Performance Ranking of Turkish Life Insurance Companies Using
AHP and TOPSIS
Ilyas Akhisar
Marmara University, Banking and Insurance School, Turkey
[email protected]
Necla Tunay
Marmara University, Banking and Insurance School, Turkey
[email protected]
Abstract. The need for ongoing performance measurement and the determination of the key factors
related with efficiency and effectiveness of firm is substantial in consequence of globalization and the
competition for the firms. Traditionally, different sectors have their own performance measurement
criteria in terms of financial ratios focusing on the success of a business in the sector. The study makes
a framework for measurement taking into account the factors of the Turkish life insurance sector and
determining the effects of parameters on the performance. In order to find the performance rank of the
life insurance companies in Turkey according to financial ratios weights for each company, we use
AHP and TOPSIS method. Performance scores of life insurance companies were obtained by TOPSIS
related with companies’ weighted financial ratios. Consequently, the performances of the insurance
companies weren't changed during analyzed period.
Keywords: AHP, TOPSIS, Turkish life insurance sector, financial ratios, performance ranking
1 Introduction
The performance measurement and the determination of the key factors related with efficiency and
effectiveness of firm is essential for the business community. Performance measurement and the
determination of critical factors needed for the success of the company has very much attention from
the side of the researchers over the last few decades (Kagioglou et al., 2001; Bassioni et al., 2004).
Conventionally, performance measurement in terms of ROE, ROA and etc. display only the financial
success of the firm in its own sector. However, the power of the financial measurement cannot cope
with the industry according to the intensity of competition (Kaplan and Norton, 1992). Therefore
performance measurement is the process of determining how successful are organizations or
individuals in attaining their objectives and implementing their strategies (Evangelidizs, 1992).
Traditionally, different sectors have their own performance measurement criteria in terms of financial
ratios, which focus on only one part of the success of a business in the sector. In particular, new
technologies and intense competition cannot do facing changes the measure based performance on
financial measurements (Kaplan and Norton, 1992). Performance measurement can also be defined as
the process of quantification of the efficiency and effectiveness of a measure.
Decision-makers in business firms are always encountering different problems with many criteria
while they are performing important functions of the firm such as profit, cost, production, labor force.
Especially when potential investors are evaluating most proper companies in their investment, they are
using multiple criteria decision making methods like all benefit groups which are interested with
business firm.
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The purpose of this study is to make a framework for measurements taking into account the factors at
the level of the Turkish life insurance sector. Within the context of this study, we will rank pension
and life insurance companies using TOPSIS (Technique for Order Preference by Similarity to an Ideal
Solution) method based on the weights obtained by AHP (Analytic hierarchy process).
2 Literature Review
Insurance sector takes on tasks such as providing capital accumulation, playing an important role
within the economic development process. Pension companies are important part of financial system
contributing to economic growth and accomplishing a series of financial system function. With the
ensuring period, they can affect economic growth by source saving and allocation with also managing
the various financial risks (Curak, Loncar and Poposki, 2009).
Rakocevic (et al., 2014) show that AHP method can be successfully used for comparison and ranking
of insurance companies operating in Montenegro. They ranked the companies based on several
quantitative and some qualitative criteria which makes the analysis more comprehensive (Suwingjo et
al., 2000).
TOPSIS, performed by Hwang and Yoon (1981), is one of the multi-criteria decision making methods
that can be used in every sector and it can be helpful for the decision making. TOPSIS method tries to
determine Positive Ideal Solution (PIS) and Negative Ideal Solution (NIS) points. Preferred alternative
is not only closest to solution, at the same time, it is alternative that is most distant to negative ideal
solution; basis underlies TOPSIS approach (Behzadian et al., 2012).
TOPSIS specifically is used to measure company’s financial performance after 1970's. The method is
often used in finance literature because it provides ease to persons at the decision point. Basic
superiorities of this method are limited amount subjective input requirement and providing relative
performance measurement of each alternative from a comprehensible mathematical equation (Yeh,
2002).
Barnes (1987) interpreted financial performance of the companies by using financial ratios and the
method produced useful information about for the company partners and potential partners.
Feng and Wang (2000) investigated financial performance of five airway services using 22 financial
ratios by TOPSIS method in order to designate performance and provide accurate decision making for
the business firms.
