Essay: Thesis: Evaluate the prospects and operational impediments of Sadharan Bima Corporation.

Abstract:
A few partial studies have been conducted on Sadharan Bima Corporation (nationalized insurance company), but the potentiality and operational impediments of Sadharan Bima Corporation (SBC) is not studied as a whole. This study is a reflection of the severity and likelihood of the prospects and operational impediments of SBC. Direct premium income, re-insurance premium income, receded premium income, net premium income, total asset, total investment, total investment income, net claims and annual profit before tax are considered as parameters. Graphical and statistical analyses are used to analyze the prospects of SBC and Factor analysis is used to find out the operational impediments of the SBC. This study found that there is a positive relationship of direct premium income, re-insurance premium income, receded premium income, net premium income, total asset, total investment, total investment income, net claims and annual profit before tax with the year. This study also advocates some recommendation to minimize the operational impediments.
Keywords: SBC,
Objective of the study:
The objective of the study can be viewed in two forms:
‘ Broad Objective
‘ Specific Objective
Broad Objectives:
The broad objective of this study is to evaluate the prospects and operational impediments of Sadharan Bima Corporation.
Specific Objectives:
‘ To identify how much ‘Irregular auditing’ affect the overall SBC.
‘ To identify how much the’ High cost of office equipment’ affect the Sadharan Bima Corporation.
‘ To identify how much ‘Lack of research and development’ affect the overall the Sadharan Bima Corporation.
‘ To identify how much ‘Employment inefficiency’ affect the overall SBC.
‘ To identify how much ‘Unavailability of customer’ affect the SBC.
‘ To identify how much ‘Poor supervision of damage property or material regarding the claim’ affect the SBC.
‘ To identify how much ‘Insufficient manpower’ affect the SBC.
Rationale of the study:
As a result of globalization, deregulation and terrorist threats, the insurance industry has undergone a tremendous transformation over the past decade. Globally insurance business was increasing at a low rate till 2007 and registered a growth of 3.3% but in 2008, the industry contracted by 4.2% to reach gross written premium of $3,888.6 billion. In the industrialized countries, life insurance premium dropped by 5.3% in 2008 while non-life premiums witnessed a smaller decline of 1.9%. The region which growing significantly is the emerging market where non-life growth was 7.1% in 2008 compared to 2007 levels and life insurance premium growth accelerated to 14.16%. The premium per capita around the world stood at $646 which was only $ 2.90 for Bangladesh. More than 87% of the year’s premiums were earned in the industrialized nations leaving a little more than 12% earned in emerging markets. While the growth around the world is becoming stagnant, Bangladesh insurance business is increasing signifactly. Over the recent years, the life and nonlife business has globally increased on an average by 4.4% and 3.1% respectively, where as Bangladesh witnessed average growth rates of 10.7% in non-life and a 25.4% growth in life business (mamun M. Z, 2011).
Literature Review:
Hossain(2013) examined the inquest of positive image of insurance industry in Bangladesh. This study conveyed that as a service industry, a huge level of people involved in the insurance industry that why the enrichment of the employees is really important in order to satisfy the customers especially for marketing related employees. The study also showed the importance of the training and development. Finally the study postulated that when insurance awareness education will prevail in insurance industry with competent workplace, efficient common system, professional commitment and a firm back-up of government patronage with political, social and economic stability, then the industry will enter into a world of dignity free from image constraints.
Hasan and Khanam(2013)evaluated the performance of Sadharan Bima Corporation (SBC), the public sector general insurance company in Bangladesh, during the period 2007 to 2011. Both primary and secondary data have been used there. The study indicated that the overall performance of the enterprise had been more or less satisfactory during the period. The operational performances profitability, & activity had been satisfactory but the financial performances long-term solvency, liquidity and also productivity were not satisfactory. Finally the paper suggested that for further continuous growth and development, SBC should take strategic steps like, adoption of modern techniques for asset management, follow-up of modern marketing strategies, launching more research & development programs, develop HRD program, relaxing pricing rules etc.
Mamun(2011) evaluate the performance of eight general insurance companies(seven private and one national) operating in Bangladesh over a period of 18 years(1990-2008) the parameters considered are net premium income, investment & investment income, net claims annual profit before taxes, total assets and share price. It was seen that most of the private companies are increasing their performance. But the nationalized insurance company (SBC) outperforms all other private insurance firms in most of the parameters except income earnings.
Bhuiyan(2011) examined the managers’ opinion on Islamic management practices in Islamic life insurance companies. The researchers showed that most of the employees of this sector are interested to follow the Islamic management. This study also advocated some recommendations for the successful applications of Islamic Management system in the Islamic insurance companies.
Hossain (2011) examined the essence of positive attitude in insurance salesmanship. This study conveyed that a bad attitude reduced output and negatively influenced people around; meanwhile, a positive attitude enhanced performances and influenced people around. The study tried concluded that an overall positive outlook is of paramount importance for success.
Haque (2006) examined the professionalism in insurance sector. He conveyed that the first impression of employees, attitudes, teamwork, level of knowledge were and integral part of professionalism of an employee. He also advocated some recommendations regarding the improvement and development of professionalism such as education and code of conducts.
Azam(2005) examined the attitude towards general Insurance service: contrasting the public and private sectors in Bangladesh. The study revealed the customers’ favorable perception towards financial strength and goodwill of SBC while office environment, risk underwriting and client services are favorable for private companies. The model anticipated overall customers’ attitude towards government owned general insurance organization is favorable than private insurance companies in Bangladesh.
Ahmed (2005) advocated the automation of general insurance companies in Bangladesh. His study focused on computerization of insurance industry. He also postulated the benefits of web services for insurers’ center around three interrelated areas which were expense optimization, process enhancement and strategic flexibility or operating adaptability.
Murshid (1993) discussed the various aspects of general insurance business of Bangladesh, such as background, business scenario, contribution and drawbacks. But it gives a partial picture.
Islam (1992) stated that while in the developing country both the Government and the financial institutions endeavor to dig deep in to the insurance fund for gainful utilization in various nation building activities like industrial financing, social and infrastructure development projects such a phenomenon is absent in Bangladesh.
Uddin and Kabir (1990) examined the trend of growth of the asset to find out asset structure, identify the statutory regulations governing quantum of different types of assets and evaluated the impact of asset structure on profit performance.
Thus the existing literatures indicate that a few partial studies have been conducted on state-owned enterprise, but the prospectus of Sadharan Bima Corporation is not studied comprehensively as a whole. This study is therefore a humble attempt to make a comprehensive evaluation of prospectus of Sadharan Bima Corporation.
Research Methodology:
Research methodology includes Research design, Data source and collection procedures; sampling method, sample unit and data analysis procedures.
Research Design:
It is an exploratory research to evaluate the prospects and operational impediments of SBC. In order to identify the prospects of SBC, the study use the graphical and statistical analysis i.e regression analysis and trend analysis etc. The study use Factor analysis to identify the variables which creates the operational impediments. Based on the outcome of the statistical analysis, the findings and analysis part has been done.
Data Source and Data collection Procedures:
For smooth and accurate study everyone has to follow some rules & regulations. The study inputs were collected from two sources:
a) Primary sources:
The methods that were used to collect the primary data are as follows:
A structured questionnaire was used as the research instrument to collect primary data from the employees of SBC. The questionnaire started with the demographic profile of the respondents. The next part of questionnaire is comprised to the key measuring variables of job satisfaction. Questions measuring the factors on a Likert scale ranging from 5 (strongly agree) to 1(strongly disagree).
b) Secondary Sources:
The secondary data has been collected from the following sources:
‘ Annual reports of the respective SBC
‘ Report of Bangladesh Insurance Academy
‘ Report of Insurance Development and Regulatory Authority
‘ Text Books on Insurance
‘ Insurance Journals, Bangladesh Insurance Academy
‘ Extensive literature search on the basis of these documents of publication
‘ Website of various international journal association.
Sampling Method:
Here the non-probability convenience sampling method was used to gather data as of the sample of this research. The convenience sample for this study is measured as the employees of respective SBC who are working within the respective organizations.
Sample Units:
In order to find out the prospects of SBC, the study use parameters are direct premium income, re-insurance premium income, receded premium income, net premium income, total asset, total investment, total investment income, net claims and annual profit before tax as parameters for a period of 2004-2013 which are collected from the audited annual report of SBC. And in order to find out the operational impediments of SBC, the study involved a field survey conducted across different regional Office in Dhaka, Mymensingh, Netrokona, Jamalpur ,Tangail and Sherpur. The respondents were approached at their workplace.
Survey Instrument:
The annual report of SBC and structured questionnaire is used to collect data from the sample. The reason behind to use the structured questionnaires is its practicality and advantageous over other survey instrument like observation, interview. Questionnaires are very cost effective. Through questionnaires responses are gathered in a standardized way, so questionnaires are very objective. Also it is less time consuming.
Data Analyzing Procedures:
The variables is used in the survey instrument have been abbreviated in order to facilitate the analysis procedures(Appendix-01) Both descriptive and inferential statistics were used to analyze the survey data. Factor analysis has been used in order to expose the factors of job satisfaction.
