Efficiency Analysis of Commercial Banks using the Data Envelopment (DEA) Model
Muhammad Akbar Saeed1, Faisal Afzal Siddiqui2, and Hashmat Ali3
1Senior Associate Professor, Department of Management Science, Bahria University, Karachi, Mobile: +92 332 221 8514, Email: m.akbar.saeed@gmail.com
2Head Research Department, Business Research Consultants, Karachi, Contact: +92 300 9297089 Email:
brc.khi@gmail.com
3PhD Scholar, Bahria University, Karachi,Contact: +92 312 170 5558, Email: hashmatkhanpn@gmail.com
Abstract: Efficiency analysis of organizations is a cardinal activity both in theory and practice. Commercial banks play a vital role in the economy of a country. Their efficiency is eventually transmitted to an efficient mechanism of allocation of scarce financial resources to the business and industry. Evaluation of bank’s efficiency is an on-going activity performed by all the stakeholders such as the management of the bank itself, the central bank as the main regulator, the rating agencies, and the financial market analysts working for different investment and banking institutions. Bank’s efficiency is usually gauged with conventional methods of financial ratio analysis. This research paper undertakes the efficiency analysis of Pakistan’s commercial banks on a particular methodology called “Data Envelopment Analysis” (DEA). The model has been successfully employed for assessing the relative performance of a set of firms that use a variety of identical inputs to produce a variety of identical outputs. The type of firm may include; manufacturing units, departments of big organizations such as universities, schools, banks, bank branches, hospitals, power plants, police stations, tax offices, prisons, defense bases, and even practicing individuals. The bank efficiency is first analyzed on the basis of single factors and then on the basis of multiple factors. The DEA model employs non-parametric methodology to gauge the efficiency of an organization. All of the 26 Pakistani commercial banks, which are registered with the State Bank of Pakistan, have been included in the study. The analysis is based on data obtained from the published annual reports of the commercial banks for the year ended 31 December, 2015. As per the findings, the top five commercial banks exhibiting the highest level of relative efficiency are; Standard Chartered, Habib Metropolitan, HBL, MCB, and UBL..
Keywords: Banks, Efficiency Analysis, Data Envelopment Analysis, Financial Ratios.
Introduction
Efficiency analysis of organizations is a well- liked activity both in theory and practice.
Commercial banks play a vital role in the economy of a country. Their evaluation process is an ongoing activity performed by all the stakeholders such as the management of the bank itself, the central bank as the main regulator, the rating agencies, and the financial market analysts working for different investment and banking institutions. Bank’s efficiency is usually gauged with conventional methods of financial ratio analysis.
This paper undertakes the efficiency analysis of Pakistan’s commercial banks on a particular methodology called “Data Envelopment Analysis”
(DEA). The model has been successfully employed for assessing the relative performance of a set of firms that use a variety of identical inputs to produce a variety of identical outputs. The type of firm may
include; manufacturing units, departments of big organizations such as universities, schools, banks, bank branches, hospitals, power plants, police stations, tax offices, prisons, defense bases, and even practicing individuals.
History of DEA Model
Efficiency measurement and analysis involve both parametric as well as non parametric methods. The DEA model belongs to the latter type. The process of measuring the efficiency of a an organization such as a firm or a public sector agency was first introduced into the Operations Research literature by Charnes, Cooper, and Rhodes (CCR) and was published in the European Journal of Operational Research in 1978 [1].Model developed by CCR was applicable only to scenarios in which technologies characterized by constant returns to scale were predominant. The CCR model was later extended by Banker, Charnes, and Cooper (Management Science, 1984) to cater
technologies demonstrating variable returns to scale [2].
In succeeding years, endeavors by a huge number of researchers accumulated into a momentous volume of literature around CCR-BCC models, and the standard approach of DEA emerged as a valid alternative to regression analysis for efficiency measurement.
Simultaneous improvement in computer software for unraveling tricky problems in DEA has made it more appealing for use in practical applications.
