

LITERATURE REVIEW 

Different academics, scholars, and decision-makers have evaluated the financial performance of the banking industry over 

the past two decades using the CAMEL framework. In one of the earliest studies, Barker and Holdsworth (1993) found that 

the CAMEL ratings system is an effective tool for predicting banks' failure and measuring these institutions' performance. 

Based on similar findings, Barr et al. (2002) argued that the CAMEL rating system is useful for measuring the financial 

performance of banks by regulators and examiners. Another important finding of Barr et al. (2002) is the strong association 

between the CAMEL rating and efficiency scores of banking institutions. Gaul and Jones (2021) examined the information 

content and the determinants of CAMELS rating between 1984 and 2020. They found that the composite ratings have 

significant predictive power for future bank performance and failures. Using quantile regressions, the study also found that 

CAMELS ratings convey helpful information regarding the operations and conditions of banks, especially those that are 

riskier and poorly-performing. More recently, Daboh and Duramany-Lakkoh (2023) employed regression analysis and data 

for the period 2012-2021 to evaluate the banking sector in Sierra Leone using the CAMEL framework. It found that capital 

adequacy and earning ability exert a positive and significant effect on the performance of banks. However, asset quality and 

liquidity management have a negative and insignificant relationship with banks' performance. This section reviews the 

strand of the literature that utilises the CAMEL approach to examine the relative performance of banks as well as rank the 

performance of these institutions in various countries. 

Venkatesh and Suresh (2014) examined four banks in Bahrain using data from 2006 to 2012 and the CAMELS 

framework. The government-owned National Bank of Bahrain (NBB) was ranked first overall in terms of capital adequacy, 

asset quality and liquidity. However, BBK held the first position in terms of management efficiency and liquidity. The 

authors argued that the findings invalidate the perception that government-owned banks underperform privately-owned 

banks. 

Ab-Rahim et al. (2018) compared the performance of sixty-three listed banks across Malaysia, Singapore, 

Thailand, and the Philippines between 1997 and 2011. It found that the banks in Singapore outperformed their counterparts 

in other countries. Notwithstanding, the banks from Thailand and Malaysia were the best performers for capital adequacy 

and asset quality, respectively. The banks from the Philippines were ranked as the best performers for earnings quality and 

liquidity.  

In Sri Lanka, Rauf (2016) examined the relative performance of a sample of privately-owned and publicly owned 

banks. The analysis revealed that the privately-owned banks outperformed their publicly owned counterparts in all the 

parameters of the CAMEL framework. Kumari (2017) applied the CAMEL framework to evaluate the performance of three 

foreign commercial banks in Sri Lanka over the period 2008-2014. It found that the capital adequacy and earnings of the 

banks were good but average for the other components of the CAMEL framework.  

Al-Najjar and Assous (2021) compared the performance of seven conventional banks with four Islamic banks using 

the CAMEL framework. The banks obtained different rankings under various parameters. The study extended the literature 

by estimating a regression model to determine the effects of the CAMEL ranking on the total deposits of Saudi banks. It 

found that CAR, efficiency ratio, ROE, and the ratio of loans to deposits positively impacted the banks' deposits. In contrast, 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

25 

the ratios of net income to net revenue and current and savings counts to total deposits (CASA) had the opposite impact on 

the banks' deposits.  

Wirnkar and Tanko (2008) investigated the performance of the largest Nigerian banks using the CAMEL 

framework. The authors found that no single factor in the CAMEL framework captured the performance of banks completely 

and therefore argued that regulators utilise the best ratios from the CAMEL framework when evaluating these institutions.  

Roman and Sargu (2013) employed the CAMELS framework to examine the financial soundness of 15 commercial 

banks that accounted for 78.1 percent of the banking sector assets in Romania. The authors used multiple ratios to assess 

each parameter from the CAMELS framework. The largest bank in the sample, Banca Comerciala Romana, was ranked 

highly for management quality, earnings, and profitability. However, this bank ranked low based on liquidity. The study 

also found that the banks in the sample were adequately capitalised.  

Altan et al. (2014) investigated the performance of three state-owned banks and twelve private sector banks in 

Turkey between 2005 and 2012. Using several ratios to represent each component of the CAMEL framework, the study 

revealed significant differences between state-owned and privately-owned banks, with Ziraat Bank obtaining the best 

ranking for asset quality and liquidity while Ada bank, Ak bank, and Halk bank obtained the top position in terms of capital 

adequacy, management quality, earning quality respectively.  

Atker (2017) compared the performance of Janata Bank Limited (a public sector bank) with NCC Bank Limited (a 

private sector bank) using the CAMEL framework and data for the period 2010-2014 in Bangladesh. It found that NCC 

Bank Limited performed better for all the component factors except Earnings Per Share (EPS) and liquidity ratios. Rahman 

and Islam (2018) extended this study by using the CAMEL rating system to evaluate seventeen private commercial banks 

in Bangladesh during the period 2010-2016. It found that the banks’ performance and ranking varied based on each 

component factor of the CAMEL framework. The NCC bank secured the first position on the group average under capital 

adequacy and asset quality. At the same time, Eastern Bank and One Bank Jamuna Bank were ranked the best under 

Management Efficiency, Earnings Quality, and Liquidity Management, respectively. The authors argued that the findings 

could help commercial banks' management formulate policies to improve their financial and overall performance.  

