







































Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

Indian Journal of Finance and Banking; Vol. 4, No. 1; 2020 
                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by Centre for Research on Islamic Banking & Finance and Business, USA 

92 

 

Revisiting the Efficiency of Indian Banking Sector: An Analysis of 
Comparative Models Through Data Envelopment Analysis 

 
 
 

Jyoti Tanwar 
Research Scholar 

Department of Economics and Finance 
BITS-Pilani, Pilani Campus 

Jhunjunu, Rajasthan- 333031, India 
E-mail: jyotanwar@gmail.com 

 
Himanshu Seth 

Research Scholar 
Department of Management 

BITS-Pilani, Pilani Campus, India 
E-mail: hseth91@gmail.com 

 
Arun Kumar Vaish 
Assistant Professor 

Department of Economics and Finance 
BITS- Pilani, Pilani campus, India 
E-mail: arunkvaish@gmail.com 

 
N V M Rao 

Professor 
Department of Economics and Finance 

BITS-Pilani, Pilani Campus, India 
E-mail: nvmrao@pilani.bits-pilani.ac.in 

 
 
Abstract 
This study examines the efficiency of the overall Indian banking industry using Data Envelopment Analysis (DEA) and to 
perform a comparative efficiency analysis of public, private, and foreign banks using six varied forms. Also, providing ranks 
to the banks based on their efficiency. The study incorporates BCC output-oriented DEA model using a sample of 50 
Indian banks (public banks = 17, private banks = 18, foreign banks = 15) for a period ranging from 2009-10 to 2018-19, 
hence incorporating the after-effects of the financial crisis and demonetization, this study uses panel data from 2009-10 to 
2018-19. The results showed that most of the Indian banks fall on the efficient side or are near to full efficiency. However, 
public banks outperform private and foreign banks in terms of their average efficiency. Results also specify that the 
performance of banks is sensitive to input-output variables, units under evaluation, and choice of the model. The current 
study has just focused on the internal factors for analyzing the efficiency of Indian banks; however, certain external factors 
might also impact the banks’ efficiency.  

 
1. Introduction 
History of banking in India is as old as Vedic civilization where usury, as well as kusidin (money lender), has been 
commonly referred. In modern times the banking in India originated in the last decade of the 18th century and has evolved 
over the years to the present shape. After independence, India got a formal banking structure catering to the elite class of 
society comprising mainly traders, industrialists, and high net worth individuals. Banks act as a financial intermediary by 
converting deposits into productive investment, creating new capital, and thus accelerating economic development. Few 
significant events such as nationalization of scheduled banks, creation of Statutory liquidity ratio and Cash reserve ratio, 
entry of private banks, and introduction of income recognition and asset classification norms to determine Non-Performing 
assets led to greater competition and strengthening of the Indian banking sector. Reserve Bank of India has regulated the 
banking system from time to time to ensure that banks are resilient to global turmoil.  

The working population of India is raising demand for banking services. Due to modernization and technological 
interference, banks have become accessible through mobile and internet. Mobile banking, internet banking, and ATMs have 
increased the volume of business for banks. Banks are also enjoying higher interest margins, which has led to competition. 
To curd competition and reduce NPAs, few public banks have decided to undergo a merger. Efficiency and productivity 

  
Keywords:    Banking, DEA, Efficiency, Ownership. 

. 
    

mailto:jyotanwar@gmail.com
mailto:hseth91@gmail.com
mailto:arunkvaish@gmail.com
mailto:nvmrao@pilani.bits-pilani.ac.in


Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

93 

 

analysis of banks became essential to reduce costs and increase profitability. This critical analysis has gained importance, 
mainly due to the speediest dynamic environment where banks are facing heavy competition, and survival has become 
difficult.  

The soundness and effectiveness of a banking system are often measured by efficiency, profitability improvement, 
increasing volume of funds flowing from savers to borrowers, and better-quality services for the customers. The efficiency 
and productivity analysis have caught the eye of the researcher in recent times. Researchers have faced one major hitch while 
measuring the effectiveness of banks. Banks provide products and services which are intangible. It is challenging to measure 
input injected, and output generated out of it. 

A plethora of models were developed to calculate performance and efficiency. One such model was introduced in 1978 
by Charnes et al., as Data Envelopment Analysis (DEA). DEA is a mathematical approach for evaluating the performance of 
a set of peer entities called Decision Making Units (DMU) that converts multiple inputs and multiple outputs. The 
simplicity of DEA over other models makes it a widely used method. In this model, its method and algorithm help in 
finding an optimization solution. Moreover, the input/output resulting in inefficiencies can be traced to every Decision-
Making Unit. 

Earlier, ratio analysis has been used as a cross-sectional technique to measure and compare the productivity of different 
industries. Ratio analysis is simple in use but also provides a limited explanation of results. Multiple data cannot be 
analyzed at once, limiting the use of ratio analysis. It loses its credibility when a comparison is made for firms having a 
different size. Results may also be ambiguous and incomplete. Data Envelopment overcame the limitations associated with 
ratio analysis. 

DEA has been implemented to measure the performance of many other industries such as railways (Kwak et al., 2016; 
George & Rangaraj, 2008); hospitals (Sharma & Dipasha, 2018); Airport (Keskin & Köksal, 2019); schools (Mante & 
O’Brien, 2002); communication (Kwon et al., 2008; Sigala, 2003); Retail distribution network (Lau, 2012); Environment 
(Mehta et al., 2019); and Energy (Ashuri et al., 2019) etc. 

In this research paper, an attempt is made to study efficiency analysis and performance benchmarking of Banks in India. 
The analysis is developed based on four areas of banking operational efficiency: deposit mobilization, fund conversion, non-
core activities, and cost-revenue management. The efficiency of the bank as a whole is also estimated by following the 
intermediation approach and production approach. The BCC model of the DEA technique is implemented to evaluate the 
efficiency of banks. 

The paper is comprised of an extensive literature review of DEA in banking, research methodology, sampling technique 
and data collection, the basis for selection of input and output, presentation and analysis of empirical findings, and 
conclusion. 
 
2. Literature Review for DEA in Banking 
DEA is a popular tool for the practitioner in deciding on a multidimensional framework. Initially, Charnes et al. (1978) 
extended Farrell’s efficiency measurement model. Charnes et al. (1978) developed a method that can incorporate multiple 
inputs and multiple outputs to determine single firm efficiency assuming Constant Return to Scale (CRS). Later, Banker et 
al. (1984) further extend the Charnes et al. (1978) CRS to variable returns to scale (VRS). In their study, they split the 
technical efficiency into pure technical efficiency and scale efficiency. 

The use of DEA in the banking industry helps management to benchmark different Decision-Making Units (DMUs). 
DEA is a widely used tool to evaluate the performance of banks based on multiple inputs and outputs. In prior studies on 
banking efficiency using DEA, researchers have used either a production approach or an intermediation approach. In the 
production approach, the bank is viewed as a producer of products and services using physical labor, physical assets, and 
other resources as inputs while deposits, loans granted and the number of transactions done is treated as output (Ferrier & 
Lovell, 1990; Fried et al., 1993; Sherman & Gold, 1985). Whereas, the intermediation approach views the bank as an 
intermediate that transforms and transfers financial assets from saver to borrowers (Elyasiani & Mehdian, 1995; Rangan et 
al., 1988; Mercan et al., 2003). The production approach and an intermediation approach became the foundation for the 
selection of inputs and outputs. 

The application of the DEA technique in recent literature is very vast. Wanke et al. (2019) studied the banking industry 
of MENA using Dynamic Network DEA. They tried to develop a relationship between financial and accounting indicators 
in banks used under the study. The banking industry is affected by the cultural and regulatory heterogeneity of MENA 
countries. Ownership, origin, and type of banks are also factoring that led to variation in efficiency scores of MENA banks. 
 Wang et al. (2019) estimated the efficiency of 18 large banks from all over the world by a dynamic slacks-based 
measure model in DEA. The Dynamic SBM model developed a new structure for interpreting the inputs and outputs. The 
findings of the study reveal the accurate efficiency of 18 banks to position them in the global market. Jreisat et al. (2018) 
undertake 14 Egyptian banks to investigate productivity changes using Malmquist indices in DEA model. Determinants of 
productivity change were further investigated using regression model. Maturity of banks, size of banks and higher loan to 
deposit ratio reflected higher potential for productivity.  
 Kamarudin et al. (2019) studied the revenue efficiency, cost efficiency, and profit efficiency of the domestic Malaysian 
Islamic banks and Malaysian foreign Islamic banks. The study revealed that Malaysian domestic banks are relatively revenue 
and cost-inefficient as compare to foreign banks operating in Malaysia. Profit inefficiency is influenced by higher revenue 
inefficiency. Further, Bank specific and external factors are analyzed to derive their relationship with domestic Malaysian 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