The DEA Model
Business organizations invariably require multiple inputs. In most cases, there are multiple outputs as well. Application of the DEA model results in measurement of relative efficiencies of organizations with multiple inputs and multiple outputs. In DEA terminology, the organizations are referred to as
“decision-making units or DMUs”. Analysis undertaken through DEA results in allocation of weights to the inputs and outputs of a DMU, based on the best possible efficiency.
Further investigation leads to weighting of the relative importance of the inputs and outputs. These weights tend to reflect the emphasis that appears to have been placed on them for that particular DMU.
The process then allocates all the other DMUs the same weights and compares the resulting efficiencies with that for the DMU of focus. The DMU that exhibits the best performance is assigned an efficiency score of 100 percent or unity. For the remaining DMUs, their performance varies between 0 and 100 percent relative to this best performance.
Efficiency: In the DEA model, the basic efficiency measure adopted is the ratio of total outputs to total inputs. More precisely it is stated as:
Efficiency = ratio of weighted sums of the inputs and outputs (>0), written in symbolic form as under:
𝐸 = 𝑤
𝑗 𝑗𝑦
𝑗𝑤
𝑗 𝑗𝑦
𝑗Where;
E = Efficiency w = weight Y = output X = input
Single Input – Single Output: The DEA model builds on the multiplicity of outputs and inputs. The
simplest of the model is the single input and single out put model. The following numerical and graphical illustration explains the situation. For example in a certain business sector there are seven organizations or DMUs which each have one input and one output corresponding or representing “X”
and “Y” measurement on a graph:
L1 = (2,2), L2 = (3,5), L3 = (6,7), L4 = (9,8), L5
= (5,3), L6 = (4,1), L7 = (10,7).
When plotted the result is shown in the following graph.
DEA identifies the units in the comparison set which lie at the top and to the left, as represented by L1, L2, L3, and L4. These units are called the efficient units, and the line connecting them is called the "envelopment surface" because it envelops all the cases.
DMUs L5 through L7 are not on the envelopment surface and thus are evaluated as inefficient by the DEA analysis. There are two ways to explain their weakness. One is to say that, for example, L5 could perhaps produce as much output as it does, but with less input (comparing with L1 and L2); the other is to say it could produce more output with the same input (comparing with L2 and L3).
Two Inputs and Single Output-The two inputs and one output is a simple DEA model. For example the two inputs could be the number of employees and the floor space of supermarkets and the single output could be the sales. However the output is unitized to 1 under the constant-returns-to-scale assumption.
Hence, input values are normalized to values for getting 1 unit of sales. This is achieved by dividing the first output by sales and then again by dividing the second output by sales. Thus the two inputs then become Employees/Sales and Floor Space/Sales. The data is plotted against the unitized axes.
From the efficiency point of view, it is natural to judge stores which use less inputs to get one unit output as more efficient. Thus an efficiency frontier is constructed which is convex to the origin. The stores which lie on the frontier, or are close to the origin are considered more efficient than stores which lie farther away from the origin and the frontier.
This report based on the DEA model uses the two inputs and single output approach. The two inputs are the employees and the number of branches of the banks. Whereas the single output is the net profit of the respective bank.
Single Input and Two Outputs-The two outputs and single input model also results in an efficiency frontier, but concave to the origin. The two axes are obtained by: output-1/input and output-2/input. The data points lying on the frontier are considered efficient. The observations inside the frontier are considered inefficient. This analysis is similar to the production possibility frontier (PPF) analysis undertaken in economics courses.
Fixed and Variable Weights
DEA analysis deals with following phenomena:
1. single input – single output 2. two inputs – single output 3. single input – two outputs 4. multiple inputs – multiple outputs The first three methods were explained in the earlier section. They also make use of simple graphs to clarify the matters. The fourth category involving multiple inputs and multiple outputs is resolved using variable weights which are derived from the data. The analysis makes use of more complex mathematics and is therefore omitted.
Literature Review
A large number of empirical studies have been conducted about factors influencing bank performance or determinants of bank performance.
However, most of these studies examine developed economies, with far fewer studies examining emerging economies such as Pakistan.
