Anadolu International Conference in Economics V,

May 11-13, 2017, Eskişehir, Turkey.

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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, azadiiuc@gmail.com

Kwek Kian Teng (Correspondent author)

Department of Economics

Faculty of Economics and Administration, University of Malaya

50603 Kuala Lumpur, Malaysia, ktkwek@um.edu.my

Muzalwana Binti Abdul Talib

Department of Applied Statistics

Faculty of Economics and Administration, University of Malaya

50603 Kuala Lumpur, Malaysia, wana_am@um.edu.my

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.

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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.

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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.

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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.

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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,

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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

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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.

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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