Performance Evaluation of Banks using Fuzzy AHP and TOPSIS, Case study: State-owned Banks, Partially...

Caspian Journal of Applied Sciences Research, 2(3), pp. 128-138, 2013

Available online at http://www.cjasr.com

ISSN: 2251-9114, ©2012 CJASR

128

Full Length Research Paper

Performance Evaluation of Banks using Fuzzy AHP and TOPSIS, Case study:

State-owned Banks, Partially Private and Private Banks in Iran

Mahrooz Amile1, Maedeh Sedaghat

2,* Morteza Poorhossein

3

1Executive Management Department, Payame Noor University, Babol, Iran

2Young Research club, Sari Branch, Islamic Azad University, Sari, Iran

3Industrial Engineering, Shomal University, Amol, Iran

Corresponding author: Maedeh Sedaghat, Email: [email protected]

Received 10 July 2012; Accepted 01 November 2012

Banks as financial and service institutions have a specific place in each country’s economy since their effective

role in money and wealth circulation. Banks’ constructive and efficient activities can positively effect the

economic sections’ growth and increase of production in each country. The aim of this study is to propose a fuzzy

multi-criteria decision model to evaluate the performance of banks. Each performance evaluation model is a tool

which encloses diverse information for decision makers after being performed. This specific application of these

models contributes in responding the questions existing in decision makers’ minds. In this paper, the affecting

criteria in two financial and non-financial levels were selected and investigated after library studies and using

experts’ viewpoints. Standard questionnaires were designed and distributed among state-owned banks, partially

private and private banks’ experts and high rank managers. 10 branches of each above mentioned banks were

selected as a case study. The data was extracted from questionnaires and the variables were weighted using fuzzy

AHP and ultimately the banks were ranked applying TOPSIS technique. Based on the findings, profitability in

financial level and service quality in non-financial level have gained the greatest importance according to

experts’ view points. In total ranking of management performance, private banks was placed at the first rank

and partially private and private banks were ranked as the second and third, respectively. The results implied

that having acceptable financial performance does not lead to having satisfactory non-financial performance

spontaneously and in today’s competitive environment, managers need to observe both financial and non-

financial performance in order to succeed.

Keywords: Performance evaluation, Multi-criteria decision making, Fuzzy AHP, TOPSIS

1. INTRODUCTION

Banks are considered as the artery of economic life

in each country. Banks, just like any other firm,

need to transform inputs into outputs at an efficient

rate in order to maximize profitability and to

survive under competitive conditions. Regarding to

the key role of banking industry in economical,

social and political development, performance

evaluation of any banking sector on each of its

branches will be an effective factor for society

improvement. Market share increment and stability

and capability in competitive environment depend

on the constant monitoring and policy making

from performance evaluation analysis and also

studying the existing strengths and weaknesses.

Measurement of performance is one of the

most effective methods to be informed about the

status of each company and institution. This

activity assists authorities to take the necessary

actions to be able to survive and thrive and reach

their assigned goals. Banks as the most critical

financial institutions in each country play vital

roles in the economical, social and political

amelioration and this fact highlights the

significance of evaluating banks’ performance.

After the development of privatization trend in

Iran, the government has been attempting to donate

the public institutions to private sectors and in this

trend, banks were not exception. The presence of

private and partially private banks makes the

researchers of the paper to conduct a survey of

investigating these banks and scrutinize their

performance in both financial and non-financial

levels and find out the benefits and drawbacks of

bank privatization. On the other side, measuring

banks’ performance has many advantages; some of

the benefits are as below:

Amile et al.

Performance Evaluation of Banks using Fuzzy AHP and TOPSIS, Case study: State-owned Banks, Partially

Private and Private Banks in Iran

129

o Getting feedback from customers about the

quality of banks’ services both financially and non-

financially and how much they are satisfied with

them.

o Make sure that decisions are made on the

basis of real data not emotions or unreal

assumptions.

o Finding suitable policies which are in

favor of both customers and stakeholders.

o Determining the sections which need to be

improved to meet customers’ needs in more

desirable ways.

o Defining the problematic areas in the

organization and providing proposals to solve these

problems.

o Utilizing benchmarking strategy in order

to positively heighten the institution’s

performance.

o Nowadays, non-financial performance

criteria have been taking more attention as an

emerging asset especially in performance

measurement (Ittner, Lacker, &Rajan, 1997). The

reasons that banks are being attracted toward the

non-financial measures as well as the financial

ones are summarized as follows (Secme et al,

2009):

o The change in cost calculation methods.

o The ever increasing of competition in each

sector especially in banking industry.

o The image in national and international

platforms.

o External demands change and

unpreventable increase in the changes in

information technologies. This paper is organized as follows: a

comprehensive literature review on the

performance measurement of banks through

different methods is given in Section 2. In Section

3, the methodology of the research is presented. In

the next section, the analysis of findings for

performance evaluation in Iran Banking Sector is

discussed. And in the last section, conclusions and

some comparison with previous studies are made.