94 

 

Islamic banks’ efficiency.  The factors such as bank size, liquidity, and management quality have a positive effect on 
efficiency whereas, bank market power has negatively influenced the efficiency of banks in Malaysia. 
 Zhou et al. (2019) developed a three-stage model to examine the efficiency of Listed Chinese banks for the year 2014-
16. Inefficiencies of banks in three stages and different periods are evaluated. Unused assets were carried forward in this 
model. Employees' cost and fixed assets are termed as shared inputs because these can be used as inputs for multiple 
outputs. Credit risk is reflected by NPAs that are treated as undesired output in the study. The study indicated that 
increasing business scale and identifying sensitive banks can improve the performance of banks in the future. Grmanová & 
Ivanová (2018) analyzed the efficiency of banks based in the Slovak Republic for the years 2009 and 2013. In the year 
2009, most banks suffered the effects of the financial crisis. By the end of year 2013 most banks were able to overcome the 
ill-effects of the financial crisis. The efficiency of banks is determined using a combination of inputs and outputs. 
Ofori-sasu et al. (2019) studied the effect of the funding structure of 25 Ghana banks on technical efficiency. Deposit 
funding and non-deposit funding have a positive influence on technical efficiency. Ghana banks are generally inefficient as 
managers are unable to exploit technology, and optimally utilize inputs to generate outputs.  

Yannick et al. (2016) addressed the difficulty faced by banks of Côte d’Ivoire to convert deposits into credit. After 
investigating 25 banks, it is found that banks are inefficient in loan allocation due to incompatibility of production scale. 
Foreign Private banks are more efficient as comparative to pubic banks. Janet et al. (2015) examined the performance and 
productivity of state-owned commercial banks in China. Big four banks are analyzed from 1990 to 2008 to study the 
banks' reaction to bank reform. The banks under study reacted positively during the reform period in terms of technical 
efficiency, scale efficiency, and productivity change. The results also indicate that protection, support, and intervention of 
the government has reduced innovation and motivation among employees. 
 Desta (2016) has shown various applications of the DEA model.The DEA model can be used to determine the firm's 
efficiency, ranking of firms based on efficiency scores, and selecting the most efficient banks. Jemric & Vujcic (2002); 
Hauner & Peiris (2005); Matthews & Ismail (2006); Isik (2007) studied the efficiency of banks based on their ownership 
structure and revealed that foreign banks are more efficient and productive than domestic banks. On the other hand, Hadad 
et al. (2008); Sufian (2009); Tahir et al. (2009); Fethi et al. (2011) results presents that domestic banks are more efficient 
than foreign banks. 
 
2.1 Literature Review on DEA in Indian Banking  
Several studies have been carried out on Efficiency Analysis using DEA approach on Indian banking. Bhattacharyya et al.  
(1997) used DEA and stochastic frontier approach (SFA) to analyze the technical efficiency of banks and reasons for 
variations in efficiency scores, respectively. The results reveal that public sector banks performed way better than private and 
foreign banks in terms of technical efficiency. The performance is hindered by operational constraints, capital adequacy 
norms, and priority sector lending requirements. 

The study of Kumar & Gulati (2009) showed that the technical efficiency of Indian public banks has improved in the 
post-reform period. Most banks exhibit improvement in efficiency after the first phase of reform. By using the concept of 
convergence, it is discovered that the inefficient banks performed reasonably well, and few overtake the already existing 
efficient banks. The noteworthy reasons for the increase in performance are heightened competition due to entry of private 
sectors, increase in operational efficiency, reduction in the cost of financial transactions, rightsizing of the labor force, use of 
technology, and recovery of NPAs. A study conducted by Ray & Das (2010) during the post-reform period indicates that 
the profit efficiency of public banks is higher than private banks. The estimates of non-parametric kernel density manifest 
rightward-shift in the distribution of efficiency. The cause of inefficiency is the ineffective scale of economy, bank size, and 
product mix. 
 Sathye (2003); Mohan & Ray (2004) undertake banks of a developing country, i.e., India, in the research. The 
productive efficiency of banks is measured, and the efficiency scores demonstrate that public sector banks and foreign banks 
perform better than private banks. The study recommends that efforts should be made to bring down NPAs and the cost of 
operations. However, the study of (Shanmugam & Das, 2004) indicated the supremacy of deposits input in generating 
outputs. The output of banks such as non-interest income, investments, and credits has shown steady improvement over a 
period of time. Progress in the productivity of Indian banks proclaims the success of the implementation of reforms.  
 Sanjeev (2006, 2009) studied the Indian banks during the reform period to ensure the improvement in the efficiency of 
banks. The average efficiency scores of public and private sector banks have increased significantly. A few banks in the 
public sector have declined in their performance due to increased competition. The competition has risen with liberalization 
policy, giving a green signal for entry of private sectors in the banking industry. An increase in NPAs has shown an inverse 
relationship with the efficiency of banks. Likewise, Tamatam et al. (2019) proves that Public sector banks had less 
efficiency and improvement in technology when compared with private banks. 
 Zhao et al. (2008) examined Indian banks based on ownership, where foreign banks have higher technical efficiency 
scores in the first phase of deregulation than private and public banks. In the second phase, public banks performed better 
than others due to the rise in competition and the advancement of technology. The NPLs are taking into consideration to 
determine the output efficiency. It is, however, observed that priority sector lending affected the credit quality of banks. 
 Rezvanian et al. (2008) conducted a study on the Indian banking industry covering the period between 1998 and 2003. 
An attempt is made to examine the effect of ownership, technological progress, and productivity growth on the efficiency of 
banks. Based on the efficiency scores calculated for three types of banks, foreign-owned banks ranked one in the efficiency, 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

95 

 

whereas private banks ranked two, and public banks stood last in the ranking. The rationalization for inefficiency is the 
under-optimal scale of operations of most of the banks. 
 Das & Ghosh (2009) assessed that banks are cost-efficient in India and can control the wastage and underutilization of 
resources. However, in terms of profit efficiency, banks lie inside the efficient profit frontier. Higher capital and less Non-
performing loans exhibit an increase in the efficiency of most banks. 
 Jagwani (2012) studied the pure technical and scale efficiency of Indian banks.  The inefficiency of banks is justified by 
managerial sub-performance. Management is incapable of converting inputs into outputs optimally. Other than management 
quality, the sub-optimal scale of operation caused inefficiencies in the banking sector. The study of  Mukherjee et al. (2002) 
showed the positive outcome of liberalization on banking sector performance measures. With the implementation of a multi 
correlation clustering method, a strategic group of banks is identified based on efficiency measure. This approach will help 
bank managers to recognize their key competitors and plan for future strategies. 
 
2.2 Literature Review on Input and Output 
It is essential in DEA methodology to select appropriate inputs-outputs for estimating the efficiency of banks.  There is no 
consensus on the choice of input-output, and input-output variables affect the derived efficiency level. For the banking 
industry, there are two approaches, mainly: the production approach and the intermediation approach. The selection of 
deposit as an input variable or out variable is the only difference between the two approaches. For the production, approach 
deposit is treated as output, while for the intermediation approach, the deposit is treated as input. Various inputs and 
outputs used by authors for deriving the efficiency of banks are given under in table 1. 
 
Table 1. Summary of Input-Output Literature 
 

S. 
No 

Author and Year Input Output No. of 
banks 

Country 

1. Kantor & Maital 
(1999) 
 

Labour costs, services, area Number of demand deposits, customer 
services transactions, credit cards, 
commission on import-export, 
commercial accounts activity 

250 Mid-East 

2. Golany & Storbeck 
(1999) 

Labour, area, marketing Loans, deposits, number of accounts 
per customer, satisfaction 

182 
branches 

USA 

3. Mukherjee et al. 
(2002) 

Net worth, borrowings, operating 
expenses, number of employees, 
number of bank branches 

Deposits, Net Profits, advances, non-
interest income, interest spread 

68 banks India 

4. Sathye (2003) Interest expense, non-interest expense Interest income, non-interest income 94 India 

5. Ho & Zhu (2004) Assets, employees, branches, capital 
stocks 

Sales, deposits 41 Taiwan 

6. Howland & Rowse 
(2006) 

Non sales FTE, sales FTE, size, city 
employment rate 

Loans, deposits, average number of 
products/customers, customer loyalty 

162 Canada 

7. Ariff & Can (2008) Deposits and other funds, number of 
employees, physical capital 

Loans, investments 28 China 

8. Das & Ghosh 
(2009) 

Deposits, number of employees, 
capital-fixed asset, equity 

Loans and advances, investments, other 
income 

71 India 

9. Olson & Zoubi 
(2011) 

Deposits, labour, physical capital Net loans, dollar value of securities and 
other earning assets 