Application of DEA was carried out to determine the relative efficiency of bank branches in Greece [3].A number of different inputs and outputs were identified and applied to a set of twenty branches.
The results identified nine branches to have a
efficiency score of 1.0 and the rest eleven branches were considered to be less efficient.
The Bank of Cyprus which is largest commercial bank in Cyprus commissioned an efficiency study of its branches. The DEA framework was applied on 140 branches. The analysis revealed that the tourist branches were more efficient than the urban branches during the peak tourist season. [4]
Banks in Finland and in United Kingdom were surveyed to develop a DEA frame work to undertake their efficiency analysis [5]. For the British banks, direct staff costs were considered to be the input whereas the outputs included the number of;
mortgage applications, insurance policies sold, new savings accounts, and transactions. For the Finnish banks, the inputs were the numbers of; human tellers, computer terminals, branches, and ATMs. Whereas the outputs were taken as the numbers of;
transactions processed by human tellers, cash withdrawals, loans processed, transactions on ATMs.
Greek banking sector has been explored for assessment of efficiency analysis for the time period 1997-1999 using DEA approach [6]. The author has used financial efficiency ratios to complement the DEA efficiency frontier studies. The study finds that the higher the size of the bank in terms of totals assets, the higher the efficiency. The research also finds that there is wide variation in performance, and efficiency is greater with reduction in the number of small banks due to mergers.
Application of the conventional Data Envelopment Analysis for banking has been based on conventional banking inputs and outputs.
Inclusion of Customer Service Quality for DEA analytical framework has been undertaken wherein service quality inputs and outputs were identified. [7]
The authors surveyed bank customers to assess standards of service quality, but the factors considered included the following inputs; clerical personnel, managerial personnel, computer terminals, working space, number of savings accounts, number of business accounts, and credit application accounts. The factors constituting the model’s output were with reference to service quality perceptions of the branches of the bank. With the consultation of bank’s management, the researchers employed the famous system called SERVQUAL as the main instrument for measurement of service quality. The paper’s findings indicate that efficiency evaluation on the basis of service quality alone would not be conclusive and it would need to be undertaken in conjunction with measures of main inputs and outputs of the bank.
Selected branches of a large American bank have been studied by Golany for the period 1992-93 using data envelopment technique [8]. The formulation of the model was based on five inputs and four outputs.
182 branches were studied for six quarters. The results identified 92 branches as fully efficient and some branches as less than 70% efficient. Innovative decision support tools were used to construct what-if scenarios to examine marginal changes in the output profile of branches.
Cost efficiency analysis of Australian banks was performed by Joshua for the period 1995 to 2002 [9].
The paper has developed two models of efficiency.
First pertains to banking service efficiency and the second covers profit efficiency. The inputs for the first model include; number of employees, property plant and equipment, and interest bearing liabilities.
Outputs for the first model include interest bearing assets and non-interest income. For the second model, the inputs were the same as in first model, whereas the outputs were profit before tax and abnormal items. The study finds improvement in efficiency of major banks in production of banking services and profit. The regional banks did not exhibit the same and encountered a decline in efficiency of producing profits. The relationship between efficiency changes and stock returns were also investigated it was found that stock returns reflect the efficiency of the banking institutions.
Assessment of performance through Data Envelopment Analysis for Portuguese bank branches was carried out by Camanho and Dyson [10]. The bank branches studied were 168. The Inputs included; number of employees in the branch, floor space, operational costs, number of ATMs. The outputs were; number of general banking transactions performed by branch staff, number of transactions in ATMs, Number of all types of accounts at the branch, value of savings, value of loans. The research paper developed efficiency profitability matrix the application of which highlighted that significant scale inefficiencies existed in most bank branches mainly due to increasing returns to scale.
Egypt introduced financial reforms in 1992. This research paper studied the impact of the reforms during the period 1992-2007 on competitiveness and efficiency of the banking sector of the country. The short term and long term impact on economic growth was also evaluated. Different impact assessment models were employed to draw conclusions, including the use of Data Envelopment Analysis for efficiency assessment. The conventional banking
inputs and outputs were utilized in this context. The state owned banks were found to be less efficient than the private sector banks. The study found relationship of significant nature between financial bank productive efficiency and economic growth in the short run than in the long run[11].