2. LITRATURE REVIEW

In the literature, there are a large number of studies

realized by different methods to measure banks’

performances and the need for such studies is

increasingly growing. Some of these empirical

studies are presented as follow;

Cinar (2010) provides a decision support

model in order to help the bank selecting the most

appropriate location for a bank’s branch

considering a case study in Turkey using fuzzy

AHP and TOPSIS.

Sun (2010) develops an evaluation model

based on the fuzzy analytic hierarchy process and

the technique for order performance by similarity

to ideal solution, fuzzy TOPSIS, to help the

industrial practitioners for the performance

evaluation in a fuzzy environment. The proposed

method enables decision analysts to better

understand the complete evaluation process and

provide a more accurate, effective, and systematic

decision support tool.

Doumpos and Zopounidis (2010) present a

case study on the implementation of a multicriteria

approach to bank rating. The proposed

methodology is based on the PROMETHEE II

method implemented in an integrated decision

support system. Special emphasis is put on the

sensitivity of the results with regard to the relative

importance of the evaluation criteria and the

parameters of the evaluation process.

Secme et al. (2009) propose a fuzzy multi-

criteria decision model to evaluate the

performances of banks. Fuzzy AHP and TOPSIS

are integrated in the proposed model. The results

show that not only financial performance but also

non-financial performance should be taken into

account in a competitive environment.

Bruce Hoa, Wu (2009), presents a hybrid

approach to conducting performance

measurements for Internet banking by using data

envelopment analysis (DEA) and principal

components analysis (PCA).

Sufian (2009) investigates the efficiency of

Malaysian banking sector around the Asian

financial crisis 1997 using the Data Envelopment

Analysis (DEA) approach. To examine the

robustness of the estimated efficiency scores under

various alternatives and to differentiate how

efficiency scores vary with changes in inputs and

outputs, this study focuses on three major

approaches viz., intermediation approach, value

added approach, and operating approach.

Liu (2009) employs slacks-based efficiency

measures, i.e., considering the slacks in input and

output factors, to measure the performances of 24

commercial banks in Taiwan.

Wu et al. (2009) proposed a Fuzzy Multiple

Criteria Decision Making (FMCDM) approach for

banking performance evaluation based on BSC. In

this research the evaluating performance index are

prioritized based on the four perspectives of a

Balanced Scorecard (BSC), then the three MCDM

analytical tools of SAW, TOPSIS, and VIKOR

were respectively adopted to rank the banking


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performance and improve the gaps with three

banks as an empirical example. The analysis

results highlight the critical aspects of evaluation

criteria as well as the gaps to improve banking

performance for achieving aspired/desired level.

Applying the three mentioned MCDM tools among

three banks, bank C has gotten the first rank. It

indicates that all the ranking results are identical.

However, the final values of the three banks

calculated by SAW and TOPISIS are extremely

close to each other. In this case, the VIKOR

method is found to be a better method of

assessment to clearly discriminate the banking

performance.

Lin et al. (2009) applies particle swarm

optimization (PSO) to obtain suitable parameter

settings for support vector machine (SVM) and

decision tree (DT), and to select a subset of

beneficial features, without reducing the

classification accuracy rate. In order to evaluate the

proposed approaches, dataset collected from

Taiwanese commercial banks are used as source

data. The experimental results showed that the

proposed approaches could obtain a better

parameter setting, reduce unnecessary features, and

improve the accuracy of classification

significantly.

Beccalli (2007) investigates whether

investment in information technology (IT) –

hardware, software and other IT services –

influences the performance of banks using a

sample of 737 European banks over the period

1995–2000.This paper analyses whether IT

investment is reflected in improved performance

(measured using both standard accounting ratios

and cost and alternative profit efficiency

measures).

