Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
1
Modeling Bank Efficiency with Bad Output and Network Data
Envelopment Analysis Approach
Md. Abul Kalam Azad
Department of Applied Statistics
Faculty of Economics and Administration, University of Malaya 50603 Kuala Lumpur, Malaysia, [email protected]
Kwek Kian Teng (Correspondent author)
Department of Economics
Faculty of Economics and Administration, University of Malaya
50603 Kuala Lumpur, Malaysia, [email protected]
Muzalwana Binti Abdul Talib
Department of Applied Statistics
Faculty of Economics and Administration, University of Malaya
50603 Kuala Lumpur, Malaysia, [email protected]
Abstract
Studies on bank efficiency have often missed bad outputs (e.g., loan loss provision) while calculating efficiency.
This paper examines efficiency of banks in Malaysia by unveiling a dynamic network data envelopment analysis
along with undesired output. This paper applies a three-step network DEA (NDEA) model with Slack based variable
returns to scale approach. Data from all 43 commercial banks in Malaysia are examined over the study period
(2009-2015). Inputs and outputs of the model are selected based on CAMELS rating. The empirical results in this
paper signify that only a few banks in Malaysia have been performing well in converting deposits and equities into
profit as well as minimizing loan loss provisions. Islamic banks in Malaysia have performed better both in
production (converting deposits and equities into earning assets) and profitability (converting loans into net income).
Conventional banks, however, have over scored in intermediation (converting earning assets into loans).
Keywords: Data envelopment analysis; efficiency; network DEA; black box
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
2
1. Introduction
The first application of data envelopment analysis (DEA)- a non-parametric approach of
frontier for benchmarking relative efficiency of banks, was transcribed by Sherman and Gold
(1985). This turned out to be the most interesting research areas within the DEA application in
the last three decades (Liu et al., 2013). Paradi and Zhu (2013) reviewed 225 DEA papers from
1997 to 2010 and have identified that both institutional and branch level of study dominate
majority of the research works. Paradi and Zhu (2013) also stated eight international journals
have published special issues on the application of DEA in banking industry from 1993 to 2009.
They also strongly suggested that the trend of studying DEA in banking would be in boost
aftermath of 2008-2009 world financial crunches. Despite this popularity, applications of DEA in
bank efficiency studies do have some limitations.
One of the most cited criticism of traditional DEA is that DEA technique does not
explore the internal structure of a decision making unit (DMU) while calculating relative
efficiency (Avkiran, 2009, Kao, 2014, Wu et al., 2006). Researchers have named it black box. In
DEA technique, only inputs and outputs are considered. But, what happens within the box was
unknown until network DEA (NDEA) came into existence (Kao, 2014). NDEA uses DEA
technique to measure relative efficiency of a DMU considering how inputs and outputs of that
DMU are linked up within the black box. So, the interdependence of inputs and outputs of a
system is explored using NDEA instead of traditional DEA. Thus, the results form NDEA are
found to be more meaningful and informative (Kao, 2014).
The specific motivation of this paper is threefold. First, the application of DEA in bank
efficiency has been redefined using the NDEA approach. In doing so, we introduced an adaptive
approach which defines the core banking process other than traditional production or profitability
approach. This application of NDEA unveils the black box of traditional banking studies and
provides biased free benchmarking. Next, a complete set of bank data from Malaysian banking
sector has examined using this proposed model. Since, Malaysian banking sector did not affected
substantially after the global crisis in 2008, this data set will be free from sudden shock.
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
3
2. Literature review
Literature survey on NDEA (c.f. table 1) reveals that independent NDEA model is found
to be the basic of most of the studies (Kao, 2014). One criticism against independent NDEA
model is it’s over simplicity (Kao, 2014) which allows other models to come into potential
alternatives. According to Kao (2014), system distance measure, process distance measure,
factor distance measure, ratio-form system efficiency and ratio-form process efficiency are the
main stream NDEA models. However, latest research interest are slacks-based measure, game
theoretic, and value-based (Kao, 2014).
Finally, the application of dynamic NDEA in bank efficiency is limited (Avkiran, 2015,
Kao, 2014). Kao (2014) critically evaluated literature on NDEA application and found that
dynamic NDEA is rare in practice. He also suggests that while application of dynamic NDEA is
available in efficiency literature, no application of Malmquist index NDEA is found.
Thus, the following two major research gaps are revealed from the above literature. First,
to the best of our knowledge, literally no study has combined meta-frontier DEA with double
bootstrap regression in second stage to examine bank efficiency in Malaysian settings. Second,
Empirical evidence on conventional bank efficiency among the developed economies are
saturated. This study will fill the gap by examining comparative performance between 1) Islamic
vs. conventional banks and 2) foreign vs. local banks in developing economies like Malaysia.
3. Methodology
The production possibility set will be as shown in equation 1 in below.
Subject to;
1
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
4
Equation 1 presents K number of divisions i.e., nodes in the proposed network DEA
model by upgrading the earlier production set in equation 3-1. Here, the number
of DMUs is n (j=1,……,n) where be the numbers of inputs and outputs for any node
k respectively. Now for the link between node k to node h be presented as and L
represents the set of links. So, the data set for the input set in node k is
, output set from node k is
,
and the intermediate set for a link between node k and node h is
where is the number of items in the link. Finally, is the
intensity vector which corresponding to node . This is to note that this is a
variable return to scale model (VRS) suitable for explaining banking activities. The last
constraint
is a VRS application.
Now, slack vectors for input (output) within the DMUs can be presented by
,
,
,
Where,
2
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
5
So, if the output-oriented efficiency is denoted with , the linear equation is
Subject to
,
,
,
if link between nodes are free, OR
if link between nodes are fixed,
3
where is the relative weight of node k which
determined corresponding to it importance. So, the overall efficiency score for an output oriented
production set (banking sector in this research) is the weighted harmonic mean of individual
node’s efficiency scores.