In Summary it can be concluded that a large number of conventional banking inputs and outputs have been widely used to gauge bank efficiency. The different factors include; equity to total assets, liquid assets to assets, total loans to total deposits, fixed assets to total assets, total borrowed funds to total assets, reserves for loans to total assets, market concentration, market size, labor productivity, bank portfolio composition, capital productivity, capitalization, per capita GDP, the cost to-income ratio and customer satisfaction.
Pakistan's Banking Sector
Pakistan’s banking sector is currently witnessing a period of consolidation. Because of the mergers, the number of banks is shrinking. The banks can be grouped in the following three categories:
Government sector, private sector, and foreign banks. The few foreign banks have a small market share in Pakistan and have been excluded from the study. The list of banks along with their symbol names as on 31 December, 2016 extracted from SBP’s website is as under:
Government Sector
1. Bank of Khyber BOK
2. Bank of Punjab BOP
3. Bank of Sindh BOS
4. First Women Bank FWBL
5. National Bank of Pakistan NBP Private Sector
1. Allied Bank ABL
2. Askari Bank ASKB
3. Al-Baraka Bank BARK
4. Bank Al-Falah ALFH
5. Bank Al-Habib BAHL
6. Bank Islami BISL
7. Burj Bank BURJ
8. Dubai Islamic DIBL
9. Faysal Bank FYSB
10. Habib Bank HBL
11. Habib Metropolitan Bank HMBL
12. J. S. Bank JSB
13. MCB Bank MCB
14. Meezan Bank MEZB
15. NIB Bank NIB
16. Samba Bank SAMB
17. Silk bank SILK
18. Soneri Bank SONR
19. Summit Bank SUMT 20. Standard Chartered SCBL
21. United Bank UBL
Analysis of Efficiency based on Financial Performance
The first efficiency analysis undertaken on the basis of the most used financial ratio “ROA” (Return on Assets) is presented in Table-1. The table also provides all of the data used in this study. The list is sorted in order of the highest ROA to the lowest ROA. The simple financial ratio efficiency analysis based on ROA reveals that although HBL is the largest bank in terms of assets and net profit, yet it is not the most efficient. The top three most efficient banks in this context are MCB, Standard Chartered, and UBL. HBL’s position is fourth followed by Habib Metropolitan in the fifth position.
The second efficiency analysis is based on bank’s profitability per employee. Table-2 shows the data of all of the banks sorted on the basis of highest to the lowest net profit per employee. As can be seen MCB has slipped from first to third position and Standard Chartered has moved from second to first position as it is generating an amount of Rs.2.483 million in net profit per employee which is the highest amongst the listed banks. HBL is in the second position, followed by MCB, UBL and Habib Metropolitan Bank.
The third efficiency analysis is based on bank’s profitability per branch. Table-3 shows the data of all of the banks sorted on the basis of highest to the lowest net profit per branch. As can be seen Standard Chartered is in the top position as it is generating an amount of Rs.93.376 million in net profit per branch, which is the highest amongst the listed banks.
HMBL is in the second position, followed by HBL in third, UBL in fourth, and MCB in the fifth position.
The fourth dimension of simple efficiency analysis is undertaken in terms of number of employees per branch. Table-4 shows the data of the banks sorted in order of lowest number of employees per branch to the highest number of employees per branch. Sindh Bank is in the top position, followed HBL, MCB, ABL and NBP in the fifth position.
Surprisingly, Standard Chartered is the heaviest in terms of number of employees per branch. Does this indicate that Standard Chartered is inefficient? This question will be answered as the analysis proceeds further.