Several studies utilized the CAMEL framework to assess the performance of Banks in India. Bodla and Verma 

(2006), one of the earliest studies to use the CAMEL approach to assess the performance of banks in India, found that State 

Bank of India (SBI) was superior to ICICI in terms of capital adequacy between 2000 and 2004. However, ICICI 

outperformed SBI in terms of asset quality, earning quality, and management quality. According to the authors, there was 

no significant difference in the liquidity positions of the two banks. On the other hand, Mishra and Aspal (2012) assessed 

the overall performance and economic soundness of the State Bank Group using the CAMEL approach. They concluded 

that different banks ranked differently based on the CAMEL ratios. However, the authors cautioned that the difference 

between the CAMEL ratios was not statistically significant based on the ANOVA test. Additionally, the top five private 

banks in India were considered by Kumar and Malhotra (2017), and their operations were evaluated based on the CAMEL 

model's parameters. It found that the Axis Bank performed best, and the IndusInd Bank performed worst. Liquidity, revenue 

capacity, and capital sufficiency were the key reasons for the subpar performance of the latter. Meanwhile, Purohit and 

Bothra (2018) compared a private sector bank, ICICI Bank, with the State Bank of India (SBI) using CAMEL parameters. 

The results revealed variations in the performance of the banks based on these parameters. The authors argued that the 

difference in the parameters reflected SBI's efforts to improve its efficiency, revenue, and liquidity, as well as the attempts 

by the ICICI Bank to strengthen its capital adequacy and asset quality. More recently, Kumar, Christy, and Raja (2022) 

evaluated the financial performances of selected private banks in India using the CAMEL Model. The observation period 

spans five years, starting from 2017 to 2021. The study found that the CAMEL framework aided in determining a bank's 

overall performance based on key prudential ratios such as capital adequacy, asset quality, management efficiency, earnings 

quality, and liquidity.  

Even with the widespread application of the CAMEL approach in assessing the financial performance of 

commercial banks, the technique has limitations. For instance, Wirnkar and Tanko (2008) argued that no one factor in the 

CAMEL framework captures a bank's overall performance. Unsurprisingly, academics have complemented the CAMEL 

framework with analytical tools such as regression analysis, while others assessed the performance using CAMEL ratios 

and alternative techniques. Echekoba et al. (2014) examined the profitability of commercial banks in Nigeria using the 

CAMEL rating model and regression analysis. The study found that only liquidity management is significantly related to 

the performance of commercial banks, while the other factors are not. Using regression analysis and the CAMEL framework, 

Muhmad and Hashim (2015) revealed that capital adequacy, asset quality, and liquidity significantly impacted Malaysian 

banks' performance when Return on Assets is used as the dependent variable in the regression model. However, only asset 

quality and earning quality impacted their performance when the model used Return on Equity as the dependent variable. 

Ngoboka and Gatauwa (2020) employed the CAMEL rating systems and panel regression analysis to examine the financial 

performance of commercial banks in Rwanda. The study found that financial performance has a significant positive 

relationship with capital adequacy and asset quality and a significant negative relationship with management efficiency. 

When bank size is used as an independent variable, the relationships between bank performance and the CAMEL ratios are 

altered. This means that relationships between bank performance and the CAMEL ratios are sensitive to the model's 

functional form. Nair et al. (2018) compared the performance of privately owned banks with those owned by the government 

using the CAMEL framework and Discriminant Analysis. The study showed that the Debt-to-Equity ratio, Tier I capital 

ratio), net NPA ratio, and sensitive sector loans to total loans ratio explained the variation in the performance between these 

two categories of banks in India. Derviz and Podpiera (2008) investigated whether the CAMEL rating can predict the long-

run S&P rating of banks in the Czech Republic using panel data and an ordered probit model. It found that capital adequacy, 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

26 

credit spread, the ratio of loans to total loans, and value-at-risk for total assets and leverage are good predictors of the S&P 

rating. Gasbarro et al. (2002) employed panel models to examine the changing banking soundness of commercial banks in 

Indonesia during the Asian Financial Crisis. The study found that during this crisis, only one component of the CAMEL 

framework, earnings, discriminated among the ratings of the bank. In an attempt to identify gaps in the extant literature that 

utilized the CAMEL framework to assess the performance of banks, Maude and Dogarawa (2016) argued that the extant 

literature is mainly descriptive and only a few studies explore the relationship between CAMEL ratios and bank 

performance. According to the authors, there is also a paucity of research in emerging countries, especially with new 

economic situations. 