4
For an optimal solution of equation 3-12, the projection onto the frontier as follows:
5
For a free type link between the nodes, the projection is as follows:
6
To define a reference set of any node k for the DMUs as follows:
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
6
7
Figure 1: Three stage network DEA
Figure 1 presents the three stage network DEA model we proposed in this paper. As
argued in literature review, during the core operations of commercial banking, bank first
concentrate on capital and deposit (total liability) which allows a bank to determine how much
growth (loan creation) it can afford in long term business (Node 1). From these, a bank produces
earning assets which in turn became loans (Node 2). In the final stage, from these loans bank
creates profit and as a byproduct bank also incurs bad loans (Loan loss provision). As an input
for second and third stages, bank also uses interest expenses and non-interest expenses
respectively. A conceptual presentation is demonstrated in figure 2.
4. Results and analysis
Bank efficiency defines banks’ relative performance- by keeping the best performer as
the benchmark, calculating the distance of below performers. Hence, the scores vary from 0 to 1.
Banks with 1 refer to the best performers within the sample and hence assume that banks with
efficiency 1 are on the frontier. So, performance of all other banks would be enveloped by the
frontier. For our analysis, we first test bank efficiency of 43 commercial banks from Malaysian
context on yearly basis (c.f. table 1). The results reveal that bank efficiency scores have no
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
7
unique pattern. Even with some ups and downs, eight banks are found to have no changes in the
efficiency scores over the period. Efficiency progress over the period is found for 19 banks and
16 banks have found to be regressed during the study period. For instance, Bank of America
Malaysia Berhad and Bank of China (Malaysia) Berhad have found to be progressed over the
time. One significant findings of table 1 is that most of banks’ efficiency scores are found to be
regressed during 2014 and 2015. This could be the result of recent economic slowdown in
Malaysia and partially because of exchange rate fall over this period. However, no specific
pattern is observed for public vs. private or Islamic vs. conventional or local vs. foreign banks.
Among the local banks, only BNP Paribas Malaysia Berhad has found as the unit efficient bank.
(see Table 1)
(see Table 2)
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
8
Table 1: Efficiency estimation of Malaysian banks (2010-2015)
DMU 2010 2011 2012 2013 2014 2015
FC1 0.18308 0.11048 0.24921 0.48309 0.27242 0.12008
FC2 0.12190 0.28047 0.55073 0.04458 0.30260 0.21679
FC3 0.17662 0.04931 0.16088 0.06244 0.08806 0.08273
FC4 0.05237 0.03177 0.11507 0.17038 0.16040 0.10087
FC5 0.13572 0.08712 0.04769 0.05166 0.10804 0.10777
FC6 0.28244 0.25314 0.26853 0.70775 0.72047 0.53933
FC7 0.24123 0.23453 0.17510 0.20170 0.26644 0.25275
FC8 0.81019 0.54933 0.40572 0.38214 0.25758 0.27826
FC9 0.88861 0.34742 0.31010 0.24519 0.33916 0.33039
FC10 0.07985 0.10114 0.07728 0.25126 0.22312 0.12168
FC11 0.03608 0.21599 0.19506 0.24832 0.09595 0.07661
FC12 0.74112 1 0.59918 1 1 1
FC13 0.32471 1 1 1 1 1
FC14 0.21627 0.30561 1 1 0.13171 0.24682
FC15 1 0.06959 1 0.74003 0.64958 0.60548
FC16 0.15978 0.28577 0.47465 0.49343 0.42973 0.34536
FC17 0.21492 0.24184 0.22731 0.37081 0.37805 0.23441
FC18 0.35424 0.37039 0.42708 0.15557 0.22739 0.20096
FC19 0.10280 0.05428 0.21875 0.27214 0.33904 0.12507
FI1 0.11895 0.17004 0.54476 0.67905 0.25748 0.35205
FI2 0.28870 0.31966 0.25858 0.26326 0.32965 0.13404
FI3 0.25130 0.18258 0.13067 0.22139 0.10950 0.08111
FI4 0.31304 0.34154 0.34248 0.43278 0.36357 0.21428
FI5 0.48214 0.76958 1 0.71091 0.71354 0.71274
FI6 0.91521 0.82951 0.67323 0.54174 0.52958 0.49492
LC1 0.01504 0.01756 0.08436 0.02932 0.27526 0.32428
LC2 0.28631 0.26253 0.14527 0.30927 0.43657 0.30701
LC3 0.25403 0.33516 0.33606 0.12247 0.26322 0.19810
LC4 0.20857 0.22059 1 0.15578 0.18587 0.17124
LC5 0.48741 0.55998 0.32271 0.77654 0.19264 0.78389
LC6 0.74772 0.74792 0.61660 0.05534 0.01963 0.08312
LC7 0.14561 0.09019 0.00332 0.15290 0.17993 0.10148
LC8 0.16225 0.17681 0.20755 0.36665 0.34823 0.28522
LI1 0.41324 0.31419 0.68744 0.57583 0.55597 0.25191
LI2 0.36355 0.31999 0.33453 0.49782 0.29584 0.18349
LI3 0.15227 0.19321 0.26070 0.19599 0.28401 0.18242
LI4 0.18329 0.23559 0.17510 0.17783 0.23104 0.16055
LI5 0.21524 0.22996 0.20704 0.41979 0.37347 0.30847
LI6 0.24474 0.47682 0.22929 0.33269 0.16639 0.20679
LI7 0.79784 0.35212 0.64819 1 0.03229 0.39156
LI8 0.02371 0.10707 0.28985 0.49508 0.61622 0.52202
LI9 1 1 0.71355 0.82689 1 1
LI10 0.23112 0.27693 0.15219 0.35874 0.29317 0.24855