Application of the Data Envelopment Analysis (DEA) Model
Which number is most crucial to the overall efficiency of a bank, the number of employees or the number of branches? Which bank is most efficient in terms of both number of employees and number of branches? The answer is provided by the DEA model. In this case the model is based on the inverse of the two ratios. Instead of Net Profit/Employee, we take its inverse that is Employees/Net Profit, and instead of Net Profit/Branch, we take Branches/Net Profit. The inverse of the two ratios is taken to facilitate the build-up of the model which requires that the denominator be unitized. Table-5 shows the inverse computed ratios. The sorting is in order of ROA. The Employee/Net Profit ratio is denoted by the symbol “X”, whereas Branch/Net Profit ratio is denoted by the symbol “Y”. The X and Y symbols have been adopted to facilitate the graphical analysis based on the DEA Model.
The graphical presentation of the data in Table-5 is shown in Figure-1. The x-axis is for the ratio Number of Employees / Net Profit, whereas the y- axis is for the ratio Number of Branches / Net Profit.
The Data Envelopment Frontier is the imaginary curved line convex to the origin. The frontier passes through the data point closest to the origin, representing the commercial bank exhibiting the highest relative efficiency, which in this case is Standard Chartered. The second position is for HMBL, followed by HBL, MCB and UBL in third, fourth and fifth position respectively.
As per the DEA analysis in Figure-1, the list of the commercial banks in order of highest relative efficiency to the lowest is as under:
1. Standard Chartered Bank 2. Habib Metropolitan Bank 3. Habib Bank
4. MCB Bank 5. United Bank 6. Allied Bank
7. National Bank of Pakistan 8. NIB Bank
9. Bank Al Habib 10. Faysal Bank 11. J. S. Bank 12. Bank of Khyber 13. Askari Bank 14. Bank Al-Falah 15. Bank of Punjab 16. Samba Bank 17. Soneri Bank 18. Meezan Bank
19. Sindh Bank 20. Dubai Islamic Bank 21. Al Baraka
22. First Women Bank 23. Summit Bank 24. Bank Islami 25. Burj Bank 26. Silk Bank
Conclusion and Recommendations
Efficiency analysis of commercial banks has been conducted in this research report using the Data Envelopment (DEA) Model. The analysis is based on the data extracted from bank’s published annual reports for the year ended December 31, 2015. The DEA model is a non-parametric model. It displays the relative efficiencies of the organizations being analyzed for one particular time period. The graph also shows the comparative position of the institutions for the level of efficiency in comparison with each other. Of the 26 Pakistani commercial banks, Standard Chartered appears to exhibit the highest relative efficiency, followed by HMBL, HBL, MCB, and UBL in the second, third, fourth and fifth positions.
Data Envelopment Analysis (DEA) has been recognized as a valuable analytical research instrument and a practical decision support tool. It can be used in undertaking extensive efficiency studies within one organization having many individual units. In case of Pakistani Banks some of whom have branches in thousands, such an efficiency study based on multiple inputs and multiple out puts can lead to refinement in the identification process of most and least efficient branches. The findings of such a study may invoke more rational strategic decision making for the top management of the banks.
References
1. Charnes, A., W. W. Cooper and E. Rhodes.
“Measuring Efficiency of Decision Making Units,” European Journal of Operational Research Vol. 1 (1978)
2. Banker, Rajiv D., A. Charnes and W. W.
Cooper. “Models for Estimating Technical and Scale Efficiencies,” Management Science, Vol. 30, (1984)
3. Giokas, M. V. (1990). A Study of the relative Efficiency of Bank Branches: An Application of DEA. The Journal of Operational Reserach Society, 591-597
4. Zenios, C. V. (1999). Banchmarks of the Efficiency of Bank Branches. Interfaces, 37-51.
5. Thanassoulis, E. (1999). Data Envelopment Analysis and its Use in Banking. Interfaces , 1-13.
6. George E. Halkos, D. S. (2004). Efficiency Measurement of the Greek Commercial Banks with the use of Financial Ratios: A Data Envelopment Analysis Approach.
Management Accounting Research.
7. Andreas C. Soteriou, Y. S. (2000). An internal customer service quality data envelopment analysis model for bank branches. International Journal of Bank Marketing , 246-252.