80 MENA 

10. Jagwani (2012) Net fixed assets, staff, deposits and 
borrowings, net worth, operating 
expenses, Non-performing assets, 
payments and provisions related to 
employees, other liabilities and 
provisions 

Net interest income, non-interest 
income, investments, net profits, 
advances 

42 banks India 

11. Řepková (2013) Labour, deposits Loans, net interest income 11 banks Czech 
Republic 

12. Malhotra et al. 
(2011) 
 

Efficiency ratio, Interest expensed to 
interest earned ratio, Loan to total 
fund ratio 

Return on asset, Interest income 
relative total fund, 
Interest spread, Asset utilization ratio, 
Capital adequacy  

35 banks India 

13. Yannick et al. 
(2016) 

Deposits, Fund borrowed Volume of loan granted 14 banks Côte 
d’Ivoire 

14. Desta (2016) Interest expense, Non-interest expense, 
Transaction deposit, Non-transaction 
deposit 
 

Gross loan, Other earning assets, 
Interest income, Non-interest income 

19 banks Africa 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

96 

 

15. Grmanová & 
Ivanová (2018)  

 Liabilities to banks and customers, 
operating cost  

Loans and advances to banks and 
customers, non-interest income. 

13 banks Slovakia 

16. Ofori-Sasu et al. 
(2019) 

Total cost, Total deposits Total loans, Other earnings 25 banks Ghana 

17. Kordrostami et al. 
(2016) 
 

Employees (The number of staffs and 
the manager of each branch), Expenses 
(Personnel, office, and other expenses) 
 

Deposits (Long term investment 
deposits, saving deposits and current 
deposits of government) Loans (The 
aggregation of short- and long-term 
personal loans) 

20 
branches 

Iran 

18. Kamarudin et al. 
(2019) 

Deposits, labour Loans, income 17 banks Malaysia 

19. Zhou et al. (2019) Interest payments, Employees’ salaries, 
Fixed assets 

Net interest incomes,  
Non-performing loans 

16 banks China 

20. Wanke et al. (2019) Net Loans, Total Earning Assets, 
Non-Earning Assets, Loan Loss 
Provisional Costs 

Net Interest Margin, Equity, Income 82 banks MENA 

21. Wang et al. (2019) 
 

Assets (tangible and intangible), 
capitalization (net worth) and 
liabilities  

Revenue as output and net interest 
income as good link 

18 banks All over 
the world 

 
3. Theoretical Framework and Methodology 
Over the past two decades, several parametric and non-parametric frontier models have received considerable attention for 
measuring the efficiency of various financial and non-financial institutions. Among these, a non-parametric performance 
assessment technique termed as data envelopment analysis (DEA) has increasingly become accessible for undertaking 
benchmarking studies concerning the banking sector (Kamarudin et al., 2019; Paradi et al., 2018). Charnes et al. (1978) 
originally designed the DEA technique for measuring the relative efficiencies of decision-making units (DMUs) or 
organizational units using the input-output dataset, also known as the CCR model which assumed a constant return to 
scale. Further, Banker et al. (1984) extended the CCR model for technologies exhibiting a variable return to scale. These 
DEA approaches involve constructing an efficient production frontier by applying linear programming techniques based on 
best practices over the data set. The efficiency of each DMU is then measured with this frontier. The DMUs with efficiency 
scores as '1' will lie on the frontier and would be efficient, and DMUs not lying on the frontier would be inefficient with 
scores less than 1. Most popularly, organizations involving multiple inputs for producing multiple outputs have been using 
the DEA technique for evaluating their organizations' efficiency.  

The available literature on DEA models has used various mathematical approaches. Essentially, these models establish 
which DMUs govern the efficient frontier or best practice frontier or envelopment surface. Mainly, there are two types of 
models - input-oriented and output-oriented. Input oriented model aims at reducing the number of inputs keeping the 
output levels at the same levels. The objective of the Output-oriented model is maximizing the level of output, following the 
same level of inputs. The present study incorporates specific DEA model as prescribed by Kumar & Gulati (2009). It uses 
the BCC output-oriented model for identifying the banks on the output frontier provided with several inputs at their 
disposal. Considering varying economies of scale in the practical scenario, using the BCC model for the analysis is more 
suitable. 
The following expression illustrates the DEA BCC model: 

max φ 
subject to 

∑ 𝜆𝑗
𝑛
𝑗=1 ∗ 𝑥𝑖𝑗 ≤ 𝑥𝑖0 

∑ 𝜆𝑗
𝑛
𝑗=1 ∗ 𝑦𝑟𝑗 ≥ φ𝑦𝑟0 

∑𝜆𝑗

𝑛

𝑗=1

= 1 

Where, 
▪ i = 1,2, 3,………….,m; 
▪ r = 1,2, 3,…………….,s; 
▪ j ≠ 0 
and, 

▪ φ signifies efficiency scores 

▪ 𝜆𝑗  denotes the weight of DMU (decision-making unit) j 

▪ 𝑥𝑖𝑗  denotes the i input of DMU j 

▪ 𝑦𝑟𝑗  denotes the r input of DMU j 

There are m inputs and s outputs for all N decision-making units. 
 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

97 

 

3.1 Sampling and Data  
The present study selects 50 banks in India, consisting of 17 Public Banks, 18 private sector banks, and 15 foreign banks; 
the list is given Appendix 1. The selection of banks is made as per the availability of data for years 2010-2019. The data 
collected for the research paper is annual and collected from the secondary source. Annual bank-level data is obtained from 
‘Capitaline Plus’ for the financial year 2009-2010 to 2018-2019, i.e., for 10 years. The time period taken in the study 
covers the post-financial crisis period and demonetization period effects. Therefore, the period is sufficient to study the 
drastic changes that occur in the economy.  
 
3.2 Selection of Input and Output 
The input and output variables selected for the study pertain to the existing literature. Mainly the input-output is guided by 
the operational pattern, performances, and objectives of the banks functioning in India. The input-output variables have 
been segregated in two headings: Area wise and Approach wise. Area-wise selection of input & output variables is further 
divided into four sets based on performance-based efficiency, whereas, Approach-wise selection of input & output variables 
is divided into two sets. The table 2 and table 3 shows the choice of input-output variables in the study. 
 
Table 2. Area wise four sets of input & output variables 
 

S.No Performance base efficiency Input Output 

1. Deposit Mobilization Efficiency (DME) Fixed Assets, Employee Cost,  
Interest expense on deposits 

Deposits 

2. Fund Conversing Efficiency (FCE) Fixed Asset, Employee Cost, 
Loanable fund 

Earning Assets 

3. Off-Balance Sheet Activities Efficiency (OBE) Fixed Assets, Employee Cost Total Non- Interest 
Income 

4. Cost- Revenue Management Efficiency (CRE) Total Interest Expense, Total 
Non-Interest Expense 

Net total Income 
Profit After Tax 
(PAT) 

 
Table 3. Approach wise two sets of input & output variables 
 

S. No Approach based efficiency Input  Output 

1. Intermediation Approach Efficiency (IAE) Loanable funds, Operating 
Expenses 

Earning Assets, Total 
Income, Profit After Tax 
(PAT) 

2. Production Approach Efficiency (PAE) Fixed Assets, Employee Cost Deposits, Earning Assets 

 
DME and FCE capture traditional functions of banks, whereas OBE measures the efficiency of the bank for non-

traditional activities. CRE depicts the cost minimization and revenue maximization efficiency of banks. In the production 
approach, a bank is treated as a producer of services, while in the intermediation approach, it is treated as a facilitator.  

In previous researched fixed assets and Number of employees were taken as a proxy for physical capital and labor. Here, 
in the present study, Fixed assets and Employee costs have been used instead. Here is a detail for inputs and outputs: 
(a) Deposit = saving deposits + demand deposits + term deposits 
(b) Loanable fund = deposits + borrowings 
(c)  Earning Assets = Investments + Advances 
(d) Total Non-Interest Income = Commission & Brokerage + Other non-interest income  
(e) Total Interest Expense = Interest expense on Deposits + Interest paid on borrowings 
(f) Total Non-Interest Expense = Operating Expenses + Non-operating expenses 
(g) Total Income = Interest income + Non-interest Income 
(h) Net total Income = Non- Interest Income + Net Interest Income (Interest income – interest expense) 

The study has undertaken six types of efficiency for each bank selected for 10 years using the VRS (BCC) model. The 
banks are segregated further based on ownership, i.e., public banks, private sector banks, and foreign banks. The purpose of 
the study is to find efficient banks as per the ownership structure based on all six types of efficiency and composite scores 
derived from the average of the above six types.  
 