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
9
Table 2: Node wise efficiency scores
Node 1
Node 2
Node 3
DMU/Year 2009 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015
FC1 1.371 0.916 0.621 0.852 1.471 0.926 1.371 0.870 0.995 0.414 0.635 1.824 0.776 0.870 0.831 0.543 1.192 0.974 0.468 1.223 0.831
FC2 0.897 1.000 1.000 0.960 2.100 1.073 0.897 0.508 1.000 0.717 1.000 0.517 1.120 0.508 0.588 1.000 0.974 1.208 2.716 0.928 0.588
FC3 0.793 2.501 0.953 1.164 1.021 1.000 0.793 0.912 1.195 1.019 1.422 1.130 1.000 0.912 1.002 1.471 0.743 1.104 1.170 1.000 1.002
FC4 1.216 1.000 1.259 0.970 0.946 1.083 1.216 1.219 1.000 1.722 1.038 1.033 1.337 1.219 0.231 1.000 2.026 0.959 0.100 7.419 0.231
FC5 1.000 1.000 0.987 1.007 0.945 1.095 1.000 1.120 1.000 0.717 1.000 0.517 1.120 1.120 0.195 1.000 0.974 1.208 2.716 0.928 0.195
FC6 1.000 0.890 0.957 0.110 1.368 1.000 1.000 1.000 0.446 0.734 1.404 0.889 1.000 1.000 1.000 0.604 0.887 0.935 0.998 1.000 1.000
FC7 1.646 0.931 1.000 0.960 2.100 1.073 1.646 5.311 0.943 1.000 1.893 0.716 1.322 5.311 5.646 0.434 1.000 2.965 2.392 1.879 5.646
FC8 0.870 0.920 0.825 0.597 0.920 0.581 0.870 0.972 0.860 0.770 1.077 0.698 0.909 0.972 0.982 0.763 1.197 1.056 0.939 1.039 0.982
FC9 0.618 0.969 0.987 0.615 1.103 1.120 0.618 1.738 1.120 1.120 0.401 1.289 0.956 1.738 1.568 1.000 0.948 0.324 1.004 1.486 1.568
FC10 1.000 0.969 0.987 1.007 0.945 1.000 1.000 1.000 0.717 1.000 0.517 1.120 1.000 1.000 1.000 0.974 1.208 2.716 0.928 1.000 1.000
FC11 0.550 1.000 2.317 0.866 1.095 1.095 0.550 1.255 1.000 2.034 2.665 2.008 1.718 1.255 0.773 1.000 0.684 0.592 13.504 2.024 0.773
FC12 0.970 0.776 0.614 6.576 0.974 3.908 0.970 0.663 0.983 1.278 1.258 1.238 1.002 0.663 0.794 1.112 1.638 0.937 1.639 1.393 0.794
FC13 1.100 0.972 1.000 0.076 4.894 1.000 1.100 1.172 0.364 1.000 4.414 1.324 1.000 1.172 1.007 0.670 1.000 0.948 1.786 1.000 1.007
FC14 1.161 1.000 1.005 1.002 0.982 1.000 1.161 1.060 1.000 1.170 0.852 0.699 1.000 1.060 0.920 1.000 0.947 0.885 1.187 1.000 0.920
FC15 0.961 0.969 0.987 1.007 0.945 1.000 0.961 0.923 0.966 0.859 0.986 0.592 1.004 0.923 0.837 0.807 1.067 0.795 0.988 1.007 0.837
FC16 1.100 0.972 1.000 0.076 0.982 1.000 1.100 0.717 1.000 0.517 0.852 0.699 1.120 0.717 1.000 0.948 1.000 2.965 2.392 1.879 1.000
FC17 2.708 0.368 0.899 0.076 4.894 1.000 2.708 0.036 0.945 1.526 0.986 0.592 1.000 0.036 0.715 0.486 0.842 1.000 0.948 1.000 0.715
FC18 1.305 1.000 1.031 0.623 1.112 0.796 1.305 1.196 1.000 1.125 0.620 1.750 0.930 1.196 0.861 1.000 0.514 0.823 0.195 1.000 0.861
FC19 1.000 0.903 0.434 0.613 0.895 0.951 1.000 1.000 0.398 0.491 2.277 0.588 1.077 1.000 1.000 0.769 1.069 1.016 0.985 1.014 1.000
FI1 1.022 1.006 1.030 1.001 1.028 0.969 1.022 0.878 1.261 1.199 0.286 1.108 1.120 0.878 0.641 0.139 3.324 0.220 1.723 1.405 0.641
FI2 2.271 1.111 0.829 0.837 0.658 1.219 2.271 0.705 0.727 0.696 0.690 0.545 0.891 0.705 0.749 0.383 0.998 1.561 1.432 1.135 0.749
FI3 1.071 0.965 1.022 0.305 3.801 1.000 1.071 0.677 0.630 0.925 1.969 0.148 1.000 0.677 0.931 0.850 1.125 1.033 0.966 1.000 0.931
FI4 0.977 0.989 0.957 1.018 0.996 0.922 0.977 0.898 0.912 0.631 3.188 0.549 0.735 0.898 0.963 0.995 0.632 1.605 0.926 1.104 0.963
FI5 1.022 1.006 1.030 1.705 1.006 0.969 1.022 0.717 1.000 0.517 27.323 0.927 0.568 0.717 1.606 1.095 0.987 1.314 0.800 1.146 1.606
FI6 0.806 1.107 0.956 0.999 1.008 0.954 0.806 0.270 2.227 0.314 1.055 0.456 0.914 0.270 1.064 0.719 0.856 0.910 0.310 2.631 1.064
LC1 0.909 0.962 0.994 1.008 0.846 0.975 0.909 0.447 0.386 0.881 2.525 0.329 0.497 0.447 0.374 0.127 1.400 1.490 0.401 1.004 0.374
LC2 0.918 0.933 1.000 1.008 1.002 1.019 0.918 0.871 0.861 1.000 0.810 0.923 0.913 0.871 0.735 0.271 1.000 3.980 0.495 1.238 0.735
LC3 1.032 0.999 0.958 0.993 1.009 1.005 1.032 0.910 0.911 0.789 2.394 1.285 0.796 0.910 0.772 0.822 0.974 1.208 2.716 0.928 0.772
LC4 0.953 0.978 1.017 1.004 0.939 0.991 0.953 1.540 0.543 1.677 0.534 0.642 0.933 1.540 0.740 4.744 0.494 0.987 0.937 0.996 0.740
LC5 0.775 1.000 0.972 0.948 0.959 0.715 0.775 0.701 1.000 0.662 0.650 1.018 0.806 0.701 0.221 1.000 0.911 0.748 0.758 5.880 0.221
LC6 0.790 0.864 0.381 0.204 4.881 0.809 0.790 0.309 0.909 0.952 1.548 1.498 0.970 0.309 0.535 1.066 1.641 1.012 1.031 1.092 0.535
LC7 2.066 1.474 0.883 0.767 1.145 1.595 2.066 1.864 0.912 0.777 1.134 1.040 0.849 1.864 1.606 1.095 0.987 0.993 1.212 1.151 1.606
LC8 1.228 1.079 1.020 0.896 1.014 0.989 1.228 0.812 1.240 1.090 0.223 1.217 0.715 0.812 0.742 0.796 0.836 0.845 1.042 1.057 0.742
LI1 1.080 0.840 0.772 0.984 0.928 0.718 1.080 0.838 0.626 0.530 1.571 0.619 0.227 0.838 0.195 0.654 1.329 0.125 1.128 0.877 0.195
LI2 0.870 0.871 0.955 0.954 1.298 1.193 0.870 0.638 0.723 0.665 0.593 1.620 1.188 0.638 0.759 0.587 0.577 0.807 0.962 1.049 0.759
LI3 1.313 0.971 1.009 0.970 1.014 1.099 1.313 0.656 0.782 1.001 0.567 1.101 1.322 0.656 0.868 0.968 0.941 0.938 1.009 1.004 0.868