3. RESEARCH METHODOLOGY

Regarding to our subject essence of research model

and the experts’ viewpoints in Iran’s central bank,

Melli bank, Saderat bank and Parsian bank are

selected as the representatives of state-owned

bank, partially private bank and private bank,

respectively due to their high market share among

other Iranian banks, hence the three mentioned

banks constituted our case study. The experts are

the head masters or high rank managers with at

least 10 years service and Bachelor degree in the

three mentioned banks.

This study compares the financial and non-

financial performances of the selected state-owned

bank, partially private bank and private banks in

Iran. For this aim, fuzzy AHP and TOPSIS

methods are integrated. While fuzzy AHP is used

for determining the weights of main and sub-

criteria, the TOPSIS method is used for evaluating

the performances of the mentioned banks.

For ranking these banks, the managers are

asked to score a point to each criteria using

LIKERT spectrum. The mean of the scores and the

weights gained form fuzzy AHP are used as the

inputs for TOPSIS method for ranking the three

mentioned banks. The financial data is obtained

from each bank’s supervision. In the following,

Fuzzy AHP and TOPSIS techniques are explained

in detail.

3.1. Extent Analysis Method on Fuzzy AHP

Analytic Hierarchy Process (AHP) is one of the

well-known Multi- criteria decision making

techniques that first proposed by Saaty (1980).

Although the classical AHP includes the opinions

of experts and makes a multiple criteria evaluation,

it is not capable of reflecting human’s vague

thoughts. The classical AHP takes into

consideration the definite judgments of decision

makers (Wang & Chen, 2007).Thus the fuzzy set

theory makes the comparison process more flexible

and capable to explain experts’ preferences

(Kahraman et al., 2003). The first study on FAHP

was carried out by Van Laarhoven and Pedrycz

(1983). Chang (1996) proposed the extent analysis

method based on the utilization of triangular fuzzy

numbers for pair-wise comparisons.

In this study, Chang’s extent analysis method

on fuzzy AHP, therefore triangular fuzzy numbers

(TFN) are used. Triangular fuzzy numbers are

represented as l/m, m/u, or (l, m, u) in which l, m

and u refer to, respectively, the lower value, modal

value and upper value.

Let X={x1, x 2, x 3,..., xn}= , G={ g 1, g 2, g 3,...,

gn} be an object set and a goal set respectively.

Then each object is taken and extent analysis for

each goal is performed respectively. Therefore, m

extent analysis values for each object can be

obtained, with the following signs:

Where all are TFNs. The

steps of Chang’s extent analysis can be given as

following: Step 1: The value of fuzzy synthetic extent with

respect to the ith object is defined

Si =

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To obtain , the fuzzy addition

operation of m extent analysis values for a

particular matrix is performed such as:

and to obtain ,the fuzzy

addition operation of ( j=1,2,…,m ) values is

performed such as :

And then inverse of the vector above is computed

, such as:

Step 2: As M1=(l1,m1,u1)and M2 =(l2,m2,u2)are

two triangular fuzzy numbers , the degree of

possibility of

M2 =(l2,m2,u2)≥M1 =(l1,m1,u1)is defined as

V(M2 ≥ M1 ) = [ min( (x) , (y)]

y≥x

(5)

and can be expressed as follows:

V(M2 ≥ M1 ) =hgt (d) =

1 ,if m2 ≥ m1

0 if l1 ≥ u2

if otherwise

(6)

Where d is the ordinate of the highest intersection

point D between and . To compare M

1and M 2, we need both the values of V (M1≥

M2) and V (M2≥ M1).

Step3: The degree possibility for a convex fuzzy

number to be greater than k convex fuzzy M i

(i=1,2,...k) numbers can be defined by

(i=1,2,…,k)

V( M≥ M1 ,…, MK ) = V[(M ≥ M1 ) and

V(M≥ M2)and… and

(M≥ Mk)]= min V(M≥ MI) (7)

Assume that d (A i) =min V (S İ ≥S K)for k =

1,2,...,n ;

k≠i . Then the weight vector is given by

W'= (d'(A1),d '(A2),...d '(An))T (8)

Step 4: Via normalization, the normalized weight

vectors are

W =(d(A 1),d(A 2),...,D(A n ))T where W is

a non fuzzy number.