After collecting the data a statistical tool is used to demonstrate the scale to which one variable is connected to another variable. This statistical tool is known as correlation analysis. Correlation analysis is used to evaluate the reality of relationship among the considered variables.
1.1 Trend of total assets:
Assets are defined any item of economic value owned by an individual or corporation especially that can be converted into cash, security, accounts receivable, inventory, office equipments, real estate, and other property like patents, copy right etc. Assets of SBC are comprised of government securities; investment in shares & debentures, house properties, interest and rent outstanding, sundry debtors, cash in hand and balance with banks, stamps in hand, stock of consumable materials, advance income tax and sundry fixed assets.
Table 01
Total Assets of SBC
Years Assets Net Increase % increase
2004 4956746765
2005 5664876567 708129802 14.29
2006 6666182320 1001305753 17.68
2007 7768142508 1101960188 16.53
2008 9623838448 1855695940 23.89
2009 9946269902 322431454 3.35
2010 11211132371 1264862469 12.72
2011 13383791792 2172659421 19.38
2012 15732767825 2348976033 17.55
2013 18470863030 2738095205 17.40
Average: 23.38%
Sources: Annual Report of SBC (2005-2013)
Figure 1: Flow of Total Assets
The total assets follow a positive flow of grow rate from FY 2004 to FY 2013. On an average 23.38 percent has been increased from each year to other year during this period. During the FY 2004 and FY 2005 the amount of total assets were BDT 4956746765 and BDT 5664876567 respectively. Total assets were increased by BDT 708129802 or 14.29 percent from FY 2004. Like that growth rate of flow of total assets of FY 2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013were 17.68 percent, 16.53 percent, 23.89 percent, 3.35 percent, 12.72 percent, 19.38 percent, 17.55 percent, 17.40 percent respectively.
Figure 2: Trend of Total Assets
The trend shows an upward trend of total assets of SBC from FY 2004 to FY 2013. The average growth rate was positive growth rate (23.38 percent). So there was a huge potentiality to enhance the volume of total assets within those years.
1.2. Statistical Analysis:
Several statistical tools are used to analyze the variability of the growth of SBC. These analyses are:
‘ Regression analysis
‘ ANOVA and F-distribution
‘ T-distribution
Regression analysis:
Regression analysis measures the nature of relationship between dependent variable and independent variable. It describes the positive or negative relationship between variables.
Regression equation: ?? = a + bX
Here, ?? is dependent variable and X is independent variable. The value of ?? depends on the value of X. b is the slope of X which gives the average amount of change of ?? per unit change in the value of X. the sign b also indicates the type of relationship between ?? and X.
Hypothesis:
The broader hypotheses are as under
H0= There is no significant relationship between total assets & year.
H1= There is significant relationship between total assets & year.
ANOVA:
ANOVA means Analysis of variance, total sum of squares can be divided into two components such as Sum of Squares due to Regression (SSR) and Sum of Squares due to Error (SSE).
SST = SSR + SSE
Here, SST = Total Sum of Squares
SSR = Sum of Squares due to Regression
SSE = Sum of Squares due to Error
H0 is rejected when the overall relationship between dependent variable and independent variable is significant. However, if H0 is accepted, we do not have the sufficient evidence to discover that a significant relationship exists between dependent and independent variable.
If H0 is accepted, MSR provides an unbiased estimate of ??2 and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, if H0 is true and the assumptions about the multiple regression model are valid, the sampling distribution of MSR/MSE is an F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-distribution is given below:
F = MSR/MSE
F-distribution:
F-distribution shows whether taken model is rejected or accepted as a whole or on an average, but not individually. So it is showing whether there is any relation between dependent variable and independent variable. Moreover, according to p value, it has been deduced that F-test rejected null hypothesis (H0) and expresses that there independent variable is significant on dependent variable.
T-distribution:
In t-test analysis, the relationship between the dependent variable and independent variable will be determined and the hypothesis test will be verified bt the value of t as the null hypothesis is accepted or rejected. If the calculated t value is greater than table value of t then null hypothesis will be rejected and alternative hypothesis will be accepted. Here the null hypothesis H0 =0, there is no relationship between dependent variable and independent variable. Alternative hypothesis (H1), there is a positive relationship between dependent variable and independent variable.
1.3 Regression Model: Year and total assets
Here we use simple regression technique; year and total assets are considered as variables. Total asset is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of assets and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. Total amount of assets and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.952 from appendix 6. It shows that how much dependent variable (assets) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 972558253.364 from appendix 6 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 169264894055203800000.000 from appendix 6 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 7566956449486699500.000 from appendix 6 showing the extent to which error is remaining after the regression and can be minimized with the increment of the assets (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 176831850504690500000.000 (appendix 6) that comes after adding the SSR 169264894055203800000.000 and SSE 7566956449486699500.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 178.952 (appendix 6)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 178.952>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & assets.
Moreover, the p-value (sig.) = 0.000 from appendix 6 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -2866581005050.527 + 1432374142.994X (appendix 6)
The regression equation describes that there is not positive relationship between assets and year.
Dependent variable (??): assets
Independent variable (X): year
If year = zero then ??= -2866581005050.527. Here, ??= -2866581005050.527 from appendix 6, which is ??, intercept. It shows that assets of SBC will be -2866581005050.527 (appendix 6) if SBC’s year is zero.
Now, the value of b or slope of X is 1432374142.994, it means if year is increased by 1 then the assets will increase for BDT 1432374142.994 assuming all other variables are constant.
T-test:
The calculated value of t is 13.377 from appendix 6 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and assets of SBC.
The projected value of the total assets of SBC from FY 2014 to FY 2020 are given below which also indicates the consisting increasing trend.
Table 02
Forecasted Total Assets
Year Total Assets (Projected)
2014 18220518939
2015 19935551550
2016 21641180342
2017 23344697185
2018 25025143613
2019 26834183886
2020 28426068311
1.4 Trend of number of Insurance Policy:
In insurance, the policy is a contract (generally a standard from contract) between the insurer and the insured, which determined the claims which the insurer is legally required to pay in exchange of an initial payment (known as the premium) from the insured. The insurer promised to pay for loss caused by perils covered under the policy language. On the other hand the insurance policy is generally an integrated contract, meaning that it includes all forms associated with the agreement between the insured and insurer.
Table: 03
Number of Insurance Policy
Years No. of insurance policy Net Increase % increase
2004 46924
2005 47685 761 1.62%
2006 48765 1080 2.26%
2007 50587 1822 3.73%
2008 55,669 5082 10.05%
2009 58,368 2,699 4.85%
2010 1,04,707 46,339 79.39%
2011 62,748 – 41,959 – 40.07%
2012 1,16,489 53,741 85.65%
2013 1,09,998 -6,491 -5.57%
Average Growth : 17.54%
Sources: Annual Report of SBC (2005-2013)
Figure 3: Trend of Number of Insurance Policy
The total numbers of insurance policies follow a consistent increasing trend except for FY 2010 where it was drastically increased to 104707l, but again in the following year it decreased drastically to 62,748. A positive growth rate of number of insurance policies had been seen from FY 2005 to FY 2010. In the FY 2011 a negative growth rate was observed due to fewer occurrences of natural disasters. But again in the FY 2012 a positive growth rate was observed. But again in the FY 2013 a negative growth rate was found. On an average 17.54 percent increase had been found from each year to other year during the period. During the FY 2008 and FY 2009 the numbers of insurance policies were 55,669 and 58,368 respectively that net increase was 2,699 or 4.85 percent from FY 2008. Like this growth rate of number of insurance policy of FY 2010, FY 2011, FY 2012 and FY 2013 were 79.39%, – 40.07%, 85.65%, and -5.57% respectively. Here in FY 2011 and FY 2013 the growth rate of numbers of insurance policies were negative that were -40.07 percent and -5.57 percent. The reasons behind this negative growth rate were more inflation, economic degradation and fewer occurrences of natural disaster in the FY 2011and FY 2013. But it was matter of great hope that in FY 2012 the growth rate of number of insurance policy had increased at 85.65%. This was the highest growth rate among the last 10 years.
Figure 4: Flow of Insurance Policy
The trend shows that an upward trend of the numbers of insurance policies of SBC up to FY 2010 and then a drastically downward slope in FY 2011 and again a drastically upward slope in FY 2012 and a little downward slope in FY 2013. But it was a matter of great hope that the average growth rate was positive (24.85 percent). So, there is a huge potentiality to include more insurance policies within current year and in future.
1.5 Regression Model: Year and Number of insurance policies
Here we use simple regression technique; year and number of insurance policies are considered as variables. Number of insurance policies is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of assets and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. Number of insurance policies and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.654 from appendix 7. It shows that how much dependent variable (number of insurance policies) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 16653.742 from appendix 7 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 4998013017.648 from appendix 7 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 2218776880.352 from appendix 7 showing the extent to which error is remaining after the regression and can be minimized with the increment of number of insurance policies (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 7216789898.000 (appendix 7) that comes after adding the SSR 4998013017.648 and SSE 2218776880.352.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 18.021 (appendix 7)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 18.021>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & number of insurance policies.