 

MATERIALS AND METHODS 
The study utilizes quarterly prudential ratios of all the commercial banks during the period 2017-2021. The banks include 

Republic Bank (Guyana) Limited (RBL), Bank of Baroda, Bank of Nova Scotia (BNS), Guyana Bank for Trade & Industry 

Limited (GBTI), Demerara Bank Limited (DBL), and Citizens Bank (Guyana) Incorporated (CBI). The first three banks are 

foreign-owned, and the others are indigenous. The prudential ratios that cover the five dimensions of the CAMEL framework 

(Capital Adequacy, Asset Quality, Management Efficiency, Earnings Quality, and Liquidity) are discussed below. 

Capital Adequacy determines whether a bank has sufficient capital to absorb unexpected losses from investing in 

risky assets. In Guyana, commercial banks are required to always maintain a minimum unimpaired paid-up capital of $250 

million. Additionally, these institutions are required to maintain a minimum capital adequacy ratio (CAR) of at least eight 

(8) percent, computed by dividing the total eligible capital of these institutions by their total risk-weighted assets (RWA). 

This study utilizes the CAR to assess the performance of the banks since it is used extensively in the literature (Al-Najjar & 

Assous, 2021; Ab-Rahim et al., 2018; Altan et al., 2014; Atker, 2017; Kumar et al., 2022; Mishra & Aspal, 2012; Rauf, 

2016; Roman & Sargu, 2013; Venkatesh & Suresh, 2014). According to Lad and Ghorpade (2022), banks with high capital 

adequacy ratios can satisfy their obligations, while those with low ratios are likely to face bankruptcy. Prior studies have 

also established that capital adequacy ratios impact bank performance (Derviz & Podpiera, 2008; Muhmad & Hashim, 2015; 

Ngoboka & Gatauwa, 2020; Nair et al., 2018). 

Since loans account for a large share of commercial banks’ assets, these institutions need to pay keen attention to 

the quality of their loan portfolios. The loans that are not performing (referred to as non-performing loans) could undermine 

the earning capacity of banks as well as erode their capital, which in turn may result in the insolvency of these institutions 

(Hou, 2007; Kane & Rice, 2001). Therefore, the quality of commercial banks’ loan portfolios is an important parameter of 

their financial strength (Frederick, 2012). Supervision Guideline No. 5 of the Bank of Guyana places the credit facilities of 

banks into the following categories: pass, special mention, substandard, doubtful, and loss. This study evaluates the asset 

quality of commercial banks using the ratio of non-performing loans to total loans. Several studies have utilised this ratio 

(Altan et al., 2014; Atker, 2017; Roman & Sargu, 2013; Venkatesh & Suresh, 2014) and found it had a significant 

relationship with bank performance (Derviz & Podpiera, 2008; Muhmad & Hashim, 2015; Ngoboka & Gatauwa, 2020; Nair 

et al., 2018).   

Management efficiency is essential for commercial banks' survival and long-term viability and stability (Ayayi & 

Sene, 2010; Ghosh & Sanyal, 2019). It is not surprising that management efficiency is considered when determining the 

performance of banks based on the CAMEL framework. Indeed, management efficiency has been found to have a significant 

relationship with the financial performance of banks (Getahun, 2015). This aspect of a bank's performance can be evaluated 

using a variety of qualitative and quantitative factors. To a large extent, some of the qualitative factors the regulatory 

authority considers regarding the management of local banking institutions are covered in Supervision Guideline No. 8 of 

the Bank of Guyana. The ratio of operating expense to total income is a traditional proxy for measuring management 

efficiency (Shah & Jan, 2014). This study uses the expense ratio to assess the relative efficiency of commercial banks. 

Earnings quality is an important factor to examine when assessing the performance of banks based on the CAMEL 

framework since earnings determine the internal capital formation and asset growth of banking institutions as well as their 

stability (Adem, 2022). There are numerous ratios for assessing the earnings quality of banks, but two of the most frequently 

used measures are Return on Assets (ROA) and Return on Equity (REO) (Al-Najjar & Assous, 2021; Altan et al., 2014; 

Atker, 2017; Dincer et al., 2011; Mishra & Aspal, 2012; Roman & Sargu, 2013; Venkatesh & Suresh, 2014). In this study, 

the return on assets (ROA) is used and computed by dividing the bank’s operating income by its total assets. Like the other 

components of the CAMEL framework, earnings quality has been found to impact the performance of banks significantly 

(Frederick, 2012; Getahun, 2015; Muhmad & Hashim, 2015).   

Liquidity, which refers to the ability of banks to meet their liabilities when they fall due (Gul Zeb, 2011), is another 

important component of the CAMEL framework. In Guyana, commercial banks are required to maintain liquidity ratios that 

equate to 20 percent of demand liabilities and 15 percent of time liabilities. The regulator sets these ratios to ensure that the 

commercial banks maintain sufficient liquid assets to satisfy the demand for cash by depositors whenever needed. While 

too little liquidity can trigger insolvency, too much can result in lower profitability (Kumar & Malhorta, 2017). The ratio of 

liquid assets to total assets is a good measure of the liquidity position of commercial banks and is used in this study. Several 

studies have employed this ratio (Altan et al., 2014; Atker, 2017; Ab-Rahim et al., 2018; Dincer et al., 2011; Mishra & 

Aspal, 2012; Rauf, 2016). It is also important to note that liquidity is significantly related to bank performance (Echekoba 

et al., 2014; Muhmad & Hashim, 2015). 

