Forecasting performance of Indian non-life insurance industry - an analysis of underwriting experience of public and private insurance companies
a*Assistant Professor; IT & Systems, T. A. Pai Management Institute, Manipal, Karnataka, India. Corresponding author's email address: subhabaha@tapmi.edu.in
bAssociate Professor; Finance T. A. Pai Management Institute, Manipal, Karnataka, India. kaushik07@tapmi.edu.in
cHonorary Visiting Professor, Indian Statistical Institute, Kolkata, India and Formerly Dean-Post Graduate Studies, Bidhan Chandra Krishi Viswavidyalaya, India. satyabrata.pal@hotmail.com
ABSTRACT
This paper is an initial attempt towards the direction of analysis of performances of Indian public and private non-life insurance companies and towards forecasting of future growth potential of the sector. This paper also presents future prospects and growth potentiality of the non-life insurance sector in India along with the current state of the underwriting cycle prevalent in the Indian non-life insurance industry. This paper presents a detailed analysis of the underwriting performance of the non-life insurance sector as a whole during the post-liberalization period of 2000-2001 to 2014-2015. It depicts a colorful prospect for the Indian non-life insurance industry in the next 10 years. This paper has also been devoted to the determination of the presence of an underwriting cycle (pattern) that exists in the performances of the non-life insurance companies (public and private) during the last 15 years.
© 2017 AESS Publications. All Rights Reserved.
Keywords: Non-life Insurance, combined ratio, underwriting cycle, damped trend exponential smoothing model, forecasting premium data
ARTICLE HISTORY: Received: 10-Feb-2017, Accepted: 08-Mar-2017, Online available: 25-Mar-2017
Contribution/ Originality
The article entitled 'Forecasting Performance of Indian Non-life Insurance Industry - An Analysis of Underwriting Experience of Public and Private Insurance Companies' discusses performance of the Indian non-life insurance industry in the last decade after liberalization and future growth potential of the sector. This is an initial attempt towards this direction in the Indian perspective and it tries to identify the pattern of underwriting cycle existing in the Indian non-life insurance industry after liberalization along with forecasting future growth potential of the Indian non-life sector. This article is first such contribution in the study related to the Indian non-life insurance industry.
1. INTRODUCTION
Bedi and Singh (2011) mentions that the liberalization of the insurance market through the Insurance Regulatory and Development Act (IRDA), 1999 paved the way for entry of private insurance players bringing in immense competition in the Indian non-life insurance sector. Starting journey in the non-life insurance market from the financial year 2000-2001, the 23 private non-life insurers present today have captured around 40 percent of the insurance market in terms of the net premium income (IRDA Indian Insurance Statistics 2014-15) (As per Indian Insurance Statistics Handbook, 2014). The importance of the non-life insurance sector can be understood from the fact that is mentioned in Indian Insurance Statistics Handbook (2014). The net premium income of the non-life sector is equivalent to 70 thousand Crore Indian Rupee (both public and private non-life insurers combined). The number of non-life insurance policies issued to individuals/corporates/groups in the financial year 2014-2015 stands to 13 crore (IRDA Statistics). As per the IBEF report, the Indian non-life insurance industry is 78000 crore INR premium income industry and it is growing at a pace of 17-18% per annum. As per IBEF report, the Indian insurance market is having a huge opportunity waiting to be harnessed as India currently is accounting for less than 1.5 percent of the World's total insurance premiums and about 2 percent of the world's life insurance premiums despite being the second most populous country in the world.
Rejda (1999) tells about underwriting cycle in non-life insurance. According to Rejda (1999), there exists a cyclical pattern in the underwriting results following which the property and casualty insurance and re-insurance premium, the profits and availability of coverage rise and fall over time. Property and liability Insurance markets fluctuate between the periods of tight underwriting standards and high premiums, called 'hard' insurance market, and periods of loose underwriting standards and low premiums, called 'soft' insurance market in the US market. As per Rejda (1999), these market conditions are direct or indirect effects of certain economic conditions. According to [3], in the US study, it was found that during the periods - 1956-1958, 1964-1966, 1972-1975, 1984-1988, 1992-1994, 2001-2003, the insurance market was hard while during the remaining part there was a soft insurance market.
Aquino (2008) made a time-series study as well as regression analysis study on different parameters, like, direct premium level, GDP, underwriting profit, operating profit and total investment return etc. based on property and casualty insurance data during the period 1984 to 2006 in order to identify the underwriting cycle pattern prevalent in the US market. Yu-na et al. (2009) worked on Chinese market Non-life insurance data for the period 1980 to 2006 to determine the presence of the underwriting cycle and concluded that there was a long cycle of 12.5-16.7 years and middle cycle was 5.6 years. Wang et al. (2010) did some work on modelling underwriting cycle pattern in the US property and casualty Insurance market.In the Indian perspective, Pal and Mazumdar (2013) made a similar study based on the performance of the General Insurance Corporation of India, which is the largest and the only non-life re-insurer in India. The study indicated the existence of two cycles, each consisting of a six-year phase (1996-1997 to 2002-2003; 2002-2003 to 2008- 2009). Pal and Mazumdar (2013) also made a detailed analysis of the GIC underwriting performance and made forecast on the future performance of GIC in the coming years. Pal and Mazumdar (2014) studied the performance of the public non-life insurance companies during the period 2000-2001 to 2011-2012 and concluded presence of underwriting cycles during the decade.