(9)

o Buyukozkan and Cifci (2012) used a

combined fuzzy AHP and fuzzy TOPSIS for

based strategic analysis of electronic service

quality in healthcare industry.

o Jenab et al. (2012) utilized Fuzzy AHP

for Manufacturing Complexity Analysis.

o Kumar, (2012) applied fuzzy AHP and

TOPSIS methodology to evaluate 3PL in a supply

chain.

o Tao (2012) used Fuzzy AHP Model to

Risk Assessment in the Supply Chain

Management.

o Kabir and Sumi (2012) applied an

integrating Fuzzy AHP with TOPSIS Method for

the Selection of Concrete Production Facility

Location.

o Gurumurthy and Kodali (2012) utilized

Fuzzy AHP for the selection of a methodology to

improve the product development process.

o Sheikhi et al. (2012) used Fuzzy AHP for

Selecting the Native and Non-Native Music

Portfolio in Reducing of Stress.

3.2. TOPSIS Method

The TOPSIS (Technique for Order Preference by

Similarity to Ideal Solution) method was firstly

proposed by Hwang and Yoon (Hwang & Yoon,

1981). According to this technique, the best

alternative would be the one that is the nearest to

the positive ideal solution and the farthest from

the negative ideal solution (Benitez et al., 2007).

The positive ideal solution is a solution that

maximizes the benefit criteria and minimizes the

cost criteria, whereas the negative ideal solution

maximizes the cost criteria and minimizes the

benefit criteria (Wang et al., 2006).

In this study, TOPSIS method is used for

determining the final ranking of the alternatives. Step1: Decision matrix is normalized via Eq.(10):


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Step2: Weighted normalized decision matrix is

formed:

vij = w i * rij j = 1,2,3,...J , i = 1,...,n (11)

Step3: Positive ideal solution (PIS) and negative

ideal solution (NIS) are determined:

minimum values (13)

Step4: The distance of each alternative from PIS

and NIS are calculated

= i=1,2,…,J (14)

= i=1,2,…,J (15)

Step5: The closeness coefficient of each

alternative is calculated

CLi =

Step6: By comparing CC i values, the ranking of

alternatives are determined.

o Tavana and Hatami-Marbini (2011)

utilized Adjusted and modified TOPSIS, AHP and

entropy method for prioritizing five mission

simulators for the human exploration of Mars.

o Yue (2011a) applied Extended TOPSIS

with interval numbers for assessing air quality at

the Asian Olympic Games in Guangzhou.

o Yue (2011b) utilized Extended TOPSIS in

order to recruit an on-line manager for a local

chemical company.

o Chang et al. (2010) used TOPSIS for

Evaluation of the performance of 82 Taiwanese

mutual funds for consecutive 34 months.

o Dagdeviren (2010) used ANP and

modified TOPSIS for personnel selection problem

in manufacturing systems.

o Jahanshahloo, Lotfi, and Izadikhah (2006)

utilized TOPSIS with interval data for comparing

15 bank branches based on financial ratios.

o Shyur (2006) applied ANP and modified

TOPSIS for ranking commercial-off-the-shelf

products by their overall performance in an

electronic company.

4. FINDINGS ANALYSIS

4.1. Performance Evaluation’s Indicators

The general model of the performance evaluation

is illustrated in the hierarchical structure in Fig. 1.

The first level determines the best total

performance which is one of the main goals

Fig. 1: Hierarchical structure of model for total performance evaluation

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133

4.2. Financial Performance Evaluation

Despite the existing of many types of financial

ratios in the evaluation of banks’ performances,

evaluation results can vary according to the

different ratios. A bank indicating a high

performance according to one ratio may have a

very low performance according to another ratio.

Lack of an objective methodology causes mistrust

among bank managers (Secme et al, 2009).

Financial ratios have been grouped as

profitability, return on assets, capital adequacy

ratio, share of attracting sources and demanding

loss ratio.

The hierarchical structure in Fig. 2 shows the

financial aspect of the performance evaluation.

At first, the weights of variables are acquired

via fuzzy AHP. For obtaining the value of each

criterion, the Chang Extent Analysis is applied.

The final weights of financial criteria are

presented in table 1. After obtaining the weights through AHP, the

banks are ranked using TOPSIS. Norm

normalization is applied for normalizing financial

amounts for each bank as presented in table 2.