The study applies the CAMEL rating system to the ratios described above, a tool that is widely used for assessing 

the overall performance of commercial banks. With this technique, each bank is ranked based on the average value for each 

component factor. The bank with the highest average value for Capital Adequacy, Earnings Quality and Liquidity is ranked 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

27 

the best, followed by the bank with the second highest score, and so on. However, the bank with the lowest average ratio 

for Asset Quality and Earnings is ranked the best, followed by the second lowest value, and so on. The average ranking is 

then computed to determine the overall ranking of the banks.   

The study also employs Linear Discriminant Analysis (LDA) to identify the linear combination of prudential 

variables that differentiate between good-performing and bad-performing banks. This technique was utilised by Altman 

(1968) to predict firm failure. Mous (2005) also employed the same technique to predict bank failure. The discriminant 

function is generally defined thus:  

 

𝐷 = 𝑣0 + 𝑋1𝑣1 + 𝑋2𝑣2 + 𝑋3𝑣3 + ⋯ + 𝑋𝑝𝑣𝑝                                         (1) 

 

Where, D is the dependent or grouping variable,  𝑋1, 𝑋2, 𝑋3, … , 𝑋𝑝 are the explanatory variable and 

𝑣0, 𝑣1, 𝑣2, 𝑣3, … , 𝑣𝑝 are the corresponding estimated discriminant coefficients that can be used to predict the value of the 

dependent variable. In this two-group case, the discriminant function is defined specifically as follows: 

 

         𝐷 = 𝑣0 + 𝑋1𝑣1 + 𝑋2𝑣2 +  𝑋3𝑣3 + 𝑋4𝑣4 + 𝑋5𝑣5               (2) 

 

Where,  

D = The discriminant function for the dependent or grouping variable, good or poor performing banks. The banks are 

classified based on their ranking from the CAMEL approach. Those with ranking above the average are classified as good-

performing banks while those below are classified as poor-performing banks.   

X1 = eligible capital divided by risk-weighted assets 

X2 = non-performing loans divided by total loans 

X3 = operating expense divided by total income 

X4 = operating income divided by total assets 

X5 = liquid assets divided by total assets 

v1, v2, v3, v4, and v5 are the corresponding estimated discriminant coefficients.  

 

The study utilizes the Wilks Lambda to determine the validity of the discriminant analysis. A Wilks Lambda that 

is close to zero and statistically significant (i.e., less than 5 percent) suggests that discriminant analysis effectively 

differentiates the two groups. Once discriminant analysis is determined to be suitable, the discriminant function is estimated, 

and the various significance tests are performed. The Tests of Equality of Means are applied to ascertain the significance of 

each predictor, while the strength of the function is evaluated with the eigenvalue and canonical correlation. Predictors with 

p-values less than 10 percent are considered significant and therefore retained, while those with p-values above 10 percent 

are removed to derive the parsimonious. Eigenvalues higher than one and canonical correlation above 0.75 suggest a high 

degree of association between the discriminant score and the group, and the model is robust. The canonical correlation is 

akin to the R-square in regression analysis. 

The unstandardized coefficients of the parsimonious function are utilized to generate the discriminant score of each 

bank that is compared with the group Centroid scores. If the discriminant score of a bank is more than the Centroid, then 

the bank comes from Group 1 (Good-performing bank), and if the Discriminant score for a bank is below the Centroid, it 

comes from Group 2 (Poor-performing bank).  

As with multiple regression analysis, the predictors are assumed to be not highly correlated in the discriminant 

function. The Variance Inflation Factor (VIF) is computed for the predictors to ensure this assumption is not violated. Where 

the VIF is less than 10, multicollinearity is not a problem. The Box’s M test is also performed to determine whether the 

assumption of homogeneity of the variance-covariance matrices is not violated. An alpha value of more than 0.001 for the 

Box’s M test means that the assumption of equality of covariance matrices has not been violated, and one can proceed with 

the discriminant analysis. The Normality assumption of each predictor is tested by applying the Normal P-P plot for each 

predictor. Finally, the function is examined to determine how well it classifies each group correctly. 

 

DISCUSSIONS 

Results from the CAMEL rating system: The results from analysing the selected CAMEL ratios are discussed in this section.  

 

Capital Adequacy 
The commercial banks' capital adequacy ratio (CAR) displayed mixed performance during the review period. The CAR 

ratio for RBL and BOB trended upwards, while it declined for GBTI and DBL between 2017 and 2021. The ratio oscillated 

for BNS and CBI during the corresponding period. Notwithstanding, all the commercial banks reported capital adequacy 

ratios well above the Regulatory requirement level of 8 percent. It, therefore, means that all the banks maintained adequate 

capital to absorb unforeseen losses and avoid bankruptcy in the future.  