Moreover, the p-value (sig.) = 0.003 from appendix 7 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -15562850.109 + 7783.442X (appendix 7)
The regression equation describes that there is not positive relationship between assets and year.
Dependent variable (??): number of insurance policies
Independent variable (X): year
If year = zero then ??= -15562850.109. Here, ??= -15562850.109 from appendix 7, which is ??, intercept. It shows that number of insurance policies of SBC will be -15562850.109 (appendix 7) if SBC’s year is zero.
Now, the value of b or slope of X is 7783.442, it means if year is increased by 1 then the number of insurance policies will increase for 7783.442 assuming all other variables are constant.
T-test:
The calculated value of t is 4.245 from appendix 7 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and number of insurance policies of SBC.
The trend of the total no. of insurance policy of SBC up to FY 2020 are given below which also indicates the consisting increasing trend.
Table: 04
Forecasted Number of Insurance Policy
Year Forecasted no. of insurance policy
2014 116594.9
2015 128400.6
2016 140040.8
2017 150774.8
2018 160377.9
2019 167148.2
2020 182585.8
1.6 Trend of Net Claim:
A claim settlement is an agreement between two or more parties to settle a legal claim with payment and other terms. Claim is one of the most important factors of any insurance organization. To a large extent a company’s future business largely depends on how promptly and fairly it has dealt with the claims. Indeed the payment of claims may be regarded as the primary service of insurance to the public. Any claim is settled in three steps: first the claimant sends a notice to the insurance company, second the insurance company conducts an investigation on the claims (Institute, Insurance. 1999) on the basis of investigation and third, the claims are letter settled. Net claims constitute of the amount of claims settled during a particular year along with the claims outstanding of the year. Those outstanding claims are settled in the following year. Therefore net claims are indicator of the amount of claims logged and the amount of claims paid out in a particular year. (M. Z. Muhammad, 2011)
Table: 05
Net Claim Paid of SBC
Year Net Claim Paid (BDT) Net Increase % change
2004 527711360
2005 862873676 335162316 63.51%
2006 1025429417 162555741 18.84
2007 1331861733 306432316 29.88
2008 1340291236 8429503 0.63
2009 1322186274 -18104962 -1.35
2010 1513397213 191210939 14.46
2011 1125809515 -387587698 -25.61
2012 1641076760 515267245 45.77
2013 2108408984 467332224 28.48
Average Growth : 19.40
Sources: Annual Report of SBC (2005-2013)
Figure 5: Trend of Net Claim Paid
The total volume of net claim follows a consistent increasing trend except for FY 2009 and FY 2011. The highest claims were settled in FY 2013 (BDT 2108408984). A positive growth rate of net claim paid had been seen from FY 2005 to FY 2008. But in the FY 2009 and FY 2011 there were a negative growth rate -1.35%, -25.61% observed respectively. But again in the FY 2010, FY 2012, FY 2013 a positive growth rate of the net claim paid was observed. On an average 19.40 percent increase had been found from each year to other year during the period. During the FY 2004 and FY 2005 the net claim paid were BDT 527711360 and BDT 862873676 respectively that net increase was BDT 335162316 or 63.51 percent from FY 2006. Like this, growth rate of net claim paid of FY2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013 were 18.84 percent,29.88 percent, 0.63 percent, -1.35 percent, 14.46 percent, -25.61 percent, 45.77 percent and 28.48 percent respectively. Here in FY 2009 and FY 2011 the growth rate of net claim paid was negative that is -1.35% and -25.62%. The reason behind this negative growth rates were less natural disaster occurred during that period.
Figure 6: Flow of Net Claim Paid
The trend shows an upward flow of net claim paid of SBC from FY 2005 to FY 2008 and then a little downward slope in FY 2009 and FY 2011 due to less occurrences of natural disaster during this period. A consistent increasing upward slope was observed in the last two years (FY 2012 and FY 2013) due to large amount of net claims arose from fire insurance policy holders. The average growth rate was a positive growth rate (19.40%). So, there is a huge potentiality to pay net claim within current year and in future.
1.7 Regression Model: Year and Net claim
Here we use simple regression technique; year and number of insurance policies are considered as variables. Net claim is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of net claims and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. Net claims and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.731 from appendix 8. It shows that how much dependent variable (Net claims) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 226111577.325 from appendix 8 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 1298718285878566910.000 from appendix 8 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 409011563204533500.000 from appendix 8 showing the extent to which error is remaining after the regression and can be minimized with the increment of net claim (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 1707729849083100420.000 (appendix 8) that comes after adding the SSR 1298718285878566910.000 and SSE 409011563204533500.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 25.402 (appendix 8)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 25.402>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & net claim.
Moreover, the p-value (sig.) = 0.001 from appendix 8 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -250721139095.091 + 125467285.891X (appendix 8)
The regression equation describes that there is not positive relationship between net claim and year.
Dependent variable (??): net claim
Independent variable (X): year
If year = zero then ??= -250721139095.091. Here, ??= -250721139095.091 from appendix 8, which is ??, intercept. It shows that net claim of SBC will be -250721139095.091 (appendix 8) if SBC’s year is zero.
Now, the value of b or slope of X is 125467285.891, it means if year is increased by 1 then the net claim will increase for 125467285.891 assuming all other variables are constant.
T-test:
The calculated value of t is 5.040 from appendix 8 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and net claim of SBC.
The forecasted value of claim settlement of SBC from 2014 to FY 2020 are given below which also indicates the consisting increasing trend.
Table: 06
Forecasted Net Claim Paid of SBC
Year Forecasted Net Claims Settlement
2014 1969974689
2015 2045417850
2016 2144264003
2017 2247160538
2018 2398341998
2019 2551223989
2020 2689894070
1.8 Underwriting Premium Income:
SBC is entitled to 50 percent of public sector business in Bangladesh. Insurance Corporation (Amendment) Act 1990 provides that fifty percent of all insurance business relating to any public property or to any risk or liability appertaining to any public property shall be placed with the SBC and the remaining fifty percent of such business may be placed with this Corporation or with any other insurers in Bangladesh. But for practical reason and in agreement with the Insurance Association of Bangladesh SBC underwrites all the public sector business and 50 percent of that business is distributed among the existing 43 private general insurance companies equally under National Co-insurance Scheme.(source: website of SBC)
Table: 07
Underwriting Premium Income of SBC
Years Underwriting Premium Income (BDT) Net Increase % change in Underwriting Premium Income
2004 778600000
2005 886100000 107500000 13.81
2006 1044500000 158400000 17.88
2007 1265800000 221300000 21.19
2008 1419000000 153200000 12.10
2009 1613500000 194500000 13.71
2010 1659900000 46400000 2.88
2011 1974800000 314900000 18.97
2012 2189200000 214400000 10.86
2013 1909600000 -279600000 -12.77
Average Growth : 10.96%
Sources: Annual Report of SBC (2005-2013)
Figure 7: Trend of Underwriting Premium Income
Underwriting premium of SBC follows a consistent increasing trend except for FY 2013. The highest underwriting premiums were collected in FY 2012 (BDT 2189200000). A positive growth of underwriting premium incomes had been seen from FY 2005 to FY 2012. But in the FY 2013 a negative growth rate was observed (-12.77 percent). On an average 10.96 percent increase had been found from each year to other year during the period. During the FY 2004 and FY 2005 the amount of direct premium incomes were BDT 778600000 and BDT 886100000 respectively that was net increase of BDT 107500000 or 13.81 percent increase from FY 2004. Like this growth rate of underwriting premium incomes of FY 2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013 were 17.88 percent, 21.19 percent, 12.10 percent, 13.71 percent, 2.88 percent, 18.97 percent, 10.86 percent and -12.77 percent respectively. Here in FY 2013 the growth rate of underwriting premium incomes was negative that was -12.77 percent due to lack of activity of SBC in increasing the number of insurance policy.
Figure 8: Flow of Underwriting Premium Income
The underwriting premium shows a small fluctuation during this period and it was highest in FY 2012. The trend shows that there was an upward slope of underwriting premium income of SBC from FY 2005 to FY 2012 and then a little downward slope in FY 2013 which was really an alarming situation. But it was a matter of great hope that its average growth rate was positive 10.50 percent and the trend also shows the increasing trend of underwriting premium. So there is a huge potentiality to get more underwriting insurance premium within current year and in future.
1.9 Regression Model: Year and Net claim
Here we use simple regression technique; year and number of insurance policies are considered as variables. Underwriting premium is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of underwriting premium and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. Underwriting premium and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.934 from appendix 9. It shows that how much dependent variable (Underwriting premium) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 123497053.701 from appendix 9 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 1944115881818181630.000 from appendix 9 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 122012178181818096.000 from appendix 9 showing the extent to which error is remaining after the regression and can be minimized with the increment of underwriting premium (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 2066128059999999740.000 (appendix 9) that comes after adding the SSR 1944115881818181630.000 and SSE 122012178181818096.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 127.470 (appendix 9)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 127.470>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & underwriting premium.