4. Results and Discussion 
4.1 Private Sector Banks 
The study was conducted on 18 private banks, and efficiency scores were calculated based on six sets of Input & Output 
variables. From the descriptive analysis of statistic of efficiency, it was revealed that private banks were most efficient in 
Intermediation Approach Based Efficiency (97.35%), followed by Fund Conversion Efficiency (96.99%), Cost- Revenue 
Efficiency (88.41%), Deposit Mobilization Efficiency (81.56%), Production Approach Based Efficiency (71.54%). The 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

98 

 

lowest efficiency of banks was found in Off-Balance sheet Activity Efficiency, i.e., 36.36%. The inefficiency of the bank 
also reveals that there is further scope for banks to increase output from the same inputs.  
 
Table 4. Summary Statistics of efficiency of private banks 
 

The summary statistics  
of different efficiency IAE PAE DME FCE OBE CRE 

Composite  
Score 

No. of DMU 18 18 18 18 18 18 18 

Average efficiency 0.9735 0.7154 0.8156 0.9699 0.3636 0.8841 0.7870 

SD 0.0275 0.2259 0.1488 0.0257 0.3336 0.0997 0.1113 
Maximum efficiency 1 0.9751 0.9838 1 0.9972 1 1 

Minimum efficiency 0.9061 0.3447 0.3589 0.9040 0.0390 0.6548 0.5819 

No. of efficient banks 3 1 1 2 1 1 1 

 
The table 5 shows the list of banks that were fully efficient in six types of efficiency calculated. 
 
Table 5. List of fully efficient private banks 
 

Type of efficiency Name of the bank 

IAE HDFC, Nainital Bank, RBL Bank Ltd 

PAE HDFC 

DME Jammu & Kashmir Bank 

FCE HDFC, Nainital Bank 

OBE ICICI Bank 

CRE Nainital Bank 

 
It was observed that no bank was fully efficient in all six types of efficiencies. The composite score has been calculated 

by taking the average of IAE, PAE, DME, FCE, OBE, and CRE. The most efficient bank as per composite score is ICICI 
bank, followed by Axis bank, HDFC bank, IndusInd Bank, and Federal Bank. 

Just after the financial crisis, the performance of most banks in the private sector is inefficient. However, few banks 
recovered in a later period, and their performance has also accelerated. During the demonetization phase 2016-17, the 
business of banks has undoubtedly flourished, which is reflected in their performance. Excess deposit growth in the banking 
system during this period has increased the performance of most of the banks in the private sector. 

If we talk about non-traditional activities, then private banks are still lagging. Traditional activities generate a large 
portion of revenue, and non-traditional activities contribute a very insignificant amount.  

Figure 1 shows the efficiency score of private banks. The average score for 10 years has been taken to determine the 
efficiency score for IAE, PAE, DME, FCE, OBE and CRE. 

 

 
 

Figure 1. Efficiency score of private banks (average of 10 years) 
 

4.2 Public Sector Banks 
Likewise, the analysis was conducted on 17 Public sector banks, and the results were similar to private sector banks. The 
efficiency of banks is highest in IAE with 98.58%, followed by FCE - 98.28%, CRE - 94.36%, DME- 92.32%, PAE – 
84.94%, OBE- 78.92%. This analysis shows that the performance of banks is still based on traditional functions. Still, the 

0
0.2
0.4
0.6
0.8
1

1.2

IAE

PAE

DME

FCE

OBE

CRE



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

99 

 

off-balance-sheet activity efficiency of Public banks is significantly better than private and foreign banks. Public banks deal 
in insurance, brokerage, and generate fair revenue. 
 
Table 6. Summary statistics of efficiency of public sector banks 
 

The summary statistics  
of different efficiency 

IAE PAE DME FCE OBE CRE Composite  
score 

No. of DMU 17 17 17 17 17 17 17 

Average efficiency 0.9858 0.8494 0.9232 0.9828 0.7892 0.9436 0.9124 

SD 0.0158 0.1240 0.0668 0.0175 0.1594 0.0446 0.0554 
Maximum efficiency 1 1 1 1 1 1 1 

Minimum efficiency 0.9363 0.5913 0.7884 0.9472 0.4813 0.8761 0.8312 

No. of efficient banks 3 4 3 4 2 2 2 

 
The table 7 shows the list of banks that were fully efficient in six types of efficiency calculated. 
 
Table 7. List of fully efficient public sector banks 
 

Type of efficiency Name of the bank 

IAE Indian Bank, Punjab & Sind Bank, State Bank of India 

PAE Bank of Baroda, Corporation Bank, Punjab & Sind Bank, State Bank of India 

DME Bank of Baroda, Corporation Bank, Punjab & Sind Bank, State Bank of India 

FCE Andhra Bank, Corporation Bank, Punjab & Sind Bank, State Bank of India 

OBE Punjab & Sind Bank, State Bank of India 

CRE Punjab & Sind Bank, State Bank of India 

 
State bank of India and Punjab & Sind Bank are fully efficient in all six types of efficiencies – IAE, PAE, DME, FCE, 

OBE, and CRE. The most efficient bank as per composite score is again State bank of India and Punjab & Sind bank. Apart 
from them, other efficient banks with rank 2, 3, 4 & 5 are Corporation Bank, Bank of Baroda, Andhra Bank, and Canara 
bank, respectively. 

The efficiency of Public sector banks is generally stagnant even after the crisis. Most banks have recovered at a faster 
pace due to the governmental policies to revive the economy. 

During demonetization, Public sector banks have lion share in deposits leading to lower cost of funds, yet the 
performance of banks has declined. Most banks were not able to discharge their day to day operations during the 
demonetization phase. There was excess deposit but also withdrawals from banks. Most banks were busy exchanging banned 
currency notes as per the RBI guidelines and could not perform their regular work. These events led to a decline in the 
performance of banks.   

Figure 2 represents the efficiency score of public sectors banks. The average score for 10 years has been taken to 
determine the efficiency score for IAE, PAE, DME, FCE, OBE and CRE. 

 

 
 

Figure 2. Efficiency score of public sector banks (Average of 10 years) 
 

0

0.2

0.4

0.6

0.8

1

1.2

IAE

PAE

DME

FCE

OBE

CRE



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

100 

 

4.3 Foreign Sector Banks 
Foreign banks also showed a similar pattern of efficiency when compared with Public banks and Private Banks. Banks are 
highly efficient for IAE – 92.83% and least efficient in OBE – 55.88%. 
 
Table 8. Summary Statistics of efficiency of foreign banks 
 

The summary statistics  
of different efficiency 

IAE PAE DME FCE OBE CRE Composite 
 Score 

No. of DMU 15 15 15 15 15 15 15 

Average efficiency 0.9283 0.7871 0.8696 0.9374 0.5588 0.8532 0.8224 

SD 0.0911 0.2354 0.1567 0.0906 0.3323 0.1394 0.1192 

Maximum efficiency 1 1 1 1 1 1 1 

Minimum efficiency 0.7409 0.2443 0.5257 0.6863 0.0943 0.6134 0.6369 

No. of efficient banks 4 4 5 6 4 4 1 

 
The table 9 shows the list of banks that were fully efficient in six types of efficiency calculated. 
 
Table 9. List of fully efficient foreign banks 
 

Type of efficiency Name of the bank 

IAE Standard Chartered Bank, Barclays, Bank of Ceylon, American Express 

PAE Standard Chartered Bank, Barclays, Shinhan Bank, Krung Thai Bank Public Company Ltd 

DME Standard Chartered Bank, Barclays, AB Bank, Mashreq bank, PSC, Krung Thai Bank Public 
Company Ltd 

FCE Standard Chartered Bank, Barclays, Bank of Ceylon, American Express, AB Bank, Krung Thai 
Bank Public Company Ltd 

OBE Standard Chartered Bank, American Express, AB Bank, Krung Thai Bank Public Company Ltd 

CRE Bank of Ceylon, American Express, Mashreq bank, Standard Chartered Bank 

 
There is only one bank which is fully efficient in all six types of efficiency i.e., Standard Chartered Bank. When 

composite efficiency is calculated and banks are ranked, then also standard Chartered bank is ranked first. Banks that 
secured rank 2, 3, 4, and 5 are Krung Thai bank Public Company Ltd, AB Bank, Barclays, and Mashreq bank respectively. 
Figure 3 represents the efficiency score of foreign sectors banks. 
 

 
 

Figure 3. Efficiency score of foreign banks (Average of 10 years) 
 

 
 
 

0

0.2

0.4

0.6

0.8

1

1.2

IAE

PAE

DME

FCE

OBE

CRE



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

101 

 

4.4 Ownership Based Analysis 
From the tables provide above on descriptive statistics of efficiency based on IAE, PAE, DME, FCE, OBE, and CRE for 
Private, Public and Foreign banks, a summary table is derived which shows the most efficient, moderately efficient and least 
efficient banking sector. 