 

Table 1. Capital Adequacy Ratio (%) 

 
Year RBL GBTI BNS DBL CBI BOB 

2017 19.12 31.61 27.77 33.96 27.62 43.62 

2018 21.75 29.88 30.57 32.48 30.25 52.07 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

28 

2019 22.61 29.36 27.71 30.04 27.08 67.90 

2020 22.42 31.25 29.42 30.49 26.46 66.08 

2021 23.99 26.71 29.56 28.03 29.47 59.10 

Average 21.98 29.76 29.01 31.00 28.18 57.75 

Rank 6 3 4 2 5 1 

 

As per Table 1, BOB is in the top position with the highest group average CAR of 57.75, followed by DBL with a 

group average CAR of 31.0. RBL was the lowest-ranked bank, with a group average of 21.98. 

 

Asset Quality 

The ratio of non-performing loans to total loans of all the commercial banks trended downwards, suggesting an improvement 

in the asset quality of these institutions. This may be attributed to the improvement in the economy between 2017 and 2021. 

Table 2 shows that DBL is the best-ranked bank with the lowest group average ratio of non-performing loans to total loans 

of 1.85, followed by RBL with a group average of 2.18. The lowest-ranked commercial bank was GBTI, with a group 

average of 10.99. 

 

Table 2. Non-performing loans to total loans (%) 

 
Year RBL GBTI BNS DBL CBI BOB 

2017 2.68 11.91 5.68 2.74 8.99 8.57 

2018 2.44 11.91 6.62 2.30 6.65 10.15 

2019 2.00 11.84 7.19 1.62 5.95 13.24 

2020 1.82 10.89 5.63 1.41 4.57 9.56 

2021 1.95 8.43 4.22 1.18 3.44 6.79 

Average 2.18 10.99 5.87 1.85 5.92 9.66 

Rank 2 6 3 1 4 5 

 

Management Efficiency 

Which is important for a bank’s long-term growth and survival, was mixed during the years 2017-2021. Management 

efficiency, measured by the ratio of operating expenses to operating income, fluctuated for GBTI, RBL, and CBI. However, 

the ratio exhibited a general downward trend for BNS and steadily increased for BOB during the review period.   

 

Table 3. Operating expenses to operating income (%) 

 
Year RBL GBTI BNS DBL CBI BOB 

2017 68.34 63.97 64.12 64.39 70.91 44.37 

2018 66.05 65.80 66.16 67.29 73.39 54.03 

2019 66.54 63.96 65.68 65.02 72.47 54.53 

2020 70.25 65.51 64.88 67.49 76.20 56.58 

2021 67.26 63.77 59.86 69.45 72.42 58.83 

Average 67.69 64.60 64.14 66.73 73.08 53.67 

Rank 5 3 2 4 6 1 

 

Table 3 shows that BOB is the best-ranked commercial bank with the lowest average expense ratio of 53.67, 

followed by BNS, GBTI, DBL, and RBL with average expense ratios of 64.14, 64.60, 66.73, and 67.69, respectively. The 

lowest-ranked bank was CBI, with an expense ratio of 73.08. 

 

Earning Quality 

As noted earlier, the quality of earnings is a crucial criterion because it demonstrates a bank’s profitability and explains the 

sustainability and growth of its future earnings. In this study, the ROA is used to measure the earning quality of commercial 

banks in Guyana. The profitability ratio trended downward for RBL and BOB, suggesting that the earnings quality of these 

banks deteriorated during the review period. Conversely, the ROA displayed a general upward trend for CBI while it 

fluctuated for BNS and DBL. 

 

Table 4. Return on Asset (%) 

 
Year RBL GBTI BNS DBL CBI BOB 

2017 4.62 2.72 4.79 5.49 2.28 4.34 

2018 4.86 2.22 4.41 5.81 2.33 2.19 

2019 4.73 2.30 5.26 5.80 2.31 1.93 

2020 3.41 2.02 2.45 4.91 2.44 1.58 

2021 3.59 2.32 3.54 5.88 2.82 0.81 

Average 4.24 2.32 4.09 5.58 2.44 2.17 

Rank 2 5 3 1 4 6 

 

Table 4 shows that DBL is the best-performing bank with an average ROA of 5.58, followed by RBL, BNS, CBI, 

and GBTI with average ROAs of 4.24, 4.09, 2.44, and 2.32, respectively. With an average ROA of 2.17, BOB is ranked the 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

29 

lowest-performing bank. 

 

Liquidity 

The ratio of liquid assets to total assets, which indicates a bank’s capacity to fulfill its financial obligations, is a key 

performance indicator. When a bank can satisfy all its financial obligations as they fall due, it is considered adequately 

liquid. Except for DBL, the liquidity ratios of the commercial banks displayed a general upward trend.  