Moreover, the p-value (sig.) = 0.000 from appendix 9 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -306848909090.909 + 153509090.909X (appendix 9)
The regression equation describes that there is not positive relationship between underwriting premium and year.
Dependent variable (??): underwriting premium
Independent variable (X): year
If year = zero then ??= -306848909090.909. Here, ??= -306848909090.909 from appendix 9, which is ??, intercept. It shows that underwriting premium of SBC will be -306848909090.909 (appendix 9) if SBC’s year is zero.
Now, the value of b or slope of X is 153509090.909, it means if year is increased by 1 then the underwriting premium will increase for 153509090.909 assuming all other variables are constant.
T-test:
The calculated value of t is 11.290 from appendix 9 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and underwriting premium of SBC.
The trend of underwriting premium of SBC up to FY 2020 are given below which also indicates the consisting increasing trend.
Table: 08
Forecasted Underwriting Premium Income of SBC
Year Forecasted Net Claims Settlement
2014 2318400000
2015 2470653333
2016 2609821333
2017 2741259644
2018 2879931858
2019 3016562313
2020 3162316161
1.10 Re-Insurance Premium Income:
Re-Insurance is a process whereby one entity takes on all or part of the risk covered under a policy insured by insurance companies in consideration of a premium payment. It is a process whereby one entity (the reinsure) takes on all or part of the risk covered under a policy issued by an insurance company in consideration of premium payment. In other words, it is a form of an insurance cover for insurance companies. There are two basic method of re-insurance: Facultative Reinsurance, Treaty Reinsurance.
In respect of reinsurance, the Insurance Act provides that fifty percent of a company’s reinsurance business must be placed with the SBC and remaining fifty percent may be reinsured either with this Corporation or with any insurer in Bangladesh or abroad.
Table: 09
Re-Insurance Premium Income
Year Re-Insurance Premium Income (BDT) Net Increase % change in
Re-Insurance Income
2004 2320300000
2005 2676600000 356300000 15.36
2006 2914000000 237400000 8.87
2007 3291600000 377600000 12.96
2008 3594300000 302700000 9.20
2009 3792600000 198300000 5.52
2010 4085300000 292700000 7.72
2011 4042500000 -42800000 -1.05
2012 5816000000 1773500000 43.87
2013 6050700000 234700000 4.04
Average Growth: 11.83%
Sources: Annual Report of SBC (2005-2013)
Figure 9: Trend of Re- Insurance Premium Income
Reinsurance premium of SBC follows a consistent increasing trend except for FY 2011 where it was decreased to BDT 4042500000. But again in the following year it increased drastically to BDT 1773500000. The highest underwriting premiums were collected in FY 2013 which was BDT 6050700000. A positive grow rate of re-insurance premium income had been seen from FY 2005 to FY 2010. But in the FY 2011 a negative growth rate (-1.05 percent) was observed. But again in the FY 2012 and FY 2013 a positive growth rate of the re-insurance premium income were observed. On an average 11.83 percent increase had been found from each year to other year during the period. During the FY 2004 and FY 2005 the re-insurance income were BDT 2320300000 and BDT 2676600000 respectively that was net increase of BDT 356300000 or 15.36 percent increase from FY 2004. Like this growth rate of re-insurance premium income of FY 2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013 were 8.87 percent, 12.96 percent, 9.20 percent, 5.52 percent,7.72 percent, -1.05 percent, 43.87 percent and 4.04 percent respectively. But in FY 2011 the growth rate of re-insurance premium income was negative that was ‘ 1.05 percent due to decreased operation of private insurance companies. But it was matter of great hope that in FY 2012 and FY 2013 the growth rate of re-insurance premium income had increased at 43.87 percent and 4.04 v due to increase operation of private insurance companies.
Figure 10: Total Re-Insurance Premium Income Flow Overtime
The trend shows an upward flow of reinsurance premium income of SBC up to FY 2010 and then a little downward slope in FY 2011 but again a large upward slope for FY 2012 and FY 2013. The average growth rate was a positive growth rate (11.83 percent). So there is a huge potentiality to get more re-insurance premium income within current year and future.
1.11 Regression Model: Year and Re-Insurance premium (RP)
Here we use simple regression technique; year and number of insurance policies are considered as variables. RP is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of RP and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. RP and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.882 from appendix 10. It shows that how much dependent variable (RP) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 425707725.387 from appendix 10 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 12323571829363638000.000 from appendix 10 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 1449816539636361470.000 from appendix 10 showing the extent to which error is remaining after the regression and can be minimized with the increment of RP (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 13773388369000000000.000 (appendix 10) that comes after adding the SSR 12323571829363638000.000 and SSE 1449816539636361470.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 68.001 (appendix 10)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 68.001>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & RP.
Moreover, the p-value (sig.) = 0.000 from appendix 10 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -772412252727.273 + 386492727.273X (appendix 10)
The regression equation describes that there is not positive relationship between RP and year.
Dependent variable (??): RP
Independent variable (X): year
If year = zero then ??= -772412252727.273. Here, ??= -772412252727.273 from appendix 10, which is ??, intercept. It shows that RP of SBC will be -772412252727.273 (appendix 10) if SBC’s year is zero.
Now, the value of b or slope of X is 386492727.273, it means if year is increased by 1 then the RP will increase for 386492727.273 assuming all other variables are constant.
T-test:
The calculated value of t is 8.246 from appendix 10 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and RP of SBC.
The forecasted amount of reinsurance premium of SBC from FY 2014 to FY 2020 are given below which also indicates the consisting increasing trend.
Table: 10
Re-Insurance Premium Income
Year Forecasted Reinsurance Premium
2014 5984100000
2015 6424226667
2016 6891164000
2017 7358341156
2018 7847938240
2019 8342978081
2020 8805337976
1.12 Net Premium:
The net premium earnings of the SBC are considered separately because premium earnings of SBC are calculated by deducting the reinsurance ceded from the sum of re-insurance premium and underwriting premium. This is unique for SBC because of the Government regulation where SBC get a higher premium income by insuring the private insurance businesses. The reinsurance policy can be more effective by making the overall management system less bureaucratic and with more technical assistance (Samrath, 2001). The following table shows the breakdown of the Net Premium Income of SBC into underwriting premium, Reinsurance Premium and Reinsurance Ceded:
Table: 11
Net Premium Income
Year Underwriting Premium (BDT) Reinsurance Premium (BDT) Reinsurance Ceded (BDT) Net Premium (BDT) Net Increase Growth Rate
2004 778600000 2320300000 1435900000 1663000000
2005 886100000 2676600000 1612700000 1950000000 287000000 17.26%
2006 1044500000 2914000000 1859402079 2099097921 149097921 7.65
2007 1265800000 3291600000 1980432806 2576967194 477869273 22.77
2008 1419000000 3594300000 2190622613 2822677387 245710193 9.53
2009 1613500000 3792600000 2218370241 3187729759 365052372 12.93
2010 1659900000 4085300000 2563140508 3182059492 -5670267 -0.18
2011 1974800000 4042500000 2582981987 3434318013 252258521 7.93
2012 2189200000 5816000000 2882658815 5122541185 1688223172 49.16
2013 1909600000 6050700000 3162021015 4798278985 -324262200 -6.33
Average Growth Rate 13.41
Figure 11: Trend of Net Premium Income
Net premium of SBC follows a consistent increasing trend except for FY 2012 where it was increased to BDT 5122541185. But again in the following year it decreased to BDT 4798278985. The highest underwriting premiums were collected in FY 2012 which was BDT 5122541185. A positive grow rate of net premium income had been seen from FY 2004 to FY 2009. But in the FY 2010 a negative growth rate (-0.18 percent) was observed. But again in the following years up to FY 2012, a positive growth rate of the net premium income was observed. In FY 2013, there was a negative growth rate of -6.33 percent. On an average 13.41 percent increase had been found from each year to other year during the period. During the FY 2004 and FY 2005 the net premium income were BDT 1663000000 and BDT 1950000000 respectively that net increase was BDT 287000000 or 17.26 percent increase from FY 2004. Like this growth rate of net premium income of FY 2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013 were 7.65 percent, 22.77 percent, 9.53 percent, 12.93 percent, -0.18 percent, 7.93 percent, 49.16 percent and -6.33 percent respectively. But in FY 2010 and FY 2013 there was a negative growth rate due to less achievement of direct insurance and reinsurance premium due to due to decreased operation of national and private insurance companies. The average growth rate of net premium of SBC was 13.41 percent.
Figure 12: Flow of Net Premium Income Flow Overtime
The trend shows an upward flow of net premium income of SBC from FY 2014 to FY 2012 and then a little downward slope in FY 2013 due to less volume of insurance policy with SBC. The average growth rate was a positive growth rate (13.41 percent). But the reinsurance premium income was increased in FY 2013. If the SBC increases their activities to prompt their insurance business regarding underwriting premium, there were a huge potentiality to get more net premium income within current year and in future.