The analysis shows that Public sector banks are leading private and foreign banks in all six types of efficiency. In 
contrast, private banks are moderately efficient for IAE, FCE, and CRE and least efficient for PAE, DME, and OBE. As for 
Foreign banks, they are moderately efficient for PAE, DME, and OBE. 

 
    Table 10. Ownership-wise efficiency of banks 
 

 Efficiency level IAE PAE DME FCE OBE CRE 

Most efficient Public bank Public bank Public bank Public bank Public bank Public bank 

Moderately efficient Private bank Foreign bank Foreign bank Private bank Foreign bank Private bank 

Least efficient Foreign bank Private bank Private bank Foreign bank Private bank Foreign bank 

 
Later based on composite scores, it was revealed that public banks are leading, followed by private banks and foreign 

banks. Ownership of banks has a significant impact on the productivity and efficiency of banks. Public banks are more 
efficient than private and foreign banks (Jagwani, 2012). The efficiency of Public sector banks is 91.23%, Private bank – 
78.71%, and Foreign Bank – 82.24%. Though the efficiency of foreign banks is significantly more than Private banks yet 
when compared with standard deviation, Private bank shows lesser deviation. The dispersion amongst the public banks is 
very less when compared with private and foreign banks, which reflects the single ownership of government. Moreover, 
Public banks generally follow identical practices and policies. The competition has also contributed towards increased 
efficiency of Public banks as they thrive for their survival with expansion of private and foreign sector banks (Zhao et al., 
2008; Sanjeev, 2006, 2009; Kumar & Gulati, 2009). Rationalization of staff and branches has reduced cost burden on 
banks. The higher value of standard deviation in private and foreign banks indicates that the methods of banks might differ 
due to diverse management and ownership. 

Minimum dispersion in public sector banks is consistent with the results of Bhattacharyya et al. (1997); Sathye (2003). 
Public sector banks are more familiar with the regulatory system as compared to foreign banks. Bhattacharyya et al. (1997) 
justified the greater variability in the efficiency of foreign banks by showing that they depend on less stable wholesale or 
corporate resources, interbank borrowings, and refinance of assets. On the other hand, the domestic banks have an extensive 
network of branches and rely on a more stable retail banking business. 

 
Table 11. Summary of statistics based on ownership 

 

Summary of statistics  Public bank Private bank Foreign bank 

No. of DMU 17 18 15 

Mean 0.9124 0.7871 0.8224 

SD 0.0554 0.1112 0.1191 

Maximum 1 1 1 

Minimum 0.8312 0.5819 0.6369 

No. of efficient banks 2 1 1 

coefficient of variation 0.0607 0.1415 0.1449 

 
4.5 Overall Analysis 
Lastly, the efficiency score of all 50 banks without segregating them sector-wise was calculated, and the results are unique. 
The top five banks for overall efficiency are State Bank of India, ICICI, YES Bank, Axis Bank, and HDFC. Fully efficient 
banks for IAE, PAE, DME, FCE, OBE and CRE is shown in the table 12 below: 
 
Table 12. List of fully efficient banks in six types of efficiency 
 

IAE PAE DME FCE OBE CRE 

State Bank of India Bank of Baroda Bank of Baroda State Bank of 
India 

State Bank of 
India 

State Bank of 
India 

Barclays Bank of Ceylon State Bank of India Barclays AB Bank Ltd HDFC 

Bank of Ceylon Krung Thai Bank 
Public Company 

AB Bank Ltd AB Bank Ltd Krung Thai 
Bank Public 

Standard 
Chartered 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

102 

 

Ltd Company Ltd 

American Express ----- Mashreqbank Bank of Ceylon ----- Bank of Ceylon 

       ----- ----- Krung Thai bank 
Public Company 
Ltd 

Krung Thai bank 
Public Company 
Ltd 

----- American Express 

        ----- ----- ----- American 
Express 

----- Mashreqbank 

 
 
5. Conclusion 
The paper studies 50 banks operating in India for the period 2009-10 to 2018-19, segregated them based on ownership 
into Public, Private and foreign banks. The study is very comprehensive in a manner as it uses different inputs and outputs 
to calculate the efficiency of banks. It is noted that the DEA technique is sensitive to inputs and outputs, CCR and BCC 
model, Number of DMUs, and the number of inputs and outputs. The results in the study proved that by changing inputs 
and outputs, the efficiency score of banks has also fluctuated. The efficiency scores are based on technical efficiency in this 
study.  

Here in this study, efficiency is calculated using four key performance areas. The choice of Input and Output changes 
the efficiency scores each performance area, i.e., DME, FCE, OBE, and CRE. The model has also determined overall 
efficiency scores of banks using intermediation and production approach (IAE and PAE). Analyzing the efficiency in such a 
broader way made it possible to capture the multidimensional performance of banking. It provides insight for banks to 
improve performance in their weak areas of efficiency. Banks can also improve their productivity by bringing down the 
Non-Performing Loans, reducing the cost in fixed assets, and reducing the number of branches (Sathye, 2003; Chaluvadi et 
al., 2018). Digitalization and online banking have the potential to reduce both fixed asset cost and employee cost.   

The analysis depicts that the technical efficiency of Private Banks is relatively less in Off-balance sheet efficiency (OBE) 
and Production Approach efficiency (PAE) as compared to other efficiencies. Banks can improve performance by focusing 
more on commission-based activities, increasing brokerage income, and other non-interest income. The results are similar 
for public banks and foreign banks. All banks are relatively efficient in the Intermediation approach (IAE). Merger and 
Acquisition can also play a significant role in increasing the efficiency of banks. Many studies, like Ishwarya (2019); Patel 
(2018); Singh & Gupta (2015) have found significant positive impact on the productivity of banks. Through mergers & 
acquisitions, banks were able to pool resources and minimize cost. 

Generally, all banks have shown an increasing trend in efficiency scores with few exceptions. The efficiency score of 
Dhanlaxmi Bank, Tamilnad Bank, RBL Bank Ltd, and DCB (from private sector banks) has shown a decreasing trend in 
most of the types of efficiency. As for public banks, the performance of banks as accelerated over the period, but banks like 
Bank of India, Andhra Bank, and bank of Maharashtra performed poorly in OBE. In foreign banks, the growth is seen in 
most of the banks apart from a few. The poor-performing bank is Societe Generale. Over a while, the efficiency of a few 
banks declined due to intense competition as banks fight for resources.   

There are a few limitations of this study, which can become a further scope of research. The relevance of the inputs and 
outputs can be examined by using regression analysis. Moreover, in this study, only the internal factors affecting the 
performance of banks are taken whereas, environmental factors could also be used to test their influence on efficiency. The 
analysis may go further by decomposing technical efficiency change and technological progress using the DEA-based 
Malmquist productivity index. Also, scale efficiency can be calculated for further refinement of analysis. Data for 10 years 
for each bank was unavailable; therefore, many banks are dropped in the sample. Data for a few inputs, such as the number 
of employees and branches of banks for 10 years, is not available, resulting in either the dropping off input or used with 
modification.  More inputs and outputs can be used, but as the DEA model suggests that the Number of DMUs should be 
greater than 3(m+n) or (m*n); therefore, we have refined inputs and outputs in the model. 
 
References 
Ariff, M., & Can, L. (2008). Cost and profit efficiency of Chinese banks: A non-parametric analysis. China Economic 

Review, 19(2), 260–273. https://doi.org/10.1016/j.chieco.2007.04.001 
Ashuri, B., Wang, J., Shahandashti, M., & Baek, M. (2019). A data envelopment analysis (DEA) model for building energy 

benchmarking. Journal of Engineering, Design and Technology, 17(4), 747–768. 
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in 

Data Envelopment Analysis. Management Science, 30(9), 1078–1092. 
https://doi.org/10.1287/mnsc.30.9.1078 

Bhattacharyya, A., Lovell, C. A. K., & Sahay, P. (1997). The impact of liberalization on the productive efficiency of Indian 
commercial banks. European Journal of Operational Research, 98(2), 332–345. 

Chaluvadi, S., Raut, R., & Gardas, B. B. (2018). Measuring the performance efficiency of banks in a developing economy: 
The case study of Indian public sector vs private sector. Benchmarking: An International Journal, 25(2), 575–
606. https://doi.org/10.1108/BIJ-10-2016-0157 

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

103 

 

Operational Research, 2, 429–444. https://doi.org/10.1016/0377-2217(78)90138-8 

Das, A., & Ghosh, S. (2009). Financial Deregulation and Profit Efficiency : A Non-parametric Analysis of Indian Banks. 
Journal of Economic and Business, 61(6), 509–528. 