 

Table 5. Liquid assets to total assets (%) 

 
Year RBL GBTI BNS DBL CBI BOB 

2017 30.91 30.79 31.85 72.10 36.02 36.46 

2018 35.53 28.56 32.03 66.53 40.12 37.50 

2019 32.46 29.73 38.66 66.40 40.04 36.58 

2020 43.21 32.50 51.84 69.49 41.80 49.41 

2021 41.31 32.79 50.12 68.53 48.26 43.32 

Average 36.68 30.87 40.90 68.61 41.25 40.65 

Rank 5 6 3 1 2 4 

 

Table 5 shows that DBL is in the top position with an average ratio of liquid assets to total assets of 68.61, followed 

by CBI, BNS, BOB, and RBL with average ratios of 41.25, 40.90, 40.65, and 36.68, respectively. GBTI was ranked the 

lowest, with an average ratio of liquid assets to total assets of 30.87. 

 

Overall Ranking 

To assess the overall performance of the private sector banks, an aggregate rating is calculated, and the results are shown in 

Table 6 for the study period 2017-2021. It is observed that the capital adequacy ratio of BOB is at the upper position and 

RBL at the lower position. With respect to the asset quality parameter, DBL is ranked first, while RBL is in 2nd position, 

and GBTI is in the lowest position at 6th. Concerning the management efficiency parameter, it is observed that BOB ranks 

highest while BNS is in the 2nd position, and CBI is in the 6th or lowest position.  Regarding the earnings capacity parameter, 

DBL ranked first, while RBL is ranked 2nd and BOB ranked 6th position. Under the liquidity parameter, DBL is the top 

performer, followed by CBI while GBTI bank was ranked as the worst performer. The analysis revealed that DBL is ranked 

first with an overall average rank of 1.8, followed by BNS with an average rank of 3. BOB, RBL, and CBI are ranked 3rd, 

4th, and 5th, respectively. GBTI is positioned at last with an overall average score of 4.6.  

 

Table 6. Overall Ranking 

 
Ratios RBL GBTI BNS DBL CBI BOB 

C 6 3 4 2 5 1 

A 2 6 3 1 4 5 

M 5 3 2 4 6 1 

E 2 5 3 1 4 6 

L 5 6 3 1 2 4 

Average 4.00 4.60 3.00 1.80 4.20 3.40 

Rank 4 6 2 1 5 3 

 

ANOVA Result  
A one-way ANOVA test was used to determine if significant differences existed between the average values of the CAMEL 

ratios. The ANOVA test results showed that the calculated value of the F ratio (0.726) is lower than the table value (2.621) 

with df (5, 24) at 5% significance level, p-value = 0.610 > 0.05). This means there is no statistically significant difference 

between the average values of the CAMEL ratios, so the null hypothesis is accepted. It also shows no significant difference 

in the performance of the Private Sector Banks under the CAMEL model. CAMEL ratios with the data given in Table 6. 

The results of the one-way ANOVA test are shown in Table 7. 

 

Table 7. One-way ANOVA 

 
Items Sum of Squares df Mean Square Mean Square F Sig. 

Between Groups 11.500 5 2.300 2.300 .726 .610 

Within Groups 76.000 24 3.167 3.167   

Total 87.500 29     

 

Results: Discriminant Analysis Approach 

The ANOVA test revealed no significant difference between the prudential ratios based on the CAMEL model. The authors 

made an attempt to classify the six private sector banks into Performance Ratings. The experiment is designed as banks are 

initially grouped as good performing banks and poor performing banks. A discriminant analysis is performed, and the model, 

results, and interpretations are presented as follows. 

As explained earlier, the suitability of the Linear Discriminant Analysis is examined using Wilks’ Lambda. The 

Wilks’ Lambda value is 0.263 and statistically significant at the 1 percent level of significance (see Table 8), suggesting 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

30 

that discriminant analysis is very effective in differentiating between the two groups, good-performing, and poor-performing 

banks.  

 

Table 8. Wilks’ Lambda 

 
Test of Function(s) Wilks' Lambda Chi-square df Sig. 

1 .263 34.096 5 .000 

 

From Table 9, we observe that the average CAMEL ratios of the good-performing banks are superior to the average 

ratios of the poor-performing banks. In particular, the average Capital Adequacy, Earnings Quality, and Liquidity ratios of 

the good-performing banks are higher than the average for the poor-performing banks. Conversely, the average Asset 

Quality and Management Efficiency ratios for the good-performing banks were lower than the poor-performing banks (see 

Table 9).  