8. B. Golany, J. E. (1999). A Data Envelopment Analysis of the Operational Efficiency of Bank Branches . Interfaces, 14-26.
9. Joshua Kirkwood, D. N. (2006). Australian Banking Efficiency and its Relation to Stock Returns. The Economic Record, 253- 267.
10. Camanho, A. S., & Dyson, R. G. (1999).
Efficiency, Size, Benchmarks and Targets for Bank Branches: An Application of Data Envelopment Analysis. Journal of Operational Research Society, 903-915.
11. Poshakwale, S. S., &Qian, B. (2011).
Competitiveness and Efficiency of the Banking Sector and Economic Growth in Egypt. African Development Review, 99- 120.
Table 1: List of Banks Sorted in order of Return on Assets (ROA) BANK ROA ASSETS * NET PROFIT * EMPLOYEES BRANCHES
1 MCB 2.45% 1,020,980 25,035 12092 1247
2 SCBL 2.07% 455,992 9,431 3798 101
3 UBL 1.82% 1,486,187 27,010 14623 1311
4 HBL 1.58% 2,218,423 35,102 15060 1663
5 HMBL 1.57% 489,886 7,673 4277 237
6 ABL 1.54% 992,739 15,314 10244 1048
7 NBP 1.17% 1,711,874 20,077 15548 1406
8 BOK 1.15% 155,159 1,789 2448 131
9 BAHL 1.15% 640,024 7,332 9391 420
10 JSB 1.12% 220,807 2,465 2946 243
11 NIB 1.04% 245,043 2,551 2678 171
12 BOP 1.00% 472,283 4,718 6739 405
13 FYSB 0.98% 430,073 4,222 5357 281
14 SBL 0.97% 128,104 1,245 1985 242
15 MEZB 0.94% 531,850 5,023 8581 551
16 ASKB 0.92% 536,189 4,944 6781 391
17 SONR 0.87% 253,342 2,213 3676 266
18 ALFH 0.83% 903,416 7,514 10280 630
19 SAMB 0.54% 80,166 431 657 34
20 BARK 0.28% 86,933 240 1845 121
21 DIBL 0.27% 157,093 431 2952 200
22 FWBL 0.25% 21,347 53 564 43
23 SUMT 0.13% 188,366 238 2852 191
24 BISL -0.05% 174,549 -87 3537 176
25 BURJ -1.22% 32,736 -400 968 74
26 SILK -1.29% 133,137 -1,712 3153 88
*Assets and Net Profit figures are stated as Rs. in millions Source: Annual Reports of Banks for 2015
Table 2: List of Banks Sorted in order of Net Profit per Employee
BANK ROA Net Profit* per Employee* ASSETS * NET PROFIT * EMPLOYEES BRANCHES
1 SCBL 2.07% 2.483 455,992 9,431 3798 101
2 HBL 1.58% 2.331 2,218,423 35,102 15060 1663
3 MCB 2.45% 2.070 1,020,980 25,035 12092 1247
4 UBL 1.82% 1.847 1,486,187 27,010 14623 1311
5 HMBL 1.57% 1.794 489,886 7,673 4277 237
6 ABL 1.54% 1.495 992,739 15,314 10244 1048
7 NBP 1.17% 1.291 1,711,874 20,077 15548 1406
8 NIB 1.04% 0.952 245,043 2,551 2678 171
9 JSB 1.12% 0.837 220,807 2,465 2946 243
10 FYSB 0.98% 0.788 430,073 4,222 5357 281
11 BAHL 1.15% 0.781 640,024 7,332 9391 420
12 ALFH 0.83% 0.731 903,416 7,514 10280 630
13 BOK 1.15% 0.731 155,159 1,789 2448 131
14 ASKB 0.92% 0.729 536,189 4,944 6781 391
15 BOP 1.00% 0.700 472,283 4,718 6739 405