Manabendra and Choudhury (2009) evaluated 4 criteria as client centeredness, competence, financial
possibility and easiness for determining service quality of banking sector.
Deng et al. (2000) have calculated performance score for seven textile companies according to each
company's financial ratios – they evaluated them by four financial ratios of profitableness, efficiency,
market position and debt.
Tsai et al. (2008) used Analytic Network Process (ANP) to determine the weights and constructed
performance evaluation for property-liability insurance companies in Taiwan by TOPSIS.
Isseveroglu and Sezer (2015) assessed financial performance of sixteen pension and life-pension
companies of Turkey using TOPSIS method by converting to demonstrate company performance
unique point via using financial ratios (Isseveroglu and Sezer, 2015).
242
3 Materials and Methodologies
3.1 Turkish Insurance Sector
Turkish insurance sector categorize in accordance with global insurance industry such as life and nonlife branches. Since 1998, insurance companies have been obliged to act either in the life or non-life
insurance groups according to Turkish Insurance Regulation.
The gradual increase of a potential and low penetration rate continue to draw attention of foreign
investors to the Turkish insurance market. The number of international shared insurance companies is
steadily increasing since 2001, up going trend reached 44 in 2008 and accessed to 44 at the end of
2013. In december 2013, 58 of them were joint-stock company, one was a cooperative company, and
two were branches of international companies. The share of international capital is 50% or above in 39
companies.
In 2013 the number of policies issued by life and in non-life insurance branches in Turkey increased
for 21.93% and 8.64% compared to the previous year. Regarding the asset, the receivables from main
activities reached fifty percent of total assets in 2013 with the effect of rapid growth in pension
system.
In 2013, gross written premium in insurance market rose by 22% and reached to 24.2 billion compared
to the previous year. When the premium production is examined with respect to non-life and life /
pension insurance, it is seen that the share of non-life companies was 84% in 2013.
High premium growth rates of life / pension companies in 2011 changed to the decline by 0.37% in
2012, while there is again a sharp increase in 2013. Despite the combined ratios have been over 95 %,
technical profitability of life / pension companies in 2013 is about 12%.
In Turkey, non-life insurance premiums written exceed the total life insurance premiums owning to
the fact that non-life business accounting for approximately 85% of total business. In current system,
life insurance companies could operate in all life insurance branches including health / sickness branch
and pension companies could get license in all life insurance branches except for health / sickness
branch (www.treasury.gov.tr).
3.2 Analytic Hierarchy Process
The AHP is an intuitively easy method for formulating and analyzing decisions (Saaty, 1980).
Numerous applications of the AHP have been used since its development and it has been applied to
many types of decision problems (Zahedi, 1986). Researchers interested in more detail could refer to
the most recent book written by Saaty and Penivati (2008).
In the application of AHP, the relative importance or weights of the criteria are determined after the
related performance criteria are identified and arranged in a hierarchy. The relative importance values
are determined with a scale of from 1 to 9, where a score of 1 represents equal importance between the
two elements and a score of 9 indicates the extreme importance of one element (row component in the
matrix) compared to the other one (column component in the matrix) (Meade and Sarkis, 1999; Saaty,
2009).
AHP is in the framework of a matrix by which a reciprocal value is assigned to the inverse
comparison; that is, aij= 1/ aji ; where aij (aij) denotes the importance of the ith (jth ) element compared to
243
the jth (ith ) element and a local priority vector can be derived as an estimate of relative importance
associated with the elements (or components) being compared by solving the following formulae of
A.w = λ max .w
where A is the matrix of pair-wise comparison, w is the eigenvector, and λ max is the largest
eigenvalue of A. If A is a consistency matrix, eigenvector X can be calculated by ( A − λ max I ) X = 0
Consistency index (C.I.) and consistency ratio (C.R.) to verify the consistency of the comparison
matrix are defined as
C.I . = (λ max − n) /(n − 1) , C.R. = C.I . / R.I .
where R.I. represents the average consistency index over numerous random entries of same order
reciprocal matrices. If C.R. ≤ 0.1 then the estimate is accepted; otherwise, a new comparison matrix is
solicited until C.R. ≤ 0.1 . In cases where inconsistency is above 10% it is recommended that the
criteria and judgments be revised.
The consistency ratio provides a numerical assessment of how inconsistent these evaluations might be.
If the calculated ratio is less than 0.10, consistency is considered to be satisfactory (Meade, 1996).