1.13 Regression Model: Year and Net Premium (NP)
Here we use simple regression technique; year and number of insurance policies are considered as variables. NP is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of NP and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. NP and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.887 from appendix 11. It shows that how much dependent variable (NP) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 385460085.833 from appendix 11 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 10649464787009716000.000 from appendix 11 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 1188635822161159940.000 from appendix 11 showing the extent to which error is remaining after the regression and can be minimized with the increment of NP (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 11838100609170876000.000 (appendix 11) that comes after adding the SSR 10649464787009716000.000 and SSE 1188635822161159940.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 71.675 (appendix 11)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 71.675>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & NP.
Moreover, the p-value (sig.) = 0.000 from appendix 11 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -718536650991.436 + 359283205.370X (appendix 11)
The regression equation describes that there is not positive relationship between NP and year.
Dependent variable (??): NP
Independent variable (X): year
If year = zero then ??= -718536650991.436. Here, ??= -718536650991.436 from appendix 11, which is ??, intercept. It shows that NP of SBC will be -718536650991.436 (appendix 11) if SBC’s year is zero.
Now, the value of b or slope of X is 359283205.370, it means if year is increased by 1 then the NP will increase for 359283205.370 assuming all other variables are constant.
T-test:
The calculated value of t is 8.466 from appendix 11 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and NP of SBC.
The trend of net premium of SBC up to FY 2020 are given below which also indicates the consisting increasing trend.
Table: 12
Forecasted Net Premium Income
Year Forecasted net premium (BDT)
2014 2318400000
2015 2470653333
2016 2609821333
2017 2741259644
2018 2879931858
2019 3016562313
2020 3162316161
1.14 Trend of Investment:
Insurance company tries to increase their wealth not only through gaining premium, but also investing that premium into different ventures that generate revenue for them. The growth of an insurance companies not only depends on the volume of insurance business in terms of `premium earnings but also depends on efficient mix of portfolio that consists of profitable investment. (Mamun 2004)
Table: 13
Investment of SBC
Year Investment (BDT) Net Increase % change in Investment
2004 826462291
2005 911938574 85476283 10.34%
2006 1029083251 117144677 12.85
2007 1140920137 111836886 10.87
2008 1259005895 118085758 10.35
2009 1459186220 200180325 15.90
2010 1834532574 375346354 25.72
2011 2405137080 570604506 31.10
2012 2773158699 368021619 15.30
2013 2804901445 31742746 1.14
Average Growth: 14.84
Sources: Annual Report of SBC (2005-2013)
Figure 13: Trend of Investment
Investment of SBC follows a consistent increasing trend through the period from FY 2004 to FY 2013.The highest amount of investment was occurred in FY 2012 which was BDT 2773158699. A positive grow rate of investment had been seen from FY 2004 to FY 2013. On an average 14.84 percent increase had been found from each year to other year during the period. During the FY 2004 and FY 2005 the net premium income were BDT 826462291 and BDT 911938574 respectively that net increase was BDT 85476283 or 10.34 percent from FY 2004. Like that growth rate of net premium income of FY 2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013 was 12.85 percent, 10.87 percent, 10.35 percent, 15.90 percent, 25.72 percent, 31.10 percent, 15.30 percent and 1.14 percent respectively.
Figure 14: Flow of Investment Overtime
The trend shows an upward flow of reinsurance premium income of SBC from FY 2004 to FY 2013. The average growth rate was a positive growth rate (14.84 percent). So there is a huge potentiality more investment within current year and in future.
1.15 Regression Model: Year and Investment
Here we use simple regression technique; year and number of insurance policies are considered as variables. Investment is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of investment and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. Investment and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.913 from appendix 12. It shows that how much dependent variable (Investment) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 226033869.954 from appendix 12 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 4847461882327409700.000 from appendix 12 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 408730482931406400.000 from appendix 12 showing the extent to which error is remaining after the regression and can be minimized with the increment of investment (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 5256192365258816500.000 (appendix 12) that comes after adding the SSR 4847461882327409700.000 and SSE 408730482931406400.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 94.878 (appendix 12)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 94.878>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & investment.
Moreover, the p-value (sig.) = 0.000 from appendix 12 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -485213289894.655 + 242398666.921X (appendix 12)
The regression equation describes that there is not positive relationship between investment and year.
Dependent variable (??): Investment
Independent variable (X): year
If year = zero then ??= -485213289894.655. Here, ??= -485213289894.655 from appendix 12, which is ??, intercept. It shows that investment of SBC will be -485213289894.655 (appendix 12) if SBC’s year is zero.
Now, the value of b or slope of X is 242398666.921, it means if year is increased by 1 then the investment will increase for 242398666.921 assuming all other variables are constant.
T-test:
The calculated value of t is 9.741 from appendix 12 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and investment of SBC.
The forecasted amount of investment of SBC from FY 2014 to FY 2020 are given below which also indicates the consisting increasing trend.
Table: 14
Forecasted Investment of SBC
Year Forecasted Investment (BDT)
2014 2977625285
2015 3292776932
2016 3613372047
2017 3934721231
2018 4242147340
2019 4517780020
2020 4758171097
1.16 Trend of Investment Income:
The general insurance business in Bangladesh earns revenues from mainly two sources. A large portion of it is acquired with the premium income and the second form is related to the income from investment. Apart from the premium income, insurance companies use the income from the investment as a source for further funding. In a broader perspective it can be said that the general insurance companies use the premiums earned from the clients as capitals for investing in productive channels to generate revenues/income. These revenues are later on used to meet the settlement of claims of clients. Henceforth it can be said that increase amount of income from investment would make it easier for insurance companies to settle claims and further reinvest in diversified portfolios. (Mamun, 2011)
The data for investment income is presented below
Table: 15
Investment Income of SBC
Year Investment Income
(BDT) Net Increase
(BDT) % change in Investment
2004 264600000
2005 363800000 99200000 37.49
2006 261378673 -102421327 -28.15
2007 326756788 65378115 25.01
2008 350925855 24169067 7.40
2009 409154452 58228597 16.59
2010 465822628 56668176 13.85
2011 637730773 171908145 36.90
2012 826529552 188798779 29.60
2013 889669405 63139853 7.64
Average Growth: 16.26
Sources: Annual Report of SBC (2005-2013)
Figure 15: Trend of Investment Income
Income from investment of SBC follows a consistent increasing trend except for the year 2006 where it was drastically decreased to 261378673. But again in the following year it drastically increased to 326756788. The highest investment income was collected in 2013 which was 889669405. A positive grow rate of investment income had been seen from FY 2004 to FY 2013 except FY 2006 which was -28.15 percent. The average growth rate of investment income was 16.26 percent. During the FY 2004 and FY 2005 the investment income was Tk. 264600000 and 363800000 respectively that net increase was TK. 99200000 or 37.49 percent from FY 2004. Like this growth rate of net premium income of FY 2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013 was -8.15 percent, 25.01 percent, 7.40 percent, 16.59 percent, 13.85 percent, 36.90 percent, 29.60 percent and 7.64 percent respectively. In FY 2006, there was a negative growth rate due to economic degradation and inflation.
Figure 16: Flow of Investment Income Overtime
The graph shows that SBC’s income from investment faced a fluctuating trend from FY 2004 to FY 2006 than follow an increasing trend throughout the fiscal years 2007-2013. The average growth rate was 16.26%. So there is a huge potentiality to achieve more investment income in current and future year.
1.17 Regression Model: Year and Investment Income (II)
Here we use simple regression technique; year and number of insurance policies are considered as variables. Investment Income (II) is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of II and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. Investment income and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.797 from appendix 13. It shows that how much dependent variable (Investment income) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 102614312.406 from appendix 13 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 381610443717113150.000 from appendix 13 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 84237576884609504.000 from appendix 13 showing the extent to which error is remaining after the regression and can be minimized with the increment of investment income (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 465848020601722620.000 (appendix 13) that comes after adding the SSR 381610443717113150.000 and SSE 84237576884609504.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 36.241 (appendix 13)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 36.241>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & investment income.
Moreover, the p-value (sig.) = 0.000 from appendix 13 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -136121712012.073 + 68011625.006X (appendix 13)
The regression equation describes that there is not positive relationship between investment income and year.
Dependent variable (??): Investment Income
Independent variable (X): year
If year = zero then ??= -136121712012.073. Here, ??= -136121712012.073 from appendix 13, which is ??, intercept. It shows that investment income of SBC will be -136121712012.073 (appendix 13) if SBC’s year is zero.
Now, the value of b or slope of X is 68011625.006, it means if year is increased by 1 then the investment income will increase for 68011625.006 assuming all other variables are constant.
T-test:
The calculated value of t is 6.020 from appendix 13 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and investment income of SBC.
The forecasted amount of investment income of SBC from FY 2014 to FY 2020 are given below which also indicates the consisting increasing trend.