This paper is an effort to investigate the performances of the non-life insurers during the period 2000-2001 to 2014-2015 and also to forecast the performance of non-life insurers more precisely with revised and accurate models in the next 10 years till 2025-2026. The current state of the underwriting cycle in the Indian non-life insurance market has also been studied in this paper.
2. MATERIALS AND METHODS
As per Rejda (1999), combined ratio is an important measure which is used to understand the underwriting cycle pattern. Combined ratio is the ratio of paid losses and loss adjustment expenses plus underwriting expenses to the premium income.
If the value of the combined ratio is less than 1, it is said that the underwriting operations are profitable. For example, if the combined ratio is 1.08 (or 108 after multiplying by 100), then for every INR 100 collected as premium, INR 108 is paid by the insurer as the expenses and the claims.
Premium Income - The revenue earned by the insurance company as premium in a particular financial year.
Incurred Claims-The claim is the expenses made by the insurer to meet up insure loss.
Operating Expenses - The operating expense are made by the insurer for running the insurance business. The operating expense includes office rent, expenditure on printing stationaries and other office expenses in the paper, major financial calculation has been made in terms of Lakh and Crore INR. Lakh and Crore are Indian measure and One Lakh means One Hundred Thousand and One Crore mean 10 million.
The below-mentioned data have been considered for analysis in the current study -
- Net Premium Income from 2000-2001 to 2014-2015 (Public Insurance Companies, Private Insurance Companies, Together)
- Incurred Claims from 2000-2001 to 2014-2015 (Public Insurance Companies, Private Insurance Companies, Together)
- Operating Expense from 2000-2001 to 2014-2015 (Public Insurance Companies, Private Insurance Companies, Together)
Regression Equation 1: Y_t=A+B.X_t where Yt = Claims at time-point t, Xt = Premium at time point t, A = intercept of the linear equation and B = Slope of the linear equation.
Regression Equation 2: Y_t=A+B.X_t where Yt = Expenses at time-point t, Xt = Premium at time point t, A = intercept of the linear equation and B = Slope of the linear equation.
The values of A and B are estimated for the best fit through the least square technique for both the regression equation for all public, private and combined data. So 6 regression equations are obtained.
The time-series analysis has been done on the premium income (non-life public insurers, non-life private insurers and both combined together). From the forecast values of net premiums till 2025-2026 financial year, the corresponding incurred claims as well as operating expenses are determined using the best fitted linear equations which are estimated through a least square technique. The future performances of the non-life insurance sectors are determined based on the forecast results using the formulae mentioned in the just preceding paragraph.
2.1. Data source
The data is collected from the IRDA annual reports as well as IRDA Indian Insurance Statistics Hand-book 2014-2015. The combined ratio has been calculated from the above data.
3. RESULTS AND DISCUSSION
The following Figure gives a glimpse of the combined ratio for the underwriting operations did in the public non-life insurance companies, the private non-life insurance companies as well as the combined performance of both.
Figure 1: Combined ratio plot (Public, Private and Combined)
Two complete underwriting cycles are distinctly visible in all 3 categories - public non-life insurers, private non-life insurers and combined non-life insurers. For public non-life insurers, the cycles are: 2001-2002 to 2007-2008 and 2008-2009 to 2014-2015 (6 years and 6 years respectively). For private non-life insurers, the cycles are: 2000-2001 to 2007-2008 and 2008-2009 to 2014-2015 (7 years and 6 years respectively). For the combined non-life insurers' performance, the cycles are: 2001-2002 to 2006-2007 and 2007-2008 to 2014-2015 (5 years and 7 years respectively).
The linear regression equation of incurred claims data on premium income gives the following results (Table 1).
Table 1: Linear regression fit of incurred claims on premium income
| Non-life Public Insurers | Non-life Private Insurers | Combined | |
| Linear Equation | Y = 988.13 + 0.7667 X | Y =-343.516 + 0.7527 X | Y = 769.115+0.7591 X |
| R-Square | 0.994 | 0.995 | 0.997 |
Y - Incurred Claims; X - Premium Income
The linear regression equation of incurred claims data on premium income gives the following results (Table -2).
Table 2: Linear regression fit of operating expenses on premium income
| Non-life Public Insurers | Non-life Private Insurers | Combined | |
| Linear Equation | Y = -20.5613 + 0.3263 X | Y = 117.138 + 0.2753 X | Y = 706.542 + 0.2931 X |
| R-Square | 0.94 | 0.991 | 0.997 |
Y - Operating Expenses; X - Premium Income
The forecasting is done on the net premium data of the public non-life insurers, private non-life insurers and combined data on non-life insurers.