3.2.1. Determine the weight of criteria by AHP
In AHP decision elements of each component are compared pair-wise with regard to their importance
in the direction of their control criterion and components are also compared pair-wise and in respect of
their contribution to the achievement of the objective. On the other hand, the geometric mean of all
evaluations is also used to obtain the required pair-wise comparison matrix (Lin et al., 2009).
The basic approach for deriving weights with AHP is obtained by way of pair-wise relative
comparisons. In general, a nine-point numerical scale is recommended for the comparisons (Saaty,
1980) given in Table 1.
Table 1: Fundamental Scale
1
equal importance
3
moderate importance of one over another
5
strong or essential importance
7
very strong or demonstrated importance
9
extreme importance
2, 4, 6, 8
intermediate values
Use reciprocals for inverse comparisons
In addition, if there are interdependencies among elements of a component, pair-wise comparisons
also need to be created, and an eigenvector can be obtained for each element to show the influence of
other elements on it.
3.3 TOPSIS Method
The TOPSIS method is basically depending on the closest distance to positive-ideal solution and most
distance to negative-ideal solution developed by Hwang and Yoon (1981).
TOPSIS method procedure steps are as follows:
244
1st Step: Constitution of Decision Matrix (A): Alternatives are positioned as decision points in rows
and evaluation criteria about decision positioned in columns in the decision matrix. In the Amxn
decision matrix, m and n represent decision point number and evaluation factor numbers respectively.
Amn = {aij | i ∈(1, 2, ..., m) and j ∈(1, 2, ..., n)}
2nd Step: Normalizated Decision Matrix (R): Normalizing by square root of the sum of the squares
scores or features belong to decision matrix criteria, calculated from A matrix by applying following
equation (Opricovic and Tzeng, 2004).
rij =
aij
m
where
∑a
k =1
2
kj
(rij ∈ R and i :1, 2, . . ., n : criteria numbers, j :1, 2,..., m : alternative numbers )
3rd Step: Weighted Normalized Decision Matrix (V): In this step firstly weighted values are
determined ( w j : for each j. criteria, relative weight values of elements of normalized decision matri )
according to purpose (Monjezi et al., 2010). V matrix is formed by multiplying elements in the R
matrix each column with w j value. It is obtained as follows
V = {Vij | w j aij | i ∈(1, 2, ..., m) and j ∈(1, 2, ..., n)} where
n
∑w
j =1
j
=1
4th Step: Construction of Positive ideal (A+ ) and Negative ideal (A- ) solutions: The biggest ones
which are the weighted factors of the column values in the V matrix selected in order to get the ideal
solution set, in other words (smallest value is selected if related evaluating factor have direction of
minimization). Positive ideal ( A+ ) and negative ideal ( A− ) solutions sets obtained from V matrix as
follows respectively,
{
}
{
}
{
}
A+ = (max vij j ∈ J ), (min vij j ∈ J ' , represented by A+ = v1+ , v2+ ,..., vn+
i
i
A − = ⎧⎨(min vij j ∈ J ), (max vij j ∈ J ' ⎫⎬ , represented by A− = v1− , v2− ,..., vn−
i
⎩ i
⎭
In both formulas, J demonstrates the benefit (maximization) and J’ demonstrates the cost
(minimization) value.
5th Step: Calculation of Distance Between Alternatives: Distance between alternatives is obtained by
n sized Euclidean Distance Approach. Distance from Positive Ideal (S+) and Negative Ideal (S-)
solutions for each alternative are calculated by formulas which are given below respectively.
Si+ =
n
∑ (v
j =1
ij
− v*j ) 2 and S i− =
n
∑ (v
j =1
ij
− v −j ) 2
6th Step: Calculation of Relative Closeness to the Ideal Solution: Distinction measurements are used
to calculation of relative closeness (C*) to the ideal solution has shown in the following (Olson 2004):
C i* =
S i−
where 0 ≤ Ci* ≤ 1
−
*
Si + Si
245
7th Step: Closeness of the Alternatives to the Ideal Solution: Closeness of the alternatives to the ideal
solution is sorted according to the value Ci* , alternative which have highest Ci* is chosen.