Table: 16
Investment Income of SBC
Year Forecasted Investment Income (BDT)
2014 853700750.1
2015 945983175.1
2016 1062358520
2017 1159159438
2018 1258716229
2019 1349047313
2020 1430519097
1.18 Annual Profit before Tax of SBC:
Annual profit before tax (APBT) is a profitability measure that looks at a company’s profits before the company has to pay corporate income tax. This measure deducts all expenses from revenue including interest expenses and operating expenses, but it leaves out the payment of tax. APBT is important because tax expense constantly changes in a company’s profit or earnings from year to year. The annual profit before tax of SBC is given below:
Table: 17
Annual Profit before Tax of SBC
Year Annual Profits Before Tax (BDT) Net Increase
(BDT) % increase
2004 390971723
2005 406753552 15781829 4.04%
2006 420385229 13631677 3.35
2007 606074420 185689191 44.17
2008 773745384 167670964 27.67
2009 1015039513 241294129 31.19
2010 1255169437 240129924 23.66
2011 1751853145 496683708 39.57
2012 1803720135 51866990 2.96
2013 2183989902 380269767 21.08
Average Growth : 21.96
Sources: Annual Report of SBC (2005-2013)
Figure 17: Trends of Annual Profit before Tax (APBT)
Annual profit before tax of SBC follows a consistent increasing trend except FY 2007 where it was drastically increased to BDT 606074420. The highest investment income was collected in FY 2013 which was BDT 2183989902. A positive growth rate of investment income had been seen from FY 2004 to FY 2013. The average growth rate of APBT was 21.96 percent. During the FY 2004 and FY 2005 the APBT was BDT 390971723 and BDT 406753552 respectively that net increase was BDT 15781829 or 4.04 percent from FY 2004. Like this growth rate of APBT of FY 2006, FY 2007, FY 2008, FY 2009, FY 2010, FY 2011, FY 2012 and FY 2013 was 3.35 percent, 44.17 percent, 27.67 percent, 31.19 percent, 23.66 percent, 39.57 percent, 2.96 percent and 21.08 percent respectively.
Figure 18: Flows of Annual Profit Before Tax (APBT)
The trend shows an upward slope of APBT of SBC from each year to other during the period. The average growth rate was also quite significant i.e. 21.96 percent. So there is a huge potentiality to get more APBT within the current year and in future.
1.19 Regression Model: Year and Annual Profit Before Tax (APBT)
Here we use simple regression technique; year and number of insurance policies are considered as variables. Annual Profit Before Tax (APBT) is considered as dependable variable and year is independent variable. In this model, all values are provided by SPSS software. I input the total amount of APBT and year in SPSS software and have got several outputs such as variables entered, model summary, ANOVA, coefficient which are given at the appendix. APBT and year of 2014 to 2020 are considered to this statistical analysis. Thereafter, I am going to the explanation of these outputs.
Adjusted R square:
From the accepted data, the value of adjusted R square is 0.929 from appendix 14. It shows that how much dependent variable (APBT) is changed for the changing of independent variable (year).
Standard Error of estimate:
Here the value is 174961138.782 from appendix 14 that shows the amount of variability of predicted result and the actual result acquired from the real observation.
Regression Sum of Square (SSR):
SSR value comes 3661776084241863200.000 from appendix 14 showing the extent to which we are able to minimize the error through using the multiple regression tools.
Error Sum of Square (SSE):
Here Residual SSE value comes 244891200670126912.000.000 from appendix 14 showing the extent to which error is remaining after the regression and can be minimized with the increment of APBT (dependent variable).
Total Sum of Square (SST):
In this observation, the value is 3906667284911990300.000 (appendix 14) that comes after adding the SSR 3661776084241863200.000 and SSE 244891200670126912.000.000.
Degrees of Freedom (df):
Here, SST has (n-1) degrees of freedom, SSR has p (number of independent variable) degrees of freedom and SSE has (n-p-1) degrees of freedom. Hence, the mean square due to regression (MSR) is SSR divided by p and the mean sum of square due to error (MSE) is SSE divided by (n-p-1). Here, 1 is degrees of freedom for the numerator and 8 is degrees of freedom for the denominator.
F-test:
If H0 is accepted, MSR provides an unbiased estimate of ??2, and the value of MSR or MSE becomes larger. To determine how large values of MSR/MSE must be to reject H0, we make to use of the fact that if H0 is true and the assumptions about the regression model is valid, the sampling distribution of MSR/MSE is a F-distribution with p degrees of freedom in the numerator and (n-p-1) in the denominator. The summary of F-test is given below:
F=MSR/MSE= 119.621 (appendix 14)
With a level of significance ??= 0.05, the tabulated value shows that one df in the numerator and three df in the denominator, F = 5.32. With 119.621>5.32, we reject the null hypothesis and alternative hypothesis is accepted. So there is significant relationship exists between year & APBT.
Moreover, the p-value (sig.) = 0.000 from appendix 14 also indicates that we can reject H0 because the P-value is less than ??=0.05.
Regression analysis:
Regression analysis measures the nature of the relationship between dependent variable and independent variable. It describes that the relationship is positive or negative between variables.
Regression equation: ?? = a + bX
?? = -422085730458.073 + 210677869.406X (appendix 14)
The regression equation describes that there is not positive relationship between APBT and year.
Dependent variable (??): APBT
Independent variable (X): year
If year = zero then ??= -422085730458.073. Here, ??= -422085730458.073 from appendix 14, which is ??, intercept. It shows that APBT of SBC will be -422085730458.073 (appendix 14) if SBC’s year is zero.
Now, the value of b or slope of X is 210677869.406, it means if year is increased by 1 then APBT income will increase for 210677869.406 assuming all other variables are constant.
T-test:
The calculated value of t is 10.937 from appendix 14 and tabular value is 1.86 at 8 df of area 0.05 which is less than calculated value of t. Hence, the null hypothesis is rejected and alternative hypothesis is accepted and concludes that there is a positive relationship between year and APBT of SBC.
The forecasted amount of annual profit before tax of SBC from FY 2014 to FY 2020 is given below which also indicates the consisting increasing trend.
Table: 18
Forecasted Annual Profit before Tax of SBC
Year Forecasted Annual Profit Before Tax
2014 2219498526
2015 2504376899
2016 2785513324
2017 3042897875
2018 3295078158
2019 3530387914
2020 3756077822
4.0 Operational Impediments of SBC:
4.1 Survey Result:
The total number of respondents for this study was 30. These 30 respondents gave their response on the following variables:
‘ Irregular auditing of overall corporation (IAU)
‘ Insufficient Office Equipment (IOE)
‘ Lack of Research and Development (LRD)
‘ Employment Inefficiency(EI)
‘ Unavailability of Customers(UC)
‘ Poor Supervision of Damage Property or Materials Regarding Claim (PSDMP)
‘ Insufficient Manpower (IM)
‘ Lack of Training Facility(LTF)
‘ Absence of Modern Technology(AMT)
Following sections present the measurement of variables based on the response of the respondents on these variables.
Irregular Auditing of Overall Corporation (IAC):
Figure 1: Irregular auditing of Overall Corporation
From above chart it is found that on about 46.67 percent of the respondents are agreed, 3.33 percent are neutral, 46.67 percent are disagree and 3.33 percent are strongly disagreed with this variable.
Insufficient Office Equipment (IOE):
Figure 2: Insufficient office equipment
From above chart it is found that on about 6.67 percent of the respondents are strongly agree, 30 percent are agreed, 10 percent are neutral, 50 percent are disagree and 3.33 percent are strongly disagreed with this variable.
Lack of Research and Development (LRD):
Figure 3: Lack of research and development
From above chart it is found that on about 3.33 percent of the respondents are strongly agreed, 43.33 percent are agree, 33.33 percent are neutral, 16.67 percent are disagree and 3.33 percent are strongly disagreed with this variable .
Employment Inefficiency (EI):
Figure 4: Employment Inefficiency
From above chart it is found that on about 6.67 percent of the respondents are strongly agreed, 10 percent are agree, 16.67 percent are neutral, 36.67 percent are disagree and 30 percent are strongly disagreed with this variable .
Unavailability of Customers (UC):
Figure 5: Unavailability of Customers
From above chart it is found that on about 16.67 percent of the respondents are strongly agreed, 20 percent are agree, 20 percent are neutral, and 43.33 percent are disagree with this variable .
Poor Supervision of Damage Property (PSDPM):
Figure 6: poor supervision of damage property or materials regarding claim
From above chart it is found that on about 13.33 percent of the respondents are neutral, 36.67 percent are disagree and 50 percent are strongly disagreed with this variable.
Insufficient Manpower (IM)
Figure 7: Insufficient Manpower
From above chart it is found that on about 13.33 percent of the respondents are strongly agreed, 36.67 percent are agree, 16.67 percent are neutral, 13.33 percent are disagree and 20 percent are strongly disagreed with this variable .
Lack of Training Facility (LTF):
Figure 8: Lack of Training Facility
From above chart it is found that on about 20 percent of the respondents are strongly agreed, 36.67 percent are agree, 16.67 percent are neutral, 26.67 percent are disagree with this variable .
Absence of Modern Technology (AMT):
Figure 9: Absence of Modern Technology
From above chart it is found that on about 40 percent of the respondents are strongly agreed, 23.33 percent are agree, 20 percent are neutral, 16.67 percent are disagree with this variable .