16 SAMB 0.54% 0.655 80,166 431 657 34
17 SBL 0.97% 0.627 128,104 1,245 1985 242
18 SONR 0.87% 0.602 253,342 2,213 3676 266
19 MEZB 0.94% 0.585 531,850 5,023 8581 551
20 DIBL 0.27% 0.146 157,093 431 2952 200
21 BARK 0.28% 0.130 86,933 240 1845 121
22 FWBL 0.25% 0.095 21,347 53 564 43
23 SUMT 0.13% 0.084 188,366 238 2852 191
24 BISL -0.05% -0.025 174,549 -87 3537 176
25 BURJ -1.22% -0.413 32,736 -400 968 74
26 SILK -1.29% -0.543 133,137 -1,712 3153 88
*Assets and Net Profit figures are stated as Rs. in millions Source: Annual Reports of Banks for 2015
Table 3: List of Banks Sorted in order of Net Profit per branch
BANK ROA NET PROFIT PER BRANCH* ASSETS * NET PROFIT * EMPLOYEES BRANCHES
1 SCBL 2.07% 93.376 455,992 9,431 3798 101
2 HMBL 1.57% 32.378 489,886 7,673 4277 237
3 HBL 1.58% 21.107 2,218,423 35,102 15060 1663
4 UBL 1.82% 20.602 1,486,187 27,010 14623 1311
5 MCB 2.45% 20.076 1,020,980 25,035 12092 1247
6 BAHL 1.15% 17.457 640,024 7,332 9391 420
7 FYSB 0.98% 15.026 430,073 4,222 5357 281
8 NIB 1.04% 14.915 245,043 2,551 2678 171
9 ABL 1.54% 14.613 992,739 15,314 10244 1048
10 NBP 1.17% 14.279 1,711,874 20,077 15548 1406
11 BOK 1.15% 13.658 155,159 1,789 2448 131
12 SAMB 0.54% 12.665 80,166 431 657 34
13 ASKB 0.92% 12.644 536,189 4,944 6781 391
14 ALFH 0.83% 11.928 903,416 7,514 10280 630
15 BOP 1.00% 11.649 472,283 4,718 6739 405
16 JSB 1.12% 10.145 220,807 2,465 2946 243
17 MEZB 0.94% 9.115 531,850 5,023 8581 551
18 SONR 0.87% 8.319 253,342 2,213 3676 266
19 SBL 0.97% 5.147 128,104 1,245 1985 242
20 DIBL 0.27% 2.153 157,093 431 2952 200
21 BARK 0.28% 1.987 86,933 240 1845 121
22 SUMT 0.13% 1.247 188,366 238 2852 191
23 FWBL 0.25% 1.242 21,347 53 564 43
24 BISL -0.05% -0.497 174,549 -87 3537 176
25 BURJ -1.22% -5.400 32,736 -400 968 74
26 SILK -1.29% -19.450 133,137 -1,712 3153 88
*Assets and Net Profit figures are stated as Rs. in millions Source: Annual Reports of Banks for 2015
Table 4: List of Banks Sorted in order of Number of Employees per branch
BANK ROA EMPLOYEES PER BRANCH ASSETS * NET PROFIT * EMPLOYEES BRANCHES
1 SBL 0.97% 8.2 128,104 1,245 1985 242
2 HBL 1.58% 9.1 2,218,423 35,102 15060 1663
3 MCB 2.45% 9.7 1,020,980 25,035 12092 1247
4 ABL 1.54% 9.8 992,739 15,314 10244 1048
5 NBP 1.17% 11.1 1,711,874 20,077 15548 1406
6 UBL 1.82% 11.2 1,486,187 27,010 14623 1311
7 JSB 1.12% 12.1 220,807 2,465 2946 243
8 BURJ -1.22% 13.1 32,736 -400 968 74
9 FWBL 0.25% 13.1 21,347 53 564 43
10 SONR 0.87% 13.8 253,342 2,213 3676 266
11 DIBL 0.27% 14.8 157,093 431 2952 200