4 Implementation and Results
4.1 Implementation
TOPSIS is an effective decision-making tool using AHP weighing for each attribute by experts. In the
application of AHP, the relative importance or weights of the criteria are determined after the related
performance criteria are identified and arranged in a hierarchy
Criteria used by the AHP were identified as a result of literature reviewing and interviewing with the
industry's leading experts. The designated criteria determined to be used in the study have three
components (Capital Adequacy Ratios, Asset Quality Ratios, Profitability Ratios) and total ten unit
(sub-criteria) under these three main criteria.
Capital Adequacy
• Premiums Received / Shareholders’ Equity
• Shareholders’ Equity / Technical Provisions
• Shareholders’ Equity / Total Assets
Profitability
• Financial Profit-Loses / Premiums Received
• Loss ratios
• Technical Profit-Loses/ Financial Profit-Loses
• Technical Profit-Loses / Premiums Received
• Total Income / Premiums Received
Asset Quality
• Cash and Cash Equivalents / Total Assets
• Retention Rate
.
Expert Choice© software was used to evaluate pairwise-comparison judgments, driving priorities from
these judgments. The consensus of the groups was calculated using the geometric mean of individual
judgments and combined experts weights are given in the following Table 2.
Table 2: AHP Weights
Main
Combine
Criteria
Weights
Capital Adequacy
0.3827
Profitability
0.3787
Asset Quality
0.2385
Sub-Criteria
Premiums Received / Shareholders’ Equity
Shareholders’ Equity / Technical Provisions
Shareholders’ Equity / Total Assets
Financial Profit-Loses / Premiums Received
Loss ratios
Technical Profit-Loses/ Financial Profit-Loses
Technical Profit-Loses / Premiums Received
Total Income / Premiums Received
Cash and Cash Equivalents / Total Assets
Retention Rate
Inconsistency
246
Combine
Weights
0.41357
0.27781
0.30714
0.36583
0.12165
0.06312
0.21738
0.23202
0.55127
0.44873
9,81 %
The consistency ratio C.R. ≤ 0.1 of the judgments is acceptable to validate for decision process.
4.2 Results
Performance ranking results of pension and life insurance companies of Turkish insurance sector for
the period of 2009 - 2013 years evaluated by TOPSIS method based on AHP weights are given in the
following Table 3.
Table 3: Performance Ranks of Life Insurance & Pension Companies (2009-2013)
No Company Name
2009 Company Name
2010 Company Name
1 ANADOLU EMEKLILIK
0,975 ANADOLU H/E
0,942 ANADOLU H/E
2 AVIVASA EMEKLILIK
0,818 AVIVASA E/H
0,850 AVIVASA E/H
3 GARANTI EMEKLILIK
0,658 GARANTI E/H
0,751 GARANTI E/H
4 YAPI KREDI EMEKLILIK
0,649 YAPI KREDI EMEKLILIK
0,651 YAPI KREDI EMEKLILIK
5 GROUPAMA EMEKLILIK
0,524 VAKIF EMEKLILIK
0,363 ZIRAAT H/E
6 ALLIANZ EMEKLILIK
0,381 ZIRAAT H/E
0,326 VAKIF EMEKLILIK
7 VAKIF EMEKLILIK
0,365 ALLIANZ H/E
0,326 AMERICAN LIFE
8 AXA HAYAT
0,144 GROUPAMA EMEKLILIK
0,285 BNP PARIBAS CARDIF EMEKLIK
9 FORTIS EMEKLILIK
0,121 FORTIS E/H
0,178 ALLIANZ H/E
10
11
12
13
14
15
16
17
18
19
20
21
22
23
2011
0,939
0,803
0,798
0,645
0,415
0,335
0,279
0,267
0,252
AMERICAN LIFE
0,107 ING EMEKLILIK
0,172 GROUPAMA EMEKLILIK
0,207
BIRLIK HAYAT
0,086 AXA H/E
0,139 ING EMEKLILIK
0,166
MAPFRE GENEL YASAM
0,072 HALK H/E
0,122 METLIFE E/H
0,117
DENIZ EMEKLILIK
0,069 DENIZ E/H
0,097 HALK H/E
0,114
FINANS EMEKLILIK
0,062 AMERICAN LIFE
0,087 AXA H/E
0,101
AEGON EMEKLILIK
0,054 MAPFRE GENEL YASAM
0,079 FINANS E/H
0,093
ACIBADEM
0,046 FINANS E/H
0,076 AEGON E/H
0,072
ERGO EMEKLILIK
0,045 AEGON E/H
0,075 ACIBADEM
0,059
DEMIR HAYAT
0,037 CARDIF HAYAT
0,072 MAPFRE GENEL YASAM
0,058
CARDIF HAYAT
0,037 ACIBADEM
0,066 ERGO E/H
0,040
CIV HAYAT
0,019 ERGO E/H
0,050 BNP PARIBAS CARDIF HAYAT
0,036
NEW LIFE
0,003 DEMIR HAYAT
0,037 DEMIR HAYAT
0,026
CIV HAYAT
0,016 CIV HAYAT
0,009
NEW LIFE
0,002 CIGNA HAYAT
0,001
247
Table 3. (Cont.)