Fig. 2: Hierarchical structure of model for financial performance evaluation

Table 1: The final weights of financial criteria

iw

iwwi

Capital adequacy ratio 0.122

Demanding loss ratio 0.148

Return on assets 0.188***

Share of attracting sources

Profitability

0.248**

0.294*

Table 2: Norm normalization

Capital

adequacy

ratio

Demanding

loss ratio

Return on

assets

Share of

attracting

sources

Profitability Normalization

Melli 3 9.14 11 25.4 20.75 784.0 0.573 0.833 0.612 0.625

Saderat 3.6 10.2 2.7 22.8 6.4 0.584 0.639 0.204 0.549 0.193

Parsian 4 8.16 6.8 23.6 25.1 0.649 0.512 0.515 0.569 0.756

Balanced matrix and determination of the

closeness (CL*) to an ideal solution and ranking

of banks in terms of financial performance are

shown in table 3. The bank with greater CLi enjoys better

performance. According to table 3, Melli bank is

placed on the first rank and Parsian and Saderat

are ranked as the second and third, respectively. 4.3. Non-financial Performance Evaluation

Evaluating financial performances only reflects an

organization’s economic aspect. However, the

other issue that is as significant as a bank’s

economic performance is the evaluation of non-

financial performance (Secme et al., 2009). The calculation steps are the same as in

financial performance evaluation. A sample

calculation for Pricing is given in Table 4.


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Table 3: Ranking of banks in financial performance

Weighted

normalization

cli Rank

Melli 787.0 787.. 0.157 781.0 0.184 0.798 1

Saderat 78701 0.095 0.038 781.0 0.057 0.055 3

Parsian 0.079 78700 78700 0.141 0.222 0.745 2

Fig. 3: Hierarchical structure of model for non-financial performance

Table 4: Total value of the non-financial main criterion pricing for each bank

Interest

rate

Bank

loan

rate

Deposit

interest

rate

Normalization Weighted

Normalization

Total

value

Melli 08. .84 .80 78.04 7800. 7801. 78144 7801. 78007 7801. Saderat .84 08. .80 780.4 78.40 78..0 7810. 78100 7800. 78.00 Parsian 08. 080 .8. 7840. 78.7. 78..0 7811. 7810. 78040 78.0.

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Table 5:Non-financial criteria values

Main Criteria Sub criteria Total value of non-financial criteria

Melli Saderat Parsian

Staff

(0.149)

Physical evidence

(0.067)

Promotion

(0.085)

Availability to customers (0.018)

Accountability to customers(0.134)

Service expressing ability(0.190)

Being confidant(0.257)

Work efficiency(0.212)Politeness and good

behavior(0.190)

Design Elements of Floor, wall and

Ceiling (Colors, Pattern, and

Material) (0.184)

Complex factors of furniture(0.133)

Equipment(0.325)

Lighting(0.160)

Style(0.197)

Advertising(0.218)

Promotional mix(0.214)

Public relations(0.283)

Direct marketing(0.286)

0.579 0.548 0.602

0.589 0.510 0.624

0.573 0.519 0.626

Service quality

(0.229)

Operation process

(0.164)

Electronic channels

(0.103)

Physical situation

(0.075)

Sensibility(0.223)

Trust(0.258)

Empathy(0.229)

Reliability(0.290)

Replacement of lost ATM(0.110)

Opening of different accounts(0.392)

Giving different liabilities(0.499)

Internet banking(0.395)

Telephone banking(0.131)

ATM(0.474)

The place of branch(0.722)

The number of branches(0.278)

0.576 0.551 0.598

0.528 0.568 0.632

0.569 0.548 0.608

0.533 0.572 0.623

For acquiring the ranking of the three

mentioned banks in non-financial performance,

the values of each criterion must be calculated and

multiplied by total weights and then the positive

and negative ideal points are estimated. CLi of

each bank is computed. The bank with greater CLi

enjoys better performance. According to table 6,

Parsian bank is placed on the first rank and Melli

and Saderat are ranked as the second and third,

respectively.

4.5. Total Performance Evaluation

Finally, banks’ total performance is calculated.

Total financial and non-financial values for each

bank are obtained by aggregating the total values

of their main criteria. In the calculation, financial

and non-financial total values are multiplied by

their weights acquired from FAHP. Then, the

positive and negative ideal points are calculated

and the CLi of each bank is gained. The bank with

greater CLi has better performance. According to

table8, Parsian bank is placed on the first rank and

Melli and Saderat are ranked as the second and

third, respectively.