 

Table 9.Group Statistics 

 
Performance of the Banks Mean Std. Deviation Valid N (List wise) 

Unweighted Weighted 
Good Performance Capital Adequacy 39.253333 14.6632881 15 15.000 

Asset Quality 5.791833 3.6050497 15 15.000 

Management 

Efficiency 

61.510833 6.7752033 15 15.000 

Earning Quality 3.945833 1.7262288 15 15.000 

Liquidity 50.052333 14.8868675 15 15.000 

Bad Performance Capital Adequacy 26.638667 3.8515158 15 15.000 

Asset Quality 6.363500 3.9939406 15 15.000 

Management 

Efficiency 

68.455000 3.9141114 15 15.000 

Earning Quality 2.997333 .9982715 15 15.000 

Liquidity 36.267167 5.8521059 15 15.000 

Total Capital Adequacy 32.946000 12.3334704 30 30.000 

Asset Quality 6.077667 3.7495850 30 30.000 

Management 
Efficiency 

64.982917 6.4828472 30 30.000 

Earning Quality 3.471583 1.4670773 30 30.000 

Liquidity 43.159750 13.1402969 30 30.000 

 

Having determined that discriminant analysis is suitable, the discriminant function is estimated with selected 

CAMEL ratios used to rank the performance of the commercial banks. Among the five prudential ratios that we have 

considered, Asset Quality failed the tolerance test. Hence, in our further analysis and modelling, we only include Capital 

Adequacy, Management Efficiency, and Liquidity with p-values below 0.01 and Earning Quality with p-value below 0.10 

(see Table 10).  

 

Table 10. Tests of Equality of Group Means 

 
Items Wilks' Lambda F df1 df2 Sig. 

Capital Adequacy .729 10.385 1 28 .003* 

Asset Quality .994 .169 1 28 .684 

Management Efficiency .703 11.814 1 28 .002* 

Earning Quality .892 3.394 1 28 .076** 

Liquidity .715 11.140 1 28 .002* 

Note: * indicates significant at 1% level and ** indicates significant at 10% level. 

 

The eigenvalue, which measures the proportion of the variance explained by the function, is greater than 1, 

suggesting that the estimated function is robust (see Table 11). The Canonical Correlation of 0.859 confirms that the 

discriminant function discriminates well (see Table 11).  

 

Table 11. Eigenvalues 

 
Function Eigenvalue % of Variance Cumulative % Canonical Correlation 

1 2.808a 100.0 100.0 .859 

First 1 canonical discriminant functions were used in the analysis. 
 

Table 11 shows that the Variance Inflation Factor (VIF) for each variable is below 10. This indicates that the 

model does not suffer from multicollinearity. Additionally, the homogeneity assumption of the variance-covariance 

matrices is not violated based on the Box’s M Test. According to Table 13, the Box’s M value is 9.832. The F value 

(0.5356), which is not significant at the 1% level of significance (p-value (0.069) > 0.01). 
 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

31 

Table 12. Summary Statistics 

 
Model Unstandardized 

Coefficients 
Standardized 

Coefficients 
T Sig. Collinearity Statistics 

B Std. Error Beta Tolerance VIF 

1 (Constant) 2.007 1.206  1.664 .109   
Capital Adequacy -.020 .009 -.478 -2.284 .031 .250 4.004 

Asset Quality -.012 .025 -.088 -.471 .642 .312 3.205 

Management Efficiency .021 .014 .271 1.555 .133 .359 2.784 

Earning Quality -.159 .062 -.458 -2.567 .017 .344 2.906 

Liquidity -.014 .006 -.370 -2.230 .035 .398 2.510 

a. Dependent Variable: Performance of the Banks 

 

Table 13. Box’s M Test Results 

 
Box's M 9.832 
F Approx. .5356 

df1 15 

df2 3156.632 

Sig. .069 

Tests null hypothesis of equal population covariance matrices. 
 

The Normality assumption of each predictor is tested and valid since the Normal P-P plot for each predictor shows 

that the data follows fairly Normal distribution (see Figure 1).   

 

 

 
 

 
 

Figure 1. Normal P-P plots for Independent Variables 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

32 

The function is further interrogated to determine how well it predicts poor-performing and good-performing banks. 

Based on the results, the function classifies 90 percent of the original grouped cases correctly, of which 80 percent of the 

good-performing banks and 100 percent of the poor-performing banks are correctly satisfied (see Table 14). It, therefore, 

means that there is a 0.20 probability that the function commits a type 1 error and a zero probability it commits a type 2 

error.  

 

Table 14. Classification Results 

 

  Performance of the Banks                         Predicted Group Membership                                 Total 

  Good Performance Bad Performance 

Original Count Good Performance 12 3 15 

Bad Performance 0 15 15 

% Good Performance 80.0 20.0 100.0 

Bad Performance .0 100.0 100.0 

90.0% of original grouped cases correctly classified. 
 

The discriminant score for each bank is computed using the equation below based on the discriminant function 

coefficients computed in Table 15. 

 

Table 15. Canonical Discriminant Function Coefficients 

 

     95% Confidence Interval 

  Coefficient Bias Std. Error Lower Upper 

Asset Quality 1 .053 .019 .121 -.161 .314 

Capital Adequacy 1 .087 .012 .067 -.010 .271 

Earning Quality 1 .697 .130 .530 -.146 2.078 

Liquidity 1 .063 .002 .068 -.096 .209 

Management Efficiency 1 -.093 -.001 .122 -.299 .289 

(Constant) 1 -2.226 -.948 8.467 -22.970 10.892 

 

𝐷 = 2.226 + 0.087 ∗ 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐴𝑑𝑒𝑞𝑢𝑎𝑐𝑦 + 0.063 ∗ 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 + 0.697 ∗ 𝐸𝑎𝑟𝑛𝑖𝑛𝑔 𝑄𝑢𝑎𝑙𝑖𝑡𝑦 − 0.093 ∗
                 𝑀𝑎𝑛𝑎𝑔𝑒𝑚𝑒𝑛𝑡 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦                                                                                                                     (3)              
 

The discriminant score is compared with the Group Centroid =  (1.619 − 1.619)/2 =  0. Any bank which has 

a discriminant value above zero comes from the group of good-performing banks while any bank with a Discriminant value 

below zero comes from the group of poor-performing banks.  