LI4 0.864 1.000 0.911 0.995 0.998 0.998 0.864 0.717 1.000 0.517 1.861 0.657 0.762 0.717 1.151 1.000 1.597 0.633 2.302 1.066 1.151
LI5 0.971 0.955 1.000 0.969 0.946 1.019 0.971 0.924 0.603 1.000 0.803 0.217 1.152 0.924 0.495 15.401 1.000 0.992 2.667 0.489 0.495
LI6 0.822 0.976 0.985 1.006 0.819 1.112 0.822 0.464 0.969 0.462 0.582 0.297 0.774 0.464 0.926 1.000 0.619 0.787 0.812 0.937 0.926
LI7 0.539 0.789 0.829 0.777 0.795 0.953 0.539 0.603 0.434 0.336 0.418 0.427 0.727 0.603 0.344 0.416 0.893 0.902 0.784 1.001 0.344
LI8 1.566 0.716 0.983 0.290 1.398 0.771 1.566 0.495 0.457 0.615 0.880 0.901 0.802 0.495 0.335 0.460 1.107 0.971 0.945 1.171 0.335
LI9 0.917 0.758 1.000 0.551 1.264 0.663 0.917 0.926 0.847 1.000 2.811 0.863 0.570 0.926 1.015 1.780 1.000 2.076 1.009 0.654 1.015
LI10 0.917 1.000 1.058 0.671 0.941 1.000 0.917 1.120 1.000 7.703 0.211 0.827 1.000 1.120 0.195 1.000 1.675 0.138 11.252 1.000 0.195
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
10
Table 2 shows the efficiency scores of 43 banks for all node-1, node-2 and node-3 for the
proposed model. There is a number of discrimination in results which signify that though these
banks have been operating in the same region, efficiency of these banks vary from one to
another. In the proposed network model, node-1 explains a bank’s capacity to convert its
liabilities and owners’ equity into earning assets. This proposed model drops non-earning assets
in node-1 since these assets will not help a bank anyway. Results from this table provide a
number of critical points for discussion. Out of total 43 banks, in every year, more than one bank
are always found with unit efficiency score. This particular result clearly signifies that every year
(2009-2015) Malaysia has a few banks which perform at their optimal level and score at the
frontier with unit efficiency.
First, out of 19 foreign conventional banks only one bank, namely FC18: The Royal Bank
of Scotland Berhad, is found to be unit efficient in all the examined years. This result signifies
that The Royal Bank of Scotland Berhad has been in the optimal level of converting its total
source of fund into total earning assets. On yearly basis, 6, 8, 8, 8, 9, 10, 10 and 10 banks were
found in the frontier (unit efficient) in 2010 and on words respectively. Thus, on an average, half
of the total foreign conventional banks were found as the unit efficient unit. A high competition
among the foreign conventional banks, thus, is expected. Over the study period, banks with
higher efficiency levels (more than 90%) are FC2, FC4, FC5, FC10, and FC15 with annual
average efficiency of 97%, 94%, 95%, 98% and 91% respectively. The worse average efficiency
is observed for FC7 and FC8 with 36% and 49% average annual efficiency respectively.
Particularly, FC7 (Deutsche Bank Malaysia Berhad) is consistently performing very inefficient
capacity of converting capital into earning assets. This also means that most of its capital is
remaining as nonearning assets. Interestingly, even though FC7 (Deutsche Bank Malaysia
Berhad) has profit in almost every years, this very poor efficiency score in all the estimated years
signify that comparing to the best performers like FC18: The Royal Bank of Scotland Berhad,
FC7 has the least capacity for converting capital into earning assets.
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
11
Now, from 6 foreign Islamic banks, all banks have annual average efficiency more than
71%. The highest average yearly efficiency is recorded for FI5: OCBC Al-Amin Bank Berhad
with 99.6% and FI6: Standard Chartered Saadiq Berhad with 96.3%. Out of total 6 banks within
this group, at least two banks were consistently found as unit efficient in every year. This is a
clear indication that bank competition is also possible to exist in this group of banks. Also, there
is an indication that Islamic foreign banks are better efficient than that of foreign conventional
banks in Malaysian context
Among the 18 local banks (8 local conventional banks and 10 Local Islamic banks), only
7 banks (3 local conventional and 4 local Islamic) have found with high efficiency scores. In
both groups the least bank performers have recorded with 57% efficiency only. Also, not a bank
was found unit efficient through the study years.
Table 4 also presents the efficiency scores from the node-2 of the proposed SBM-NDEA
model. In this proposed model, banks are specifically assumed to create loans out of their
earning assets (intermediate input) from node-1. In addition, expenses on interest is also
included as input. Liquidity requirement is excluded from this node as expected output. Thus,
examining node-2 explains a fundamental job of a bank: how efficiently bank can create loans
from its earning assets with special attachment of interest expenses for financing the liability.