4.2 Factor Analysis Result:
4.2.1 Kaiser-Meyer Olkin Measure of Sampling Adequacy (KMO):
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .769
Bartlett’s Test of Sphericity Approx. Chi-Square 81.282
Df 21
Sig. .000
The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is an index used to examine the appropriateness of factor analysis. High value (between 0.5 and 1.0) indicates the factor analysis is appropriate.(Maihotra,2008)
In order to measure the appropriateness of the factor analysis, Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was examined. The value of KMO was 0.769, which is an indication of sampling adequacy and thus the appropriateness of the factor analysis.
The Bartlett’s test of sphericity test the adequacy of the correlation matrix and yielded a value of 81.282 and an associated level of significance smaller than 0.01. Thus the hypothesis that the correlation matrix is an identity matrix can be rejected, i.e. the correlation matrix has significant correlations among at least some of the variables.
4.2.2 Correlation Matrix Analysis:
The variables must be correlated for the factor analysis to be appropriate. If the selected variables have poor correlation between them, then the resulting factors would not mean anything significant other than the reproduction of the original variables. From the correlation matrix (Appendix 02), it is found that significant correlation exist between several variables. The variable IOE is highly correlated (0.552) with the variable LRD. High level of correlation (0.551) also exists between variables IOE and UC, between IOE and LTF (0.507). The variable LRD is highly correlated (.552) with the variable IOE. High level of correlation (0.481) also exists between variables LRD and LTF, between LRD and AMT (0.470). The variable EI is highly correlated (0.557) with the variable PSDPM. High level of correlation (0.493) also exists between variables EI and LTF. The variable UC is highly correlated (0.551) with the variable IOE. The variable PSDPM is highly correlated (0.557) with the variable EI. The variable IM is highly correlated (0.669) with the variable LTF, high correlation (0.716) also exists between IM and AMT. The variable LTF is highly correlated (0.507) with the variable IOE. High level of correlation (0.481) also exists between LTF and LRD, and between LTF and EI (0.493), and between LTF and IM (0.669), the variable LTF is highly correlated (0.603) with the variable AMT. The variable AMT is highly correlated (0.470) with the variable LRD and highly correlated (0.716) exists between AMT and IM, The variable AMT is highly correlated (0.603) with the variable LTF.
4.2.3 Anti-Image Correlation Matrix Analysis:
Anti image correlation matrix is used to measure the sampling adequacy level and it implies that variables that has anti image correlation below the acceptable level (0.5) can be excluded from the factor analysis. The anti-image correlation matrix (Appendix 03) shows that the variables IAC and PSDPM have anti image correlations which are 0.428, 0.461 respectively. Since these anti image correlation values are bellow the accepted level (0.5), these variables are excluded from factor analysis.
4.2.4 Determination of the Number of Factors:
In this approach, only one factor with eigenvalue greater than 1 .0 is retained; the other factors are not included in the model. An eigenvalue represents the amount of variance associated with the factor. Hence, only one factor with a variance greater than 1.0 is included. Factors with variance less than 1.0 are no better than a single variable, because due to standardization, each variable has a variance of 1.0. (Malhotra, 2008).
Total Variance Explained
Component Extraction Sums of Squared Loadings
Total % of Variance Cumulative %
1 3.713 53.049 53.049
Extraction Method: Principal Component Analysis.
The Total Variance Explained section presents the number of common factors extracted, the eigenvalues associated with these factors, the percentage of total variance accounted for by each factor, and the cumulative percentage of total variance accounted for the factors. Using the criterion of retaining factors with eigenvalues of 1 or greater, one factor was retained. This factor was accounted for 53.049 percent, of the total variance respectively, for a total of 53.049 percent.
4.2.5 Component Matrix:
Component Matrixa
Component
1
Inefficient of Office Equipment .745
Lack of Research and Development .657
Employment Inefficiency .531
Unavailability of Customer .689
Insufficient Manpower .777
Lack of Training Facility .838
Absence of Modern Technology .815
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
In the component matrix, one factor is found which is comprised of Insufficient office equipment, lack of research and development, employment inefficiency, unavailability of customer, insufficient manpower, lack of training facility, absence of modern technology. Here inefficient of office equipment explained 74.5 percent, lack of research and development explained 65.70 percent, employment inefficiency explained 53.10 percent, unavailability of customer explained 68.90 percent, insufficient manpower explained 77.70 percent, lack of training facility explained 83.80 percent, and the absence of modern technology explained 81.50 percent.
4.2.6 Rotate Factors:
Since only one factor is retained from the total variance Explained, so the rotate factor cannot be suitable.
The following table is a part of the outcome of factor analysis-‘Total Variance Explained’.
Factor Name of the Factor Rotation Sums of Squared Loadings
Eigen Value % of Variance Cumulative %
1 Internal Organizational problems 3.713 53.049 53.049
Extraction Method: Principal Component Analysis.
The table shows that the eigenvalue of Internal Organizational problems explains 53.049 percent of variances. The result of the factor analysis shows that these factors collectively produce at about 53.049 percent or approximately 54 percent variance in the data set.
Recommendations:
1. It was observed that about 30 percent respondents are agreed and 50 percent respondents were disagreed with the ‘Insufficient Office Equipment’. It has been found that the regional office and the main branches have sufficient office equipment but the branches which are situated in the remote areas and they have strongly feeling the shortage of office equipment. The higher authority should take proper steps to fulfill the insufficient office equipment problem.
2. Proper research & development is necessary to any organization to achieve their vision. It was observed that about 43.33 percent respondents were agreed with ‘Lack of Research and Development’. For the improvement of research and development, the corporation can make an extensive research and helps other institutions to do those types of research by providing all the available information freely and timely that will be helpful to make an innovation of this field of business.
3. The ‘Employment Inefficiency’ and ‘Lack of Training Facility’ are so strongly obvious to the SBC. Here about 16.67 percent and 56.67 percent respondents were agreed with this variable respectively. To remove the problem the higher authority can also appoint the skilled person and day by day. Another important factor is training. Training facility is provided by the SBC but this is not sufficient for all employees. For the lack of proper training facility the employees are becoming unskilled day by day. By giving proper training facility the higher authority can develop the skill of the employees who are engaged with the SBC.
4. Customers are the main factors for any organization. SBC doesn’t have as many customers as they should have.16.67 percent and 20 percent of the respondents were strongly agreed and agreed with this variable respectively. The main factors are the lack of proper advertisement and the commission discrimination. According to the role of IDRA private insurance companies provide commission to their clients who are insured their properties but such kinds of commission do not provide the employees who are engaged with SBC. So the authority should go to the mass media to inform all level of people about the advantage of insurance and in the mean time they also should have to encourage their employees by providing a reasonable commission. SBC should evaluate customer’s needs from their perspective.
5. ‘Insufficient Manpower’ problem is another hindrance to the development of SBC. It was found that 13.33 percent and 36.67 percent of the respondents were strongly agreed and agree with this variable. Without sufficient manpower any organization cannot run smoothly. Sufficient manpower is the hardcore to any organization. Although sufficient manpower is available in the regional office but the remote branches of SBC doesn’t have that facility. For this reason the branches are operated their operational activities lamely. To overcome the problem the higher authority can decentralize the overall manpower that are engaged with SBC, and appoint sufficient employees.
6. About 40 percent respondents were strongly agreed with the factor ‘Absence of Modern Technology’. In the age of globalization without engaged with modern technology, any organization cannot achieve their goal. This research discovered that SBC didn’t comply with modern technology and even in the regional office they were still using type machine for typing any documents. And the modern internet connection is not available where private insurance companies are engaged with modern technology. As a government institution of SBC the government should take attention for engagement of modern technology to survive the competition with other insurance. The insurance companies can offer to its customer better service if all of its departments are computerized and incorporated under Local Area Network (LAN).
Conclusion:
The overall prospects are bright in future, since there profit sequence from FY 2005 to FY 2013 is increasing day by day. Although the total number of insurances, total assets, claim settlement procedure were fluctuating from year to year, the average growth rate of all the mentioned parameters was positive. If SBC takes necessary steps to overcome internal organizational impediments, the prospects of SBC will be brighter in future.
References:
Hossain S.M(2013) ‘Inquest of Positive Image of Insurance Industry in Bangladesh’, Insurance Journal, Bangladesh Insurance Academy, Vol-59, Dec., p. 41-49.
Bhuiyan M.A.H (2011) ‘Islamic Management Practices in Islamic Life Insurance Companies of Bangladesh’ Insurance journal, Bangladesh Insurance Academy, vol-58, Dec, p. 69-84.
Hossain S.M.I, 2011 ‘The Essence of Positive Attitude in Insurance salesmanship’, Insurance Journal, Bangladesh Insurance Academy, Vol-58, July, p.85-97.
Azam M.S, 2005, ‘Customers’ Attitude towards General Insurance Service: Contrasting the Public and Private Sectors in Bangladesh’, Insurance Journal, Bangladesh Insurance Academy, Vol-56, July, p. 91-110.