 

Table 16. Functions at Group Centroids 

 
Performance of the Banks Function 

1 

Good Performance 1.619 

Bad Performance -1.619 

Unstandardized canonical discriminant functions evaluated at group means. 

 

The overall Discriminant Score with the rankings and Group classification are presented in the Table below. From 

Table 17, the best-performing banks are BNS, DBL, and BOB, with discriminant scores above 0, while the poor performers 

are RBL, GBTI, and CBL, with discriminant scores below 0. The best performer is DBL with the highest discriminant score 

(2.475), followed by BOB (1.881), BNS (-0.240), RBL (-1.343), GBTI (-1.602), and CBL (-2.274). 

 

Table 17. Discriminant Score, Bank Classification and Ranking 

 
Bank Discriminant Score Bank Classification Ranking 

RBL -1.342661 Poor Performing 4 

GBTI -1.602257 Poor Performing 5 

BNS -0.2402725 Poor Performing 3 

DBL 2.4752725 Good Performing 1 

CBL -2.2742185 Poor Performing 6 

BOB 1.881226 Good Performing 2 

 

CONCLUSIONS  

The study applies the CAMEL rating tool Guyana to quarterly prudential ratios of these institutions for the period 2017-

2021 to assess the performance of the six commercial banks in Guyana. While numerous researchers have applied the 

CAMEL approach to gauge the financial performance of banks in various countries, this was never done before in Guyana. 

The CAMEL analysis ranks DBL as the best performing bank with an overall average rank of 1.8, followed by BNS with 

an average rank of 3. BOB, RBL, and CBI are ranked 3rd, 4th, and 5th, respectively. GBTI is worst performing banks with 



Raja et al., Indian Journal of Finance and Banking 13(2) (2023), 23-35 

 

33 

an overall average score of 4.6 according to the CAMEL approach. The one-way ANOVA test finds that there is no 

statistically significant difference between the average values of the prudential ratios. 

Linear Discriminant Analysis shows that the prudential ratios differentiate between good and poor performance 

among the banks. The Wilks’ Lambda test reveals that the LDA is suitable. However, the results show that only four of the 

five prudential ratios explain variations in the performance of the banks. According to the results, the function classifies 90 

percent of the original grouped cases correctly, of which 80 percent of the good-performing banks and 100 percent of the 

poor-performing banks are correctly classified. It, therefore, means there is a 0.20 probability that the function commits a 

type 1 error and a zero probability it commits a type 2 error. Further, the discriminant scores suggest that DBL is the best-

performing bank, whereas CBL is the worst-performing bank. Based on the various diagnostic tests, the discriminant 

function did not violate the assumptions of normality, homogeneity of the variance-covariance matrices, and 

multicollinearity.  

The study's limitations included the number of commercial banks operating in Guyana. There are only six banks, 

and all are privately owned. There is no opportunity to compare the performance of privately and publicly owned banks. 

Also, a limited number of prudential ratios were available for analysis. Further, we encountered two conflicting results 

from the CAMEL approach and the Linear Discriminant Analysis.  

Therefore, future research should continue the investigation using the more versatile Bayesian method to resolve 

conflict. A study of this nature will also extend the literature since no previous work employed the Bayesian method to 

investigate the performance of banks in Guyana and the Caribbean.   

 

 
Author Contributions: Conceptualization, A.V.R, S.P., K.G. and MC.; Methodology, A.V.R. and S.P.; Software, A.V.R., S.P. and K.G.; Validation, 

A.V.R., S.P. and K.G.; Formal Analysis, A.V.R., S.P. and K.G.; Investigation, A.V.R. and S.P.; Resources, A.V.R., S.P. and M.C.; Data Curation, M.C.; 

Writing – A.V.R., S.P., K.G. and M.C.; Preparation, A.V.R., S.P., K.G. and M.C.; Writing – A.V.R. and S.P.; Visualization, A.V.R. and S.P.; Supervision, 
A.V.R., S.P., K.G. and M.C.; Project Administration, A.V.R., S.P., K.G. and M.C.; Funding Acquisition, A.V.R., S.P., K.G. and M.C. Authors have read 

and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 
groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: The Authors wish to acknowledge the comments and feedback from Dr. Collin M. Constantine, Lecturer, Girton College, University 
of Cambridge. 

Informed Consent Statement: Not applicable. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are also publicly available 
on the Bank of Guyana website [https://bankofguyana.org.gy/bog/publications/prudential-ratios]. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                            

 

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