Among the 19 foreign conventional banks, two banks namely FC3: Bank of China
(Malaysia) Berhad and FC12: Mizuho Bank (Malaysia) Berhad are found to be unit efficient
during the study years. On an average, the better performer (more than 90% efficiency) banks are
FC2: Bank of America Malaysia Berhad, FC13: National Bank of Abu Dhabi Malaysia Berhad
and FC18: The Royal Bank of Scotland Berhad. Interestingly, 7 banks out of total 19 banks are
below 50% efficient during the period. The lowest efficiency score is recorded for FC11: J.P.
Morgan Chase Bank Berhad with only 12%. That also means that banks are highly inefficient in
node 2 for converting the earning assets into loan. As well as, foreign conventional banks have
been keeping lots of liquid assets into their volts. Now, this could be due to legislative reasons,
management incapacity, lack of home ground facility, economic turmoil into their home country
etc.
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
12
In case of foreign Islamic banks, out of 6 banks only FI2: Asian Finance Bank Berhad is
found to be unit efficient throughout the years. Surpassingly, remaining 5 banks’ efficiency are
scored less than 20%. Especially, FI5: is the least efficient bank (11%) for converting earning
assets into loans comparing to the unit efficient bank (FI2) among the foreign Islamic banks.
Over all, average efficiency of foreign Islamic banks is lower than that of foreign conventional
banks.
The efficiency levels of 18 local commercial banks for the year 2010 to 2015 in node-2
are presented in Table 3. Most of banks are found to be less efficient (less than 40%). Among
these banks, only two banks have scored more than 50% namely LC6: Malayan Banking Berhad
and LI1: Affin Islamic Bank Berhad. The least performer among the local banks are LI2: Asian
Finance Bank Berhad with 6.9% and LC2: Alliance Bank Malaysia Berhad with 9.4% efficiency
only. These low efficiency among all type of banks confirm that banks are lagging behind in
converting the earning assets into loans. The production capacity among the banks are somewhat
low. This also signify that compared to the higher efficient banks (FC2, FC13, FC18 and FI2),
remaining 39 banks in Malaysia has less capacity to convert earning assets into loans and
liquidity. Last but not least, this could also be happed that interest expense and liquidity of these
banks are high.
In node-3, among the 18 local banks, only one bank is found to be unit efficient during
the study period, namely LC7: Public Bank Berhad. Majority of the banks are scored efficiency
level between 40% and 80%. The least efficiency performers among these banks are LI1: Affin
Islamic Bank Berhad with only 26.5% efficiency and LI5: Bank Muamalat Malaysia Berhad with
efficiency score of 32.2% only. These results also signify that only a few banks in Malaysian
context have been performing well in converting loans into profit as well as minimizing loan loss
provisions. A summary of earlier result is shown in figure 3 below.
Node 1 Node 2 Node 3
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
13
Figure 3: Comparative analysis of the results of Table 3
5. Conclusions
The overall efficiency scores of the banks as shown above seems however
benchmarked bank efficiency with reasonable explanations. Yet, how the selected inputs and
outputs have worked into the black-box is undefined. The limitations of existing bank efficiency
approaches in explaining banks’ true performance is exhibited by Azad et al. (2016). According
to Azad et al. (2016), bank efficiency examinations applying any of the three traditional
approaches (intermediation, production and profitability) produces biased result. They also
proposed CAMELS rating for selecting bank efficiency variables. But, their paper failed to
explain how these variables are linked to each other. Which is whether all inputs are
simultaneously used to produce all outputs. Of course not! Hence, this paper has applied an
adaptive network DEA model (c.f. figure 2) to explain the overall efficiency of bank efficiency
as well as function specific efficiency. Here, it is to mentioned that bank functions are mainly
threefold. Thus, the three nodes are presented in our proposed model.
A number of issues can be highlighted comparing the average efficiency of four groups
of banks in this study. Average results are seen to have higher in node-1 compared to node 2 and
node 3 in all respects of banks. Node-1 (on the left) presents that on an average local
conventional banks have performed well than that of foreign conventional banks. Similarly, local
Islamic banks have performed higher on an average compared to that of foreign Islamic banks.
The pattern among all four lines are almost similar. Initially during the year of 2010-2011, all
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
14
banks’ average efficiency was rising from a lower level to a higher level. After 2011, for all
groups, it is seen that an average slower efficiency is recorded during 2013 to 2015. In Figure ,
node-2 and node-3 also depict a similar pattern over the study period.
While examining the average performance of selected groups of banks in Malaysian
context for node-2, it is seen that the least average efficiency is recorded for foreign Islamic
banks. Again, the highest average efficiency index is recorded for foreign conventional banks.
Similar to the pattern in node-1, all types of banks have found least efficient in the year 2015.
On an average, only foreign conventional banks are scored efficiency level of 70% during 2013.
Other than this group, all groups’ average efficiency is observed between 20% and 40%.
Nevertheless, this poor performance by all groups signify that Malaysian banks, irrespective of
all groups, are less efficient in converting earning assets into loans.
Node-3 depicts a number of important issues. The ups and downs in the efficiency
scores for local banks are extreme while the growth or decline of foreign banks’ efficiency is
little smooth. This particular issue reflects that this might happen from the direct effect of Master
Plan of Malaysian government for force merger and financial restructuring of local banks.
Whereas foreign banks’ efficiency were moving upward or downward due to its operative
performances not for external influence. Another significant issue of node-3 is the average scores
among the groups. In 2013, the average efficiency of local conventional banks were found to be
almost as 100%. Thus, this provides a clear indication of success in Financial Master Plan
(financial restructuring and forced merger and acquisition) in Malaysian local banking sector.