12 SUMT 0.13% 14.9 188,366 238 2852 191
13 BARK 0.28% 15.2 86,933 240 1845 121
14 MEZB 0.94% 15.6 531,850 5,023 8581 551
15 NIB 1.04% 15.7 245,043 2,551 2678 171
16 ALFH 0.83% 16.3 903,416 7,514 10280 630
17 BOP 1.00% 16.6 472,283 4,718 6739 405
18 ASKB 0.92% 17.3 536,189 4,944 6781 391
19 HMBL 1.57% 18.0 489,886 7,673 4277 237
20 BOK 1.15% 18.7 155,159 1,789 2448 131
21 FYSB 0.98% 19.1 430,073 4,222 5357 281
22 SAMB 0.54% 19.3 80,166 431 657 34
23 BISL -0.05% 20.1 174,549 -87 3537 176
24 BAHL 1.15% 22.4 640,024 7,332 9391 420
25 SILK -1.29% 35.8 133,137 -1,712 3153 88
26 SCBL 2.07% 37.6 455,992 9,431 3798 101
*Assets and Net Profit figures are stated as Rs. in millions Source: Annual Reports of Banks for 2015
Table 5: List of Banks with Inverse Ratios: Emp/NP& Branches/NP BANK ROA
Net Profit per Employee
ASSETS NET PROFIT
EMPL OYEES
BRANC HES
X (EMP /NP)
Y (Branch /
NP)
1 ABL 1.54% 1.4950 992,739 15,314 10244 1048
0.6689 0.0684
2 ALFH 0.83% 0.7310 903,416 7,514 10280 630
1.3681 0.0838
3 ASKB 0.92% 0.7290 536,189 4,944 6781 391
1.3716 0.0791
4 BAHL 1.15% 0.7810 640,024 7,332 9391 420
1.2808 0.0573
5 BARK 0.28% 0.1300 86,933 240 1845 121
7.6875 0.5042
6 BISL (0.05%) (0.0250) 174,549 (87) 3537 176
(40.6552) (2.0230)
7 BOK 1.15% 0.7310 155,159 1,789 2448 131
1.3684 0.0732
8 BOP 1.00% 0.7000 472,283 4,718 6739 405
1.4284 0.0858
9 BURJ (1.22%) (0.4130) 32,736 (400) 968 74
(2.4200) (0.1850)
10 DIBL 0.27% 0.1460 157,093 431 2952 200
6.8492 0.4640
11 FWBL 0.25% 0.0950 21,347 53 564 43
10.6415 0.8113
12 FYSB 0.98% 0.7880 430,073 4,222 5357 281
1.2688 0.0666
13 HBL 1.58% 2.3310 2,218,423 35,102 15060 1663
0.4290 0.0474
14 HMBL 1.57% 1.7940 489,886 7,673 4277 237
0.5574 0.0309
15 JSB 1.12% 0.8370 220,807 2,465 2946 243
1.1951 0.0986
16 MCB 2.45% 2.0700 1,020,980 25,035 12092 12470.4830 0.0498
17 MEZB 0.94% 0.5850 531,850 5,023 8581 551
1.7083 0.1097
18 NBP 1.17% 1.2910 1,711,874 20,077 15548 1406
0.7744 0.0700
19 NIB 1.04% 0.9520 245,043 2,551 2678 171
1.0498 0.0670
20 SAMB 0.54% 0.6550 80,166 431 657 34
1.5244 0.0789
21 SBL 0.97% 0.6270 128,104 1,245 1985 242
1.5944 0.1944
22 SCBL 2.07% 2.4830 455,992 9,431 3798 101
0.4027 0.0107
23 SILK (1.29%) (0.5430) 133,137 (1,712) 3153 88
(1.8417) (0.0514)
24 SONR 0.87% 0.6020 253,342 2,213 3676 266
1.6611 0.1202
25 SUMT 0.13% 0.0840 188,366 238 2852 191
11.9832 0.8025
26 UBL 1.82% 1.8470 1,486,187 27,010 14623 1311
0.5414 0.0485
*Assets and Net Profit figures are stated as Rs. in millions Source: Annual Reports of Banks for 2015
Figure 1: Data Envelopment Frontier Efficiency Analysis of Banks