Desta, T. S. (2016). Are the best African banks really the best ? A Malmquist data envelopment analysis. Meditari 
Accountancy Research, 24(4), 588–610.  

Elyasiani, E., & Mehdian, S. (1995). The Comparative Efficiency Performance of Small and Large US Commercial Banks in 
the Pre- and Post Deregulation Eras. Applied Economics, 27, 1069–1079. 

Ferrier, G. D., & Lovell, C. A. K. (1990). Measuring Cost Efficiency in Bankng : Econometric and linear programming 
evidence. Journal of Econometrics, 46, 229–245. 

Fethi, M. D., Shaban, M., & Weyman-Jones, T. (2011). Liberalisation, Privatisation and the Productivity of Egyptian 
banks: a non-parametric approach. The Service Industries Journal, 31(7), 1143–1163. 

Fried, H. O., Lovell, C. A. K., & Schmidt, S. S. (1993). The Measurement of Productive Efficiency: Techniques and 
Applications. Oxford university. 

George, S. A., & Rangaraj, N. (2008). A performance benchmarking study of Indian Railway zones. Benchmarking: An 
International Journal, 15(5), 599–617. 

Golany, B., & Storbeck, J. E. (1999). A data envelopment analysis of the operational efficiency of bank branches. Interfaces, 
29(3), 14–26. https://doi.org/10.1287/inte.29.3.14 

Grmanová, E., & Ivanová, E. (2018). Efficiency of banks in Slovakia : Measuring by DEA models. Journal of International 
Studies, 11(1), 257–272.  

Hadad, M. D., Hall, M. J. B., Kenjegalieva, K., Santoso, W., Satria, R., & Simper, R. (2008). Efficiency in Indonesian 
banking: Recent evidence (WP 2008-13). 

Hauner, D., & Peiris, S. J. (2005). Bank Efficiency and Competition in Low-Income Countries : The Case of Uganda 
(WP/05/240). IMF Working Paper. 

Ho, C.-T., & Zhu, D.-S. (2004). Performance measurement of Taiwan’s commercial banks. International Journal of 
Productivity and Performance Management, 53(5), 425–434. https://doi.org/10.1108/17410400410545897 

Howland, M., & Rowse, J. (2006). Measuring bank branch efficiency using data envelopment analysis: Managerial and 
implementation issues. Information Systems and Operational Research, 44(1), 49–63. 
https://doi.org/10.1080/03155986.2006.11732739 

Ishwarya, J. (2019). A Study on Mergers and Acquisition of Banks and a Case Study on SBI and its Associates. 
International Journal of Trend in Research and Development, September, 22–26. 

Isik, I. (2007). Bank ownership and productivity developments : Evidence from Turkey. Studies in Economics and Finance, 
24(2), 115–139.  

Jagwani, B. (2012). Efficiency Measurement in the Indian Banking Industry : An Application of Data Envelopment 
Analysis. Vision, 16(4), 315–331.  

Janet, J. X., Gan, C., & Hu, B. (2015). An empirical analysis of China’s Big four state-owned banks’ performance : A data 
envelopment analysis. Journal of Banking Regulation, 16, 1–21.  

Jemric, I., & Vujcic, B. (2002). Efficiency of Banks in Croatia : A DEA Approach. Comparative Economic Studies, 
XLIV(2), 169–193.  

Jreisat, A., Hassan, H., & Shankar, S. (2018). Determinants of the Productivity Change for the Banking Sector in Egypt. 
Global Tensions in Financial Markets (Research in Finance), 34, 86–116. https://doi.org/10.1108/S0196-
382120170000034011 

Kamarudin, F., Sufian, F., Aina, N., Anwar, M., & Hussain, H. I. (2019). Bank Efficiency in Malaysia a DEA Approach. 
Journal of Central Banking Theory and Practice, 8(1), 133–162.  

Kantor, J., & Maital, S. (1999). Measuring Efficiency by Product Group: Integrating DEA with Activity-Based Accounting 
in a Large Mideast Bank. Interfaces, 29(3), 27–36. 

Keskin, B., & Köksal, C. D. (2019). A hybrid AHP/DEA-AR model for measuring and comparing the efficiency of 
airports. International Journal of Productivity and Performance Management, 68(3), 524–541. 

Kordrostami, S., Amirteimoori, A., & Noveiri, M. J. S. (2016). Ranking of bank branches with undesirable and fuzzy data: 
A DEA-based approach. Iranian Journal of Optimization, 8(2), 71–77. 

Kumar, S., & Gulati, R. (2009). Did efficiency of Indian public sector banks converge with banking reforms? International 
Review of Economics, 56(1), 47–84. https://doi.org/10.1007/s12232-008-0057-2 

Kwak, N. K., Chun, Y. S., & Kim, S. (2016). Benchmarking of rail transport service performance through DEA for Indian 
railways. The International Journal of Logistic Management, 27(3), 629–649. 

Kwon, H., Stoeberl, P. A., & Joo, S. (2008). Measuring comparative efficiencies and merger impacts of wireless 
communication companies. Benchmarking: An International Journal, 15(3), 212–224. 

Lau, K. H. (2012). Distribution network rationalisation through benchmarking with DEA. Benchmarking: An International 
Journal, 19(6), 668–689. 

Malhotra, R., Malhotra, D. K., & Poteau, R. R. (2011). A DEA Based Multidimensional Framework for Analyzing 
Commercial Banks. Proceedings of the Northeast Business & Economics Association, 281. 

Mante, B., & O’Brien, G. (2002). Efficiency measurement of Australian public sector organisations. Journal of Educational 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

104 

 

Administration, 40(3), 274–298. 
Matthews, K., & Ismail, M. (2006). Efficiency and Productivity Growth of Domestic and Foreign Commercial Banks in 

Malaysia (E2006/2). 
Mehta, K., Sharma, R., & Vyas, V. (2019). Efficiency and ranking of sustainability index of India using DEA-TOPSIS. 

Journal of Indian Business Research, 11(2), 179–199. 
Mercan, M., Reisman, A., Yolalan, R., & Emel, A. B. (2003). The effect of scale and mode of ownership on the financial 

performance of the Turkish banking sector: results of a DEA-based analysis. Socio-Economic Planning Sciences, 
37, 185–202. 

Mukherjee, A., Nath, P., & Pal, M. N. (2002). Performance benchmarking and strategic homogeneity of Indian banks. 
International Journal of Bank Marketing, 20(3), 122–139. https://doi.org/10.1108/02652320210430965 

Ofori-sasu, D., Abor, J. Y., & Mensah, Lord. (2019). Funding structure and technical efficiency - A data envelopment 
analysis ( DEA ) approach for banks in Ghana. International Journal of Managerial Finance, 15(4), 425–443.  

Olson, D., & Zoubi, T. A. (2011). Efficiency and bank profitability in MENA countries. Emerging Markets Review, 12(2), 
94–110. https://doi.org/10.1016/j.ememar.2011.02.003 

Paradi, J. C., Sherman, H. D., & Tam, F. K. (2018). Bank Branch Operational Studies Using DEA. In Data Envelopment 
Analysis in the Financial Services Industry (pp. 145-158). Springer, Cham. 

Patel, R. (2018). Pre & Post-Merger Financial Performance: An Indian Perspective. Journal of Central Banking Theory and 
Practice, 7(3), 181–200. https://doi.org/10.2478/jcbtp-2018-0029 

Mohan, T., & Ray, S. C. (2004). Comparing Performance of Public and Private Sector Banks : A Revenue Maximisation 
Efficiency Approach. Economic & Political Weekly, 39(12), 1271–1276. 

Rangan, N., Grabowski, R., Aly, H. Y., & Pasurka, C. (1988). The technical efficiency of US banks. Economics Letters, 
28(2), 169–175. 

Ray, S. C., & Das, A. (2010). Distribution of cost and profit efficiency : Evidence from Indian banking. European Journal 
of Operational Research, 201(1), 297–307. https://doi.org/10.1016/j.ejor.2009.02.030  

Řepková, I. (2013). Estimation of banking efficiency in the Czech Republic: Dynamic data envelopment analysis. 
DANUBE: Law and Economics Review, 4(4), 261–275. https://doi.org/10.2478/danb-2013-0014 

Rezvanian, R., Rao, N., & Mehdian, S. M. (2008). Efficiency change , technological progress and productivity growth of 

private, public and foreign banks in India : evidence from the post- liberalization era. Applied Financial 
Economics, 18(9), 701–713. https://doi.org/10.1080/09603100701222317  

Sanjeev, G. M. (2006). Data Envelopment Analysis (DEA) for Measuring Technical Efficiency of Banks. Vision- The 
Journal of Business Prespective, 10(1), 13–27. 