References
Abdul-Majid, M., Saal, D. S. & Battisti, G. (2010), "Efficiency in Islamic and conventional banking: an international comparison", Journal of Productivity Analysis, 34, 25-43. http://dx.doi.org/10.1007/s11123-009-0165-3
Abdul-Majid, M., Saal, D. S. & Battisti, G. (2011a), "Efficiency and total factor productivity change of Malaysian commercial banks", The Service Industries Journal, 31, 2117-2143. http://dx.doi.org/10.1080/02642069.2010.503882
Abdul-Majid, M., Saal, D. S. & Battisti, G. (2011b), "The impact of Islamic banking on the cost efficiency and productivity change of Malaysian commercial banks", Applied Economics, 43, 2033-2054. http://dx.doi.org/10.1080/00036840902984381
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
15
Ahmad, S. & Rahman, A. R. A. (2012), "The efficiency of Islamic and conventional commercial banks in Malaysia", International Journal of Islamic and Middle Eastern Finance and Management, 5, 241-263. http://dx.doi.org/10.1108/17538391211255223
Akther, S., Fukuyama, H. & Weber, W. L. (2013), "Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking", Omega-International Journal of Management Science, 41, 88-96. http://dx.doi.org/10.1016/j.omega.2011.02.009
Avkiran, N. K. (2009), "Opening the black box of efficiency analysis: An illustration with UAE banks", Omega-International Journal of Management Science, 37, 930-941. http://dx.doi.org/10.1016/j.omega.2008.08.001
Avkiran, N. K. (2015), "An illustration of dynamic network DEA in commercial banking including robustness tests", Omega-International Journal of Management Science, 55, 141-150. http://dx.doi.org/10.1016/j.omega.2014.07.002
Avkiran, N. K. & McCrystal, A. (2012), "Sensitivity analysis of network DEA: NSBM versus NRAM", Applied Mathematics and Computation, 218, 11226-11239. http://dx.doi.org/10.1016/j.amc.2012.05.014
Azad, M. A. K., Munisamy, S., Masum, A. K. M., Saona, P. & Wanke, P. (2016), "Bank efficiency in Malaysia: a use of malmquist meta-frontier analysis", Eurasian Business Review, 03 September 2016, 1-25. http://dx.doi.org/10.1007/s40821-016-0054-4
Chan, S.-G., Koh, E. H. Y., Zainir, F. & Yong, C.-C. (2015), "Market structure, institutional framework and bank efficiency in ASEAN 5", Journal of Economics and Business, 82, 84-112. http://dx.doi.org/10.1016/j.jeconbus.2015.07.002
Charnes, A., Cooper, W., Golany, B., Halek, R., Klopp, G., Schmitz, E. & Thomas, D. 1986. Two phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: Tradeoffs between joint services and army advertising. Austin, Tex, USA: Center for Cybernetic Studies. University of Texas-Austin
Cook, M. (2008). Banking Reform in Southern Asia: The Region's Decisive Decade, London and New York, Routledge.
Cook, W. D., Hababou, M. & Tuenter, H. J. H. (2000), "Multicomponent efficiency measurement and shared inputs in data envelopment analysis: An application to sales and service performance in bank branches", Journal of Productivity Analysis, 14, 209-224.
Du, K. & Sim, N. (2016), "Mergers, acquisitions, and bank efficiency: Cross-country evidence from emerging markets", Research in International Business and Finance, 36, 499-510. http://dx.doi.org/10.1016/j.ribaf.2015.10.005
Ebrahimnejad, A., Tavana, M., Lotfi, F. H., Shahverdi, R. & Yousefpour, M. (2014), "A three-stage Data Envelopment Analysis model with application to banking industry", Measurement, 49, 308-319. http://dx.doi.org/10.1016/j.measurement.2013.11.043
Färe, R. & Grosskopf, S. (2000), "Network DEA", Socio-Economic Planning Sciences, 34, 35-49. http://dx.doi.org/10.1016/S0038-0121(99)00012-9
Fukuyama, H. & Weber, W. L. (2015), "Measuring Japanese bank performance: a dynamic network DEA approach", Journal of Productivity Analysis, 44, 249-264. http://dx.doi.org/10.1007/s11123-014-0403-1
Ghroubi, M. & Abaoub, E. (2016), "A Meta-Frontier Function for the Estimation of Islamic and Conventional Banks’ Cost and Revenue Efficiency: The Case of Malaysia from 2006 to 2012", International Journal of Business and Management, 11, 254. http://dx.doi.org/10.5539/ijbm.v11n5p254
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
16
Hasan, M. Z. & Kamil, A. A. (2015), "Evaluation of the domestic banks’technical efficiency in Malaysia", Bulletin of Mathematics, 7, 65-79.
Ismail, F. & Ab Rahim, R. (2013), "Productivity of Islamic and conventional banks of Malaysia: an empirical analysis", IUP Journal of Bank Management, 12, 7-19.
Jadah, H. M. (2016), "Financial Performance Evaluation of Domestic Commercial Banks: An Empirical Study in Malaysia", Asian Journal of Multidisciplinary Studies, 4, 120-127.
Kamaruddin, B. H., Safab, M. S. & Mohd, R. (2008), "Assessing production efficiency of Islamic banks and conventional bank Islamic windows in Malaysia", International Journal of Business and Management, 1, 31.
Kao, C. (2014), "Network data envelopment analysis: A review", European Journal of Operational Research, 239, 1-16. http://dx.doi.org/10.1016/j.ejor.2014.02.039
Karim, M. Z. A. (2001), "Comparative Bank Efficiency across Select ASEAN Countries", ASEAN Economic Bulletin, 18, 289-304. http://dx.doi.org/10.2307/25773688
Khan, S. I. M. (2015), "Bank Efficiency in Southeast Asian Countries", International Business Management, 9, 239-250.
Khiyar, K. A. (2012), "Malaysia: 30 years of Islamic banking experience (1983-2012)", The International Business & Economics Research Journal, 11, 1133-1156.
Kordrostami, S. & Amirteimoori, A. (2005), "Un-desirable factors in multi-component performance measurement", Applied Mathematics and Computation, 171, 721-729. http://dx.doi.org/10.1016/j.amc.2005.01.081
Krishnasamy, G., Ridzwa, A. H. & Perumal, V. (2004), "Malaysian post merger banks’ productivity: application of Malmquist productivity index", Managerial Finance, 30, 63-74. http://dx.doi.org/10.1108/03074350410769038
Kwon, H.-B. & Lee, J. (2015), "Two-stage production modeling of large U.S. banks: A DEA-neural network approach", Expert Systems with Applications, 42, 6758-6766. http://dx.doi.org/10.1016/j.eswa.2015.04.062
Lai, M. C., Ling, T. P., Eng, T. K., Cheng, L. S. & Ting, L. F. (2015), "Financial Performance of Malaysia Local Banks: During Periods of Pre-Merger and Post-Merger", Journal of Economics, Business and Management, 3, 826-831.