Ahmed I, ‘Automation of General Insurance Companies in Bangladesh Insurance’, Journal, Bangladesh Insurance Academy, July,Vol-56, p.85-90.
Haque A.B.M.N2006, ‘Need for Professionalism in Insurance Sector’ Insurance Journal, Bangladesh Insurance Academy, Vol-57,July,p.37-49
Azam, M.S (2004), ‘Problems and Prospects of Service Marketing Through State-Owned Enterprises (SOE)-A
case study of Sadharan Bima Corporation’, ADP Research Report, Faculty of Business Studies, University of
Rajshahi, Rajshahi, August, p.1-10.
Abhijit, B, Mamun, M.Z and Nazrul , I (2000), ‘Performance of the Nationalizd General Insurance Company of
Bangladesh’, Bank Parikrama, Volxxv,No.4,Dhaka,BIBM, December,p.28.
Azad, A.K. (2001), ‘Does Insurance Contribution significantly to the Capital Market Development in
Bangladesh’? Bank Parikrama, Quarterly Journal of BIBM, vol ‘xxvi, Nos 3&4 , Dhaka, Sept&Dec, p.126.
Bhuiyan, B.A, Ali, K.M.M, Nurnnabi, M.A.A and Ahmed ,T. (2003). ‘A study of Relationships among Job
Design , Motivation and Performances of the Employees in the Insurance Sector of Bangladesh’, Insurance
Journal , Vol 54 (July).
Bhuiyan, B.A (2001), ‘Innovations in Insurance Operations : Bangladesh Perspective , An alternative
Development Approach , ‘Insurance Journal , Bangladesh Insurance Academy, vol, 52, Dhaka : July, p.76.
APPENDIXES
Appendix 1
Variables Description of the Variables
IAC Irregular Auditing of Overall Corporation
IOE Insufficient Office Equipment
LRD Lack of Research and Development
EI Employment Inefficiency
UC Unavailability of Customers
PSDPM Poor Supervision of Damage Property Materials
IM Insufficient Manpower
LTF Lack of Training Facility
AMT Absence of Modern Technology
Appendix 2
Appendix 3
inefficient of office equipment lack of research and development employment inefficiency unavailability of customer insufficient manpower lack of training facility absence of modern technology
Reproduced Correlation
inefficient of office equipment .555a .489 .396 .514 .579 .625 .607
lack of research and development .489 .431a .349 .453 .510a .550 .535
Employment inefficiency .396 .349 .282a .366 .412 .445 .433
unavailability of customer .514 .453 .366 .475a .536 .578 .562
insufficient manpower .579 .510 .412 .536 .604a .651 .633
lack of training facility .625 .550 .445 .578 .651 .702a .683
absence of modern technology .607 .535 .433 .562 .633 .683 .664a
Residualb
inefficient of office equipment .063 -.057 .037 -.184 -.117 -.155
lack of research and development .063 -.258 -.150 -.089 -.069 -.065
employment inefficiency -.057 -.258 .012 -.199 .048 -.077
unavailabity of customer .037 -.150 .012 -.129 -.133 -.105
insufficient manpower -.184 -.089 -.199 -.129 .018 .083
lack of training facility -.117 -.069 .048 -.133 .018 -.080
absence of modern technology -.155 -.065 -.077 -.105 .083 -.080
Appendix 4: Reproduced Correlation
Extraction Method: Principle Component Analysis
Extraction Method: Principle Component Analysis
a. Reproduced Communications.
b. Residuals are computed between observed and reproduced correlation. There are 17 (80.0%) nonredunandant residents with absolute value greater than 0.05.
Appendix 5
Questionnaire
Dear respondent
I am a student of BBA, Department of Finance and banking, Jatiya Kabi Kazi Nazrul Islam University ,Trishal, Mymensingh. For my academic purpose I require data regarding ‘Operational Impediments of Sadharan Bima Corporation’. It would be highly appreciable if you provide me some necessary information of your business regarding this issue. I also assure you that the data you will provide will remain strictly confidential.
Part 1: personal information:
1. Name:
2. Gender: Male Female
3. Age range: 20-25 years 26-30 years 31-35 years 36-40 years 41or more 4. Academic qualification: SSC HSC Graduate Post graduate
Part 2: Measuring variable:
Please tick ( ) anyone of the answer options for each one of the following factors which you might consider as constrains at the time of working with ‘Sadharan Bima Corporation’. There is no right or wrong answer to these questions. Please just give your opinion.
Strongly disagree
(1) Disagree
(2) Neutral
(3) Agree
(4) strongly agree
(5)
Irregular auditing of overall corporation
Insufficient office equipment
Lack of research and development
Employment inefficiency
Unavailability of customers
Poor supervision of damage property or materials regarding claim
Insufficient manpower
Lack of training facility
Absence of modern technology
‘Thanks for your cordial participation’
Appendix 6:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: Assets
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .978a .957 .952 972558253.364
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 169264894055203800000.000 1 169264894055203800000.000 178.952 .000b
Residual 7566956449486699500.000 8 945869556185837440.000
Total 176831850504690500000.000 9
a. Dependent Variable: Assets
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -2866581005050.527 215060637880.320 -13.329 .000
Year 1432374142.994 107075139.643 .978 13.377 .000
a. Dependent Variable: Assets
Appendix 7:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: noofinsurancepolicy
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .832a .693 .654 16653.742
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 4998013017.648 1 4998013017.648 18.021 .003b
Residual 2218776880.352 8 277347110.044
Total 7216789898.000 9
a. Dependent Variable: noofinsurancepolicy
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
1 (Constant) -15562850.109 3682621.873 -4.226 .003
Year 7783.442 1833.517 .832 4.245 .003
a. Dependent Variable: no. of insurance policy
Appendix 8:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: Netclaims
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .872a .760 .731 226111577.325
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 1298718285878566910.000 1 1298718285878566910.000 25.402 .001b
Residual 409011563204533500.000 8 51126445400566688.000
Total 1707729849083100420.000 9
a. Dependent Variable: Netclaims
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -250721139095.091 49999781384.339 -5.014 .001
Year 125467285.891 24894065.351 .872 5.040 .001
a. Dependent Variable: Netclaims
Appendix 9:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: UnderwrittingPremium
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .970a .941 .934 123497053.701
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 1944115881818181630.000 1 1944115881818181630.000 127.470 .000b
Residual 122012178181818096.000 8 15251522272727262.000
Total 2066128059999999740.000 9
a. Dependent Variable: UnderwrittingPremium
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -306848909090.909 27308755083.134 -11.236 .000
Year 153509090.909 13596578.122 .970 11.290 .000
a. Dependent Variable: UnderwrittingPremium
Appendix 10:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: RP
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .946a .895 .882 425707725.387
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 12323571829363638000.000 1 12323571829363638000.000 68.001 .000b
Residual 1449816539636361470.000 8 181227067454545184.000
Total 13773388369000000000.000 9
a. Dependent Variable: RP
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -772412252727.273 94136237758.260 -8.205 .000
Year 386492727.273 46868878.019 .946 8.246 .000
a. Dependent Variable: RP
Appendix 11:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: netpremium
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .948a .900 .887 385460085.833
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 10649464787009716000.000 1 10649464787009716000.000 71.675 .000b
Residual 1188635822161159940.000 8 148579477770144992.000
Total 11838100609170876000.000 9
a. Dependent Variable: netpremium
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
1 (Constant) -718536650991.436 85236325587.565 -8.430 .000
Year 359283205.370 42437758.740 .948 8.466 .000
a. Dependent Variable: netpremium
Appendix 12:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: Investment
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .960a .922 .913 226033869.954
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 4847461882327409700.000 1 4847461882327409700.000 94.878 .000b
Residual 408730482931406400.000 8 51091310366425800.000
Total 5256192365258816500.000 9
a. Dependent Variable: Investment
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
1 (Constant) -485213289894.655 49982598046.708 -9.708 .000
Year 242398666.921 24885510.051 .960 9.741 .000
a. Dependent Variable: Investment
Appendix 13:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: investmenttincome
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .905a .819 .797 102614312.406
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 381610443717113150.000 1 381610443717113150.000 36.241 .000b
Residual 84237576884609504.000 8 10529697110576188.000
Total 465848020601722620.000 9
a. Dependent Variable: investmentincome
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -136121712012.073 22690979594.672 -5.999 .000
Year 68011625.006 11297463.974 .905 6.020 .000
a. Dependent Variable: investmentincome
Appendix 14:
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 yearb . Enter
a. Dependent Variable: APBT
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .968a .937 .929 174961138.782
a. Predictors: (Constant), year
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 3661776084241863200.000 1 3661776084241863200.000 119.621 .000b
Residual 244891200670126912.000 8 30611400083765864.000
Total 3906667284911990300.000 9
a. Dependent Variable: APBT
b. Predictors: (Constant), year
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -422085730458.073 38688946374.685 -10.910 .000
Year 210677869.406 19262587.410 .968 10.937 .000
a. Dependent Variable: APBT

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