Sanjeev, G. M. (2009). Efficiency of Indian Public Sector Banks: An Application of DEA Approach. The IUP Journal of 
Applied Finance, 15(11), 52–61. 

Sathye, M. (2003). Efficiency of banks in a developing economy : The case of India. European Journal of Operational 
Research, 148, 662–671.  

Shanmugam, K. R., & Das, A. (2004). Efficiency of Indian commercial banks during the reform period. Applied Financial 
Economics, 14(9), 681–686. https://doi.org/10.1080/0960310042000233458 

Sharma, A. V., & Dipasha, G. (2018). Technical efficiency of private sector hospitals in India using data envelopment 
analysis. Benchmarking: An International Journal, 25(9), 3570–3591. 

Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: Evaluation with Data Envelopment Analysis. Journal 
of Banking & Finance, 9(2), 297–315. 

Sigala, M. (2003). The information and communication technologies productivity impact on the UK hotel sector. 
International Journal of Operations & Production Management, 23(10), 1224–1245. 

Singh, G., & Gupta, S. (2015). An Impact of Mergers and Acquisitions on Productivity and Profitability of Consolidation 
Banking Sector in India. Journal of Research in Management & Technology, 4(9), 33–48. 

Sufian, F. (2009). Determinants of bank efficiency during unstable macroeconomic environment : Empirical evidence from 
Malaysia. Research in International Business and Finance, 23, 54–77.  

Tahir, I. M., Bakar, N. M. A., & Haron, S. (2009). Evaluating Efficiency of Malaysian Banks Using Data Envelopment 
Analysis. International Journal of Business and Management, 4(8), 96–106. 

Tamatam, R., Dutta, P., Dutta, G., & Lessmann, S. (2019). Efficiency analysis of Indian banking industry over the period 
2008–2017 using data envelopment analysis. Benchmarking: An International Journal. 

Wang, C., Luu, Q., Nguyen, T., & Day, J.-D. (2019). Assessing Bank Performance Using Dynamic SBM Model. 
Mathematics, 7(1), 1–13.  

Wanke, P., Kalam, A., Emrouznejad, A., & Antunes, J. (2019). A dynamic network DEA model for accounting and 

financial indicators : A case of efficiency in MENA banking. International Review of Economics and Finance, 61, 
52–68.  

Yannick, G. Z. S., Hongzhong, Z., & Thierry, B. (2016). Technical Efficiency Assessment Using Data Envelopment 
Analysis: An Application to the Banking Sector of Côte D’Ivoire. Procedia - Social and Behavioral Sciences, 235, 
198–207. https://doi.org/10.1016/j.sbspro.2016.11.015 

Zhao, T., Casu, B., & Ferrari, A. (2008). Deregulation and productivity growth : a study of Indian commercial banking. 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

105 

 

International Journal of Business Performance Management, 10(4), 318–343.  
Zhou, X., Xu, Z., Chai, J., Yao, L., Wang, S., & Lev, B. (2019). Efficiency evaluation for banking systems under 

uncertainty: A multi-period three-stage DEA model. Omega, 85, 68–82.  
 
Appendix -A  
 
Table 1. List of banks 
 

S.No. Private banks Public banks Foreign bank 

1 Axis bank Allahabad Bank Standard Chartered Bank 

2 DCB Bank Ltd. Andhra Bank Barclays Bank 

3 HDFC Bank Bank of Baroda AB bank Ltd. 

4 ICICI bank Ltd. Bank of India BNP Paribas 

5 IndusInd Bank Ltd. Bank of Maharashtra Societe Generale 

6 Kotak mahindra bank Ltd. Canara Bank Shinhan Bank 

7 YES bank Corporation Bank Bank of Ceylon 

8 Dhanlaxmi bank Indian Overseas bank Abu Dhabi Commercial bank 

9 
City Union bank Indian Bank Credit Agricole Corporate bank 

10 Federal Bank Oriental Bank of Commerce Bank of Bahrain & Kuwait bank 

11 Jammu and Kashmir bank Punjab & Sind Bank Mashreqbank P S C bank 

12 Karnataka Bank Punjab National Bank MUFG Bank Ltd 

13 Karur Vysya Bank State Bank of India Firstrand Bank Ltd 

14 Lakshmi Vilas Bank Syndicate bank Krung Thai Bank Public bank 

15 Nainital Bank UCO Bank American Express Bank Ltd. 

16 RBL bank Ltd. Union Bank of India   

17 South Indian Bank United Bank of India   

18 Tamilnad Mercantile bank     

 
Appendix -B  
 
Table 2. Average Efficiency score of private sector banks (10 years) 
 

DMU IAE PAE DME FCE OBE CRE Composite  
score 

composite  
rank 

Axis bank 0.9960 0.9738 0.9289 0.9869 0.9614 0.9668 0.9690 2 

DCB Bank Ltd. 0.9834 0.5335 0.8124 0.9974 0.1360 0.7078 0.6951 15 

HDFC Bank 1 0.9752 0.8349 1 0.9449 0.9588 0.9523 3 

ICICI bank Ltd 0.9999 0.9688 0.9517 0.9761 0.9972 0.9997 0.9822 1 

IndusInd Bank Ltd. 0.9798 0.9092 0.9409 0.9621 0.6480 0.7640 0.8673 4 

Kotak mahindra bank 0.9988 0.8611 0.7360 0.9988 0.6259 0.8947 0.8525 6 

YES bank 0.9657 0.9060 0.7042 0.9614 0.4144 0.9813 0.8222 8 

Dhanlaxmi bank 0.9305 0.3642 0.3590 0.9677 0.2154 0.6549 0.5819 18 

City Union bank 0.9635 0.4209 0.7985 0.9627 0.1497 0.8919 0.6979 14 

Federal Bank 0.9739 0.9071 0.9182 0.9679 0.3933 0.9550 0.8526 5 

Jammu & Kashmir bank 0.9335 0.9391 0.9839 0.9490 0.3557 0.8568 0.8363 7 

Karnataka bank 0.9525 0.7310 0.9030 0.9387 0.0779 0.8249 0.7380 11 

Karur Vysya bank 0.9753 0.6274 0.8567 0.9508 0.0537 0.8734 0.7229 13 

Lakshmi Vilas bank 0.9764 0.5899 0.9301 0.9532 0.2284 0.8162 0.7490 10 

Nainital bank 1 0.6345 0.6913 1 0.2031 1 0.7548 9 

RBL bank Ltd 1 0.4404 0.6728 0.9856 0.0518 0.9589 0.6849 16 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

106 

 

South Indian bank 0.9061 0.7505 0.8762 0.9040 0.0391 0.9350 0.7352 12 

Tamilnad Mercantile 
Bank 

0.9884 0.3448 0.7839 0.9963 0.0495 0.8751 0.6730 17 

Note: Composite score = IAE+PAE+DME+FCE+OBE+CRE / 6 
 
Appendix -C 
 
Table 3. Average Efficiency score of public sector banks (10 years) 
 

DMU IAE PAE DME FCE OBE CRE Composite  
score 

composite  
rank 

Allahabad Bank 0.9745 0.7154 0.8397 0.9768 0.6787 0.9350 0.8533 14 

Andhra Bank 0.9835 0.9125 0.9753 1 0.8343 0.9574 0.9438 4 

Bank of Baroda 0.9363 1 1 0.9961 0.9244 0.9836 0.9734 3 

Bank of India 0.9993 0.8897 0.9223 0.9486 0.8061 0.9031 0.9115 8 

Bank of Maharashtra 0.9790 0.5913 0.9811 0.9750 0.4813 0.9797 0.8312 16 

Canara Bank 0.9878 0.9090 0.8672 0.9880 0.9050 0.9607 0.9363 5 

Corporation Bank 0.9880 1 1 1 0.9932 0.8818 0.9772 2 

Indian Overseas Bank 0.9832 0.6820 0.7885 0.9472 0.6979 0.8896 0.8314 15 

Indian Bank 1 0.6866 0.8742 0.9987 0.6169 1 0.8627 13 

Oriental Bank of 
Commerce 

0.9874 0.8604 0.8915 0.9935 0.7719 0.8838 0.8981 10 

Punjab & Sind Bank 1 1 1 1 1 1 1 1 

Punjab National Bank 0.9987 0.8823 0.9004 0.9833 0.8527 0.9822 0.9333 6 

State Bank of India 1 1 1 1 1 1 1 1 

Syndicate bank 0.9895 0.8052 0.8983 0.9804 0.5911 0.9278 0.8654 11 

UCO Bank 0.9960 0.8542 0.9260 0.9612 0.5724 0.8761 0.8643 12 

Union Bank of India 0.9875 0.8638 0.8486 0.9880 0.8355 0.9244 0.9080 9 

United Bank of India 0.9687 0.7876 0.9819 0.9725 0.8547 0.9566 0.9203 7 

 
Appendix -D  
 
Table 4. Average Efficiency score of foreign banks (10 years) 
 