Lin, K.-L., Doan, A. T. & Doong, S.-C. (2016), "Changes in ownership structure and bank efficiency in Asian developing countries: The role of financial freedom", International Review of Economics & Finance, 43, 19-34. http://dx.doi.org/10.1016/j.iref.2015.10.029
Lin, T. Y. & Chiu, S. H. (2013), "Using independent component analysis and network DEA to improve bank performance evaluation", Economic Modelling, 32, 608-616. http://dx.doi.org/10.1016/j.econmod.2013.03.003
Liu, J. S., Lu, L. Y. Y., Lu, W.-M. & Lin, B. J. Y. (2013), "A survey of DEA applications", Omega-International Journal of Management Science, 41, 893-902. http://dx.doi.org/10.1016/j.omega.2012.11.004
Lozano, S. (2016), "Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector", Omega-International Journal of Management Science, 60, 73-84. http://dx.doi.org/10.1016/j.omega.2015.02.012
Ma, C., Liu, D., Zhou, Z., Zhao, W. & Liu, W. (2014), "Game Cross Efficiency for Systems with Two-Stage Structures", Journal of Applied Mathematics, 14, 8-19. http://dx.doi.org/10.1155/2014/747596
Marimuthu, M. & Arokiasamy, L. (2011), "Conventional banks and productivity level: The Malaysian perspective", African Journal of Business Management, 5, 2493-2500.
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
17
Matthews, K. (2013), "Risk management and managerial efficiency in Chinese banks: A network DEA framework", Omega-International Journal of Management Science, 41, 207-215. http://dx.doi.org/10.1016/j.omega.2012.06.003
Matthews, K. & Ismail, M. 2006. Efficiency and productivity growth of domestic and foreign commercial banks in Malaysia. UK: Cardiff University, Cardiff Business School, Economics Section.
Mokhtar, H. S. A., Abdullah, N. & Al-Habshi, S. M. (2006), "Efficiency of Islamic banking in Malaysia: A stochastic frontier approach", Journal of Economic Cooperation, 27, 37-70.
Mokhtar, H. S. A., Abdullah, N. & Alhabshi, S. M. (2008), "Efficiency and competition of Islamic banking in Malaysia", Humanomics, 24, 28-48. http://dx.doi.org/10.1108/08288660810851450
Muda, M., Shaharuddin, A. & Embaya, A. (2013), "Comparative analysis of profitability determinants of domestic and foreign Islamic banks in Malaysia", International Journal of Economics and Financial Issues, 3, 559.
Muhmad, S. N. & Hashim, H. A. 2015. Using the camel framework in assessing bank performance in Malaysia. International Journal of Economics, Management and Accounting. UK: Cardiff University, Cardiff Business School, Economics Section.
Omar, M. A., Rahman, A. R. A., Yusof, R. M., Majid, M. S. A. & Rasid, M. (2006), "Efficiency of commercial banks in Malaysia", Asian Academy of Management Journal of Accounting and Finance, 2, 19-42.
Paradi, J. C. & Zhu, H. Y. (2013), "A survey on bank branch efficiency and performance research with data envelopment analysis", Omega-International Journal of Management Science, 41, 61-79. http://dx.doi.org/10.1016/j.omega.2011.08.010
Salami, O. L. & Adeyemi, A. A. (2015), "Malaysian islamic banks'efficiency: An intra-bank comparative analysis of islamic windows and full-fledged subsidiaries", International Journal of Business and Society, 16, 19.
Sherman, H. D. & Gold, F. (1985), "Bank Branch Operating Efficiency - Evaluation with Data Envelopment Analysis", Journal of Banking & Finance, 9, 297-315.
Sufian, F. (2005), "Sources of productivity changes of commercial banks in developing economy: Evidence from Malaysia, 1998-2003", International Journal of Applied Econometrics and Quantitative Studies, 2, 87-100.
Sufian, F. (2007a), "The efficiency of Islamic banking industry in Malaysia", Humanomics, 23, 174-192. http://dx.doi.org/10.1108/08288660710779399
Sufian, F. (2007b), "Mergers and acquisitions in the Malaysian banking industry: technical and scale efficiency effects", International Journal of Financial Services Management, 2, 304-326.
Sufian, F. (2009a), "Determinants of bank efficiency during unstable macroeconomic environment: Empirical evidence from Malaysia", Research in International Business and Finance, 23, 54-77. http://dx.doi.org/10.1016/j.ribaf.2008.07.0021
Sufian, F. (2009b), "Factors Influencing Bank Profitability in a Developing Economy Empirical Evidence from Malaysia", Global Business Review, 10, 225-241. http://dx.doi.org/10.1177/097215090901000206
Sufian, F. (2010a), "The evolution of Malaysian banking sector's efficiency during financial duress: consequences, concerns, and policy implications", International Journal of Applied Decision Sciences, 3, 366-389. http://dx.doi.org/10.1504/IJADS.2010.036852
Sufian, F. (2010b), "The Impact of the Asian Financial Crisis on Bank Efficiency: The 1997 Experience of Malaysia and Thailand", Journal of International Development, 22, 866-889. http://dx.doi.org/10.1002/Jid.1589
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
18
Sufian, F. (2011), "The nexus between financial sector consolidation, competition and efficiency: empirical evidence from the malaysian banking sector", IMA Journal of Management Mathematics, 22, 419-444.
Sufian, F. (2015), "Determinants of Malaysian bank efficiency: evidence from bootstrap data envelopment analysis", International Journal of Applied Nonlinear Science, 2, 100-119. http://dx.doi.org/10.1504/IJANS.2015.076529
Sufian, F. & Habibullah, M. S. (2009), "Do mergers and acquisitions leads to a higher technical and scale efficiency? A counter evidence from Malaysia", African Journal of Business Management, 3, 340-349.
Sufian, F. & Habibullah, M. S. (2010), "Does economic freedom fosters banks’ performance? Panel evidence from Malaysia", Journal of Contemporary Accounting & Economics, 6, 77-91. http://dx.doi.org/10.1016/j.jcae.2010.09.003
Sufian, F. & Habibullah, M. S. (2012), "Developments in the efficiency of the Malaysian banking sector: the impacts of financial disruptions and exchange rate regimes", Progress in Development Studies, 12, 19-46. http://dx.doi.org/10.1177/146499341101200102
Sufian, F. & Habibullah, M. S. (2013), "The impact of forced mergers and acquisitions on banks’ total factor productivity: empirical evidence from Malaysia", Journal of the Asia Pacific Economy, 19, 151-185.