No Company Name
1 ANADOLU H/E
2 GARANTI E/H
3 AVIVASA E/H
4 YAPI KREDI EMEKLILIK
5 ZIRAAT H/E
6 VAKIF EMEKLILIK
7 METLIFE E/H
8 ALLIANZ H/E
9 BNP PARIBAS CARDIF EMEKLILIK
2012 Company Name
2013
0,936 ANADOLU H/E
0,860
0,849 GARANTI E/H
0,712
0,796 AVIVASA E/H
0,662
0,727 ALLIANZ Y/E
0,606
0,379 ZIRAAT H/E
0,343
0,344 VAKIF EMEKLILIK
0,298
0,323 METLIFE E/H
0,249
0,236 ALLIANZ H/E
0,198
0,225 BNP PARIBAS CARDIF EMEKLILIK
0,172
10 ING EMEKLILIK
0,210 HALK H/E
11 GROUPAMA EMEKLILIK
0,192 ING EMEKLILIK
12 HALK H/E
0,122 GROUPAMA EMEKLILIK
13 FINANS E/H
0,122 CIGNA FINANS E/H
14 ACIBADEM
0,093 AXA H/E
15 AXA H/E
0,079 ACIBADEM
16 AEGON E/H
0,076 AEGON E/H
17 ERGO E/H
0,057 ERGO E/H
18 MAPFRE GENEL YASAM
0,030 ASYA E/H
19 BNP PARIBAS CARDIF HAYAT
0,029 BNP PARIBAS CARDIF HAYAT
20 DEMIR HAYAT
0,027 DEMIR HAYAT
21 CIV HAYAT
0,014 MAPFRE GENEL YASAM
22 CIGNA HAYAT
0,001 CIV HAYAT
23
FIBA E/H
24
CIGNA HAYAT
where H/E: Life/Pension, Hayat: Life, Yasam: Life and Emeklilik: Pension
0,169
0,164
0,129
0,099
0,076
0,075
0,058
0,035
0,032
0,022
0,019
0,019
0,014
0,010
0,002
The investigation of performance ranks of pension and life insurance companies shows that
company’s performance rank is affected by capital adequacy with respect to premium received, total
assets and shareholders’ equity respectively.
Moreover, it is observed that the performance ranks of the companies are highly correlated with
profitability ratios of companies whose row data. We could not get appropriate result, row data
belonging to companies according to asset quality variables, in accordance with performance ranks of
the companies.
5 Conclusions
Measuring performance in industries and determining the key drivers of performance have
been an important research topic in recent years. In particularly, investors and administrators
frequently need to decision-making and assessment about firms and sectors.
248
The AHP approach allows us to use quantitative and qualitative information making this
methodology flexible. Moreover, regarding of the non-financial factors such as service
quality, customer’s satisfaction and etc., may give more accurate results about firm
performance. AHP was selected as a very handy tool for weighting the ratios instead of
ordinary usage used by TOPSIS.
Within the context of this study, to rank pension and life insurance companies we used
TOPSIS based on the weights obtained by the AHP as well as the factors at the level of the
market (competition, application) and set of relevant efficiency together with their
interactions to determine the effects of parameters on the performance.
AHP assumes independence among the criteria and the alternatives. If there is dependence
among the criteria, the Analytic Network Process is more appropriate for comparisons which
may be formidable in a practical decision environment.
In this research, the main criteria and sub-criteria weighted by experts’ decision supporting an
adequate relation with companies own raw data. Another interesting direction of research that
performance ranks of the insurance companies evaluated by other multi-criteria decision
making methods to compare the results obtained by TOPSIS.
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