DMU IAE PAE DME FCE OBE CRE Composite  
score 

composite  
rank 

Standard Chartered Bank 1 1 1 1 1 1 1 1 

Barclays Bank 1 1 1 1 0.5192 0.8471 0.8944 4 

AB bank ltd 0.9862 0.7845 1 1 1 0.8423 0.9355 3 

BNP Paribas 0.9635 0.9766 0.9766 0.9789 0.3777 0.9225 0.8660 8 

Societe Generale 0.8656 0.5855 0.6774 0.8466 0.1692 0.6774 0.6369 15 

Shinhan Bank 0.9069 1 0.9967 0.9448 0.3134 0.8343 0.8327 9 

Bank of Ceylon 1 0.9030 0.7542 1 0.5935 1 0.8751 7 

Abu Dhabi Commercial 
Bank 

0.7409 0.7419 0.8742 0.6863 0.0943 0.7016 0.6399 14 

Credit Agricole Corporate 
Bank 

0.9910 0.8346 0.8072 0.9637 0.4313 0.8400 0.8113 10 

Bank of Bahrain & Kuwait 
Bsc 

0.7744 0.7578 0.8649 0.8165 0.1755 0.6134 0.6671 12 

Mashreqbank P S C 0.9730 0.5375 1 0.9409 0.8737 1 0.8875 5 

MUFG Bank Ltd 0.9951 0.9794 0.9320 0.9807 0.4486 0.9817 0.8862 6 

Firstrand Bank Ltd 0.9322 0.4616 0.6352 0.9021 0.3860 0.6305 0.6579 13 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

107 

 

Krung Thai Bank Public 
Co. Ltd 

0.7963 1 1 1 1 0.9071 0.9506 2 

American Express Bank 1 0.2443 0.5258 1 1 1 0.7950 11 

 
Appendix -E 
 
Table 5. Average Efficiency score of all banks without segregation (10 years) 
 

DMU IAE PAE DME FCE OBE CRE Composite  
score 

Composite  
rank 

AXIS BANK 0.9632 0.9745 0.9135 0.9985 0.9958 0.9320 0.9629 4 

DCB Bank Limited 0.8725 0.4363 0.6764 0.7869 0.1934 0.6851 0.6084 48 

HDFC Bank 0.9994 0.8694 0.9286 0.9989 0.9334 1.0000 0.9549 5 

ICICI bank Ltd 0.9875 0.9522 0.9897 0.9782 0.9896 0.9776 0.9791 2 

IndusInd Bank 
Limited 

0.9710 0.6729 0.7292 0.9199 0.7726 0.8666 0.8220 17 

Kotak mahindra 
Bank  

0.9797 0.4698 0.7968 0.9729 0.5232 0.9591 0.7836 27 

YES bank 0.9935 0.9335 0.9213 0.9969 0.9741 0.9586 0.9630 3 

Dhanlaxmi bank 0.7995 0.4011 0.6483 0.7059 0.1251 0.6145 0.5491 50 

City Union bank 0.9185 0.8029 0.8317 0.8781 0.3652 0.7850 0.7636 29 

Federal Bank 0.9501 0.7264 0.8084 0.9315 0.4039 0.8883 0.7848 26 

Jammu&Kashmir 
Bank 

0.9849 0.5429 0.8220 0.8851 0.2222 0.8699 0.7212 38 

Karnataka Bank 0.9294 0.6908 0.7781 0.9004 0.4001 0.7810 0.7466 34 

Karur Vysya Bank 0.9548 0.6836 0.7353 0.9059 0.3646 0.8217 0.7443 35 

Lakshmi Vilas 
Bank 

0.9214 0.6361 0.7020 0.8169 0.2514 0.6778 0.6676 43 

Nainital Bank 0.9093 0.6709 0.8312 0.5953 0.1013 0.7409 0.6415 46 

RBL bank ltd 0.8810 0.5598 0.7421 0.8391 0.3074 0.7481 0.6796 42 

South Indian Bank 0.9113 0.6515 0.7327 0.8702 0.2462 0.8715 0.7139 39 

Tamilnad 
Mercantile Bank 

0.9790 0.7116 0.7945 0.8670 0.3243 0.8485 0.7541 32 

Allahabad Bank 0.9488 0.6896 0.7652 0.9226 0.3850 0.7753 0.7478 33 

Andhra Bank 0.9596 0.8159 0.8869 0.9425 0.5114 0.8081 0.8207 18 

Bank of Baroda 0.9118 1.0000 1.0000 0.9471 0.4908 0.8685 0.8697 12 

Bank of India 0.9887 0.8751 0.9058 0.8907 0.4034 0.7899 0.8090 20 

Bank of 
Maharashtra 

0.9312 0.5676 0.7864 0.8845 0.2622 0.7688 0.7001 41 

Canara Bank 0.9770 0.8886 0.8540 0.9211 0.4313 0.8920 0.8273 16 

Corporation Bank 0.9693 0.9785 0.9805 0.9781 0.7143 0.7616 0.8971 8 

Indian Overseas 
Bank 

0.9623 0.6564 0.7347 0.8900 0.3558 0.7565 0.7260 36 

Indian Bank 0.9922 0.6579 0.7638 0.9356 0.2978 0.8790 0.7544 31 

Oriental Bank of 
Commerce 

0.9737 0.8329 0.8446 0.9434 0.3906 0.7503 0.7892 22 

Punjab & Sind 
Bank 

0.9819 0.6107 0.7135 0.8898 0.1816 0.8599 0.7062 40 

Punjab National 
Bank 

0.9946 0.8186 0.8726 0.9307 0.5097 0.8615 0.8313 15 

State Bank of India 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1 

Syndicate bank 0.9811 0.7065 0.8232 0.9249 0.3321 0.8018 0.7616 30 

UCO Bank 0.9828 0.8295 0.8620 0.9203 0.3338 0.7183 0.7744 28 

Union Bank of 
India 

0.9782 0.8383 0.8229 0.9338 0.4175 0.8394 0.8050 21 



Copyright © CC-BY-NC 2020, CRIBFB | IJFB 

 

www.cribfb.com/journal/index.php/ijfb                         Indian Journal of Finance and Banking                             Vol. 4, No. 1; 2020 

108 

 

United Bank of 
India 

0.9400 0.6266 0.7956 0.8810 0.3938 0.6992 0.7227 37 

Standard Chartered 
Bank 

0.9946 0.4601 0.9700 0.9813 0.6425 1.0000 0.8414 14 

Barclays Bank 1.0000 0.9353 0.9098 1.0000 0.5638 0.8416 0.8751 10 

AB bank ltd 0.9862 0.7715 1.0000 1.0000 1.0000 0.8423 0.9333 7 

BNP Paribas 0.9209 0.7404 0.8763 0.9785 0.3510 0.8652 0.7887 24 

Societe Generale 0.8630 0.5250 0.6279 0.8466 0.1692 0.6708 0.6171 47 

Shinhan Bank 0.9017 0.9626 0.9497 0.9441 0.3134 0.8285 0.8167 19 

Bank of Ceylon 1.0000 0.8785 0.7476 1.0000 0.5935 1.0000 0.8699 11 

Abu Dhabi 
Commercial Bank 

0.7312 0.6280 0.8401 0.6630 0.0943 0.6897 0.6077 49 

Credit Agricole 
Corporate Bank 

0.9794 0.7409 0.7992 0.9428 0.4313 0.8400 0.7889 23 

Bank of Bahrain & 
Kuwait Bsc 

0.7717 0.6658 0.8223 0.8157 0.1755 0.6105 0.6436 45 

Mashreqbank P S 
C 

0.9730 0.5292 1.0000 0.9409 0.8737 1.0000 0.8861 9 

MUFG Bank Ltd 0.9819 0.9175 0.8967 0.9742 0.4471 0.9781 0.8659 13 

Firstrand Bank Ltd 0.9314 0.4453 0.6348 0.9021 0.3860 0.6305 0.6550 44 

Krung Thai Bank 
Public Company 
Ltd 

0.7960 1.0000 1.0000 1.0000 1.0000 0.9071 0.9505 6 

American Express 
Bank Ltd. 

1.0000 0.2113 0.5224 1.0000 0.9871 1.0000 0.7868 25 

 
 
 
Copyrights  
Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-
access article distributed under the terms and conditions of the Creative Commons Attribution license 
(http://creativecommons.org/licenses/by/4.0/). 
 