Sufian, F. & Habibullah, M. S. (2015), "Does Foreign Banks Entry Fosters Bank Efficiency? Empirical Evidence from Malaysia", Engineering Economics, 21.
Sufian, F., Kamarudin, F. & Nassir, A. m. (2016), "Determinants of efficiency in the Malaysian banking sector: Does bank origins matter?", Intellectual Economics, 10, 38-54. http://dx.doi.org/10.1016/j.intele.2016.04.002
Sufian, F., Kamarudin, F. & Noor, N. H. H. M. (2014), "Revenue efficiency and returns to scale in Islamic banks: Empirical evidence from Malaysia", Journal of Economic Cooperation and Development, 35, 47-80.
Sufian, F. & Majid, M.-Z. A. (2007), "Banks' Efficiency and Stock Prices in Emerging Markets: Evidence from Malaysia", Journal of Asia-Pacific Business, 7, 35-53. http://dx.doi.org/10.1300/J098v07n04_03
Sufian, F., Muhamad, J., Bany-Ariffin, A., Yahya, M. H. & Kamarudin, F. (2012), "Assessing the effect of mergers and acquisitions on revenue efficiency: Evidence from Malaysian banking sector", Vision: The Journal of Business Perspective, 16, 1-11.
Thi, M. P. H., Daly, K. & Akhter, S. (2016), "Bank efficiency in emerging Asian countries", Research in International Business and Finance, 38, 517-530. http://dx.doi.org/10.1016/j.ribaf.2016.07.012
Wang, K., Huang, W., Wu, J. & Liu, Y.-N. (2014), "Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA", Omega-International Journal of Management Science, 44, 5-20. http://dx.doi.org/10.1016/j.omega.2013.09.005
Wanke, P., Azad, M. A. K. & Barros, C. P. (2016a), "Financial distress and the Malaysian dual baking system: A dynamic slacks approach", Journal of Banking & Finance, 66, 1-18. http://dx.doi.org/10.1016/j.jbankfin.2016.01.006
Wanke, P., Azad, M. A. K. & Barros, C. P. (2016b), "Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach", Research in International Business and Finance, 36, 485-498. http://dx.doi.org/10.1016/j.ribaf.2015.10.002
Wanke, P., Azad, M. A. K., Barros, C. P. & Hadi-Vencheh, A. (2015), "Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach", Expert Systems, 33, 213-229. http://dx.doi.org/10.1111/exsy.12144
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
19
Wanke, P., Azad, M. A. K., Barros, C. P. & Hassan, M. K. (2016c), "Predicting efficiency in Islamic banks: An integrated multicriteria decision making (MCDM) approach", Journal of International Financial Markets, Institutions and Money, 45, 126-141. http://dx.doi.org/10.1016/j.intfin.2016.07.004
Wanke, P. & Barros, C. (2014), "Two-stage DEA: An application to major Brazilian banks", Expert Systems with Applications, 41, 2337-2344. http://dx.doi.org/10.1016/j.eswa.2013.09.031
Wu, D. S., Yang, Z. J. & Liang, L. A. (2006), "Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank", Expert Systems with Applications, 31, 108-115. http://dx.doi.org/10.1016/j.eswa.2005.09.034
Yahya, M. H., Muhammad, J. & Hadi, A. R. A. (2012), "A comparative study on the level of efficiency between Islamic and conventional banking systems in Malaysia", International Journal of Islamic and Middle Eastern Finance and Management, 5, 48-62. http://dx.doi.org/10.1108/17538391211216820
Anadolu International Conference in Economics V,
May 11-13, 2017, Eskişehir, Turkey.
20
Appendix
Bank name Short Name Bank name
Short
Name
Bangkok Bank Berhad FC1
Al Rajhi Banking & Investment Corporation
(Malaysia) Berhad FI1
Bank of America Malaysia Berhad FC2 Asian Finance Bank Berhad FI2
Bank of China (Malaysia) Berhad FC3 HSBC Amanah Malaysia Berhad FI3
Bank of Tokyo-Mitsubishi UFJ (Malaysia)
Berhad FC4 Kuwait Finance House (Malaysia) Berhad FI4
BNP Paribas Malaysia Berhad FC5 OCBC Al-Amin Bank Berhad FI5
Citibank Berhad FC6 Standard Chartered Saadiq Berhad FI6
Deutsche Bank (Malaysia) Berhad FC7 Affin Bank Berhad LC1
HSBC Bank Malaysia Berhad FC8 Alliance Bank Malaysia Berhad LC2
India International Bank (Malaysia) Berhad FC9 AmBank (M) Berhad LC3
Industrial and Commercial Bank of China
(Malaysia) Berhad FC10 CIMB Bank Berhad LC4
J.P. Morgan Chase Bank Berhad FC11 Hong Leong Bank Berhad LC5
Mizuho Bank (Malaysia) Berhad FC12 Malayan Banking Berhad LC6
National Bank of Abu Dhabi Malaysia Berhad FC13 Public Bank Berhad LC7
OCBC Bank (Malaysia) Berhad FC14 RHB Bank Berhad LC8
Standard Chartered Bank Malaysia Berhad FC15 Affin Islamic Bank Berhad LI1
Sumitomo Mitsui Banking Corporation
Malaysia Berhad FC16 Alliance Islamic Bank Berhad LI2
The Bank of Nova Scotia Berhad FC17 AmIslamic Bank Berhad LI3
The Royal Bank of Scotland Berhad FC18 Bank Islam Malaysia Berhad LI4
United Overseas Bank (Malaysia) Bhd. FC19 Bank Muamalat Malaysia Berhad LI5
Public Islamic Bank Berhad LI6
CIMB Islamic Bank Berhad LI7
RHB Islamic Bank Berhad LI8
Hong Leong Islamic Bank Berhad LI9
Maybank Islamic Berhad LI10
Top Related