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5. DISCUSSION

In this section, some comparisons are made in

which the differences and similarities between

this study and the previous ones.

Cinar (2010) did a survey about selecting the best

model for the location of bank branches. In this

study, there were 5 major criteria and 21 sub-

criteria that reflect the mission and strategy of the

organization. Fuzzy AHP was used for

determining weights and TOPSIS was applied to

rank the candidate cities while in the present

study, physical position was considered as one of

the main criteria in non-financial performance

which was also divided in to two sub-criteria

which are the branch location and the number of

branches. In this paper, fuzzy AHP was used for

acquiring weights and TOPSIS was applied for

ranking the main criteria of non-financial

performance. The obtained results from the two

researches have shown that banks need to

recognize meaningful variables for selecting their

location according to banks’ strategy and mission.

Table 6: Rankings of banks in terms of non-financial performance

No. cli Rank

Melli 0.433 2

Saderat 0.294 3

Parsian 0.696 1

Table 7: Financial and non-financial total values

Parsian Saderat Melli Wj Total performance

0.745 0.055 0.798 0.479 Financial performance

0.696 0.294 0.433 0.521 Non-financial performance

Table 8: Rankings of banks in terms of financial, non-financial and total performances by the TOPSIS method

No. cli Rank

Melli 0.568 2

Saderat 0.226 3

Parsian 0.780 1

Sun (2010) also integrated FAHP and

FTOPSIS for performance evaluation of four

companies. The results illustrated that two

dimensions of supply chain ability and production

ability had the greatest importance for the

companies and human resource gained the third

priority while in the present paper, profitability in

financial and service quality in non-financial

obtained the first priority.

Secme et al. (2009) investigated the financial

and non-financial performance of banks using

FAHP and TOPSIS. Their findings showed that

non-financial performance has great significance

and it can even cover the weak financial

performance. Their findings are in line with the

results of this paper.

Hung Yi Wu et al. (2009) used FAHP and

three other MCDM techniques for banks

performance evaluation based on balanced score

card. In financial dimension, return on assets was

announced as the first priority and profit per

share, operating revenues, return on investment,

net profit margin and debt ratio were obtained the

next priority, respectively. In this study,

profitability, share of attracting resources, return

on assets, demanding loss ratio and capital

adequacy ratio have acquired the first to last

priority, respectively.

Doumpos and Zopounidis (2010) ranked the

banks using PROMETHEE II while in the present

paper; TOPSIS was applied for ranking banks.

The results of both studies are used for

understanding the weaknesses and strengths and

for bank performance evaluation.

6. CONCLUSION

In financial institutes, especially in banking

activities, management’s need for performance

measurement has been increased due to the

globalization. This study set out to determine the

banking system status after the presence of private

and partially private banks in Iran and make a

comparison between their financial, non-financial

and total performances utilizing a fuzzy model to

Amile et al.



137

find out how effective and productive their

performances are using suitable variables.

FAHP method is utilized to determine the

weights of the main and sub-criteria of the

performance evaluation hierarchy. The TOPSIS

method is used for ranking of banks in terms of

their financial, non-financial and total

performances. In addition to financial criteria,

non-financial performance criteria have been

evaluated for three mentioned banks.

According to the results, Parsian bank has

placed after Melli bank in terms of financial

performance, but regarding to its first rank in

terms of non-financial performance, Parsian has

the first rank in terms of total performance. Due to

the second rank of Melli bank in terms of non-

financial performance, it has the second rank in

terms of total performance. Saderat has the third

rank in terms of financial, non-financial and total

performance due to its weak performance in terms

of financial and non-financial performance.

The considerable point in total performance is

that the obvious difference between financial and

non-financial performance is the determinant

point in total performance. It means that having a

good performance especially in terms of financial

performance does not guarantee having the best

total performance. In the comparisons, non-

financial performance is found to be more

important than financial performance by the

decision makers regarding the fierce competitive

environment.

There were also some limitations in

conducting this research which are briefly

mentioned in the following; lack of an

information center about banks’ performance

status, lack of a good statistic and bank data

center available for researchers, and weak

cooperation culture in some banks.

Further research should be done to rank the

institutions and companies as alternatives using

SAW, VIKOR and ELECTRE and compare the

obtained finding with the present studies.

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Performance Evaluation of Banks using Fuzzy AHP and TOPSIS, Case study: State-owned Banks, Partially...

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