3.1. Public premium forecast
Figure 2: Log damped trend exponential smoothing model fit on public premium data
R-Square = 0.99
Table 3: Public non-life insurers' performance forecast results
| Year | Net Premium (in Crores INR) | Claims (in Crores INR) | Operating Expenses (in Crores INR) | Combined Ratio |
| 2000 | 8085.76 | 7020.36 | 2165.11 | 113.6006 |
| 2001 | 8748.59 | 7888.5 | 2643.58 | 120.386 |
| 2002 | 9473.22 | 7691.18 | 3100.15 | 113.9141 |
| 2003 | 10207.16 | 8253.3 | 4051.54 | 120.5511 |
| 2004 | 10949.93 | 9075.39 | 4221.12 | 121.4301 |
| 2005 | 11832.14 | 10569.85 | 4701.12 | 129.0635 |
| 2006 | 12928.6 | 10538.75 | 4279.061 | 114.6126 |
| 2007 | 13692.14 | 12124.81 | 4584.055 | 122.0325 |
| 2008 | 15767.18 | 13637.78 | 5526.67 | 121.5465 |
| 2009 | 17971.11 | 14967.23 | 6529.58 | 119.6187 |
| 2010 | 22310.46 | 19599.15 | 8150.15 | 124.378 |
| 2011 | 27740.23 | 22242.06 | 8503.76 | 110.8348 |
| 2012 | 31265.4 | 25061.37 | 9637.52 | 110.9818 |
| 2013 | 36172.51 | 27817.95 | 10997.51 | 107.3065 |
| 2014 | 40123.96 | 31567.75 | 13479.57 | 112.2704 |
| Premium Forecast | Forecasted Claims (Calculated from Fitted Model based on Premium) | Forecasted Operating Expenses (Calculated from Fitted Model based on Premium) | Forecasted Combined Ratio (Calculated from Forecasted Premium, Forecasted Claims as well as Forecasted Operating Expenses) | |
| 2015 | 44723 | 35277.25 | 14572.55 | 111.4635 |
| 2016 | 49630 | 39039.45 | 16173.71 | 111.2496 |
| 2017 | 55102 | 43234.83 | 17959.22 | 111.056 |
| 2018 | 61281 | 47972.27 | 19975.43 | 110.8789 |
| 2019 | 68347 | 53389.77 | 22281.06 | 110.7157 |
| 2020 | 76520 | 59656.01 | 24947.91 | 110.5645 |
| 2021 | 86081 | 66986.43 | 28067.67 | 110.424 |
| 2022 | 97381 | 75650.14 | 31754.86 | 110.2936 |
| 2023 | 110870 | 85992.16 | 36156.32 | 110.1727 |
| 2024 | 127124 | 98454.1 | 41460 | 110.0611 |
| 2025 | 146890 | 113608.7 | 47909.65 | 109.9587 |
| 2026 | 171141 | 132201.9 | 55822.75 | 109.8654 |
The log damped trend exponential smoothing has been fitted in the premium data for public non-life insurance companies and from the R-square value available from the fit, it is evident that the model has given a good fit. From the forecast value available, it is evident that the non-life premium income for the public insurers will surpass 1 Lakh Crore INR in around year 2022/2023. During the early years after liberalization of non-life insurance industry, the public insurance companies use to run with high combined ratio (they need to pay more than 120 INR for claims as well as operating expenses in order to earn 100 INR as premium). However, as seen from the forecasted results, the public insurance companies are gradually adapting to new competitive market drastically reducing different cost and so in the coming years the combined ratio will stay around 110 (they will be paying INR 110 as claims and operating expenses in order to earn 100 INR).
3.2. Private premium forecast
Figure 3:Damped trend exponential smoothing model fit on private premium data
R-Square = 0.99
Table 4: Private non-life insurers' performance forecast results
| Year | Net Premium (in Crores INR) | Claims (in Crores INR) | Operating Expenses (in Crores INR) | Combined Ratio |
| 2000 | 0.44 | 0.02 | 0.249 | 61.181 |
| 2001 | 183.33 | 42.24 | 127.25 | 92.450 |
| 2002 | 561.45 | 292.25 | 197.65 | 87.256 |
| 2003 | 1066.03 | 543.36 | 296.17 | 78.752 |
| 2004 | 1782.01 | 911.73 | 468.87 | 77.474 |
| 2005 | 2842.27 | 1548.22 | 777.4 | 81.822 |
| 2006 | 4673.164 | 2502.89 | 1283.37 | 81.021 |
| 2007 | 7158.713 | 4246.31 | 2231.78 | 90.492 |
| 2008 | 8511.99 | 6079.16 | 2927.15 | 105.807 |
| 2009 | 9945.94 | 7307.25 | 3139.76 | 105.037 |
| 2010 | 12622.35 | 9937.31 | 3917.07 | 109.760 |
| 2011 | 16710.77 | 12755.79 | 4702.85 | 104.475 |
| 2012 | 20841.56 | 14562.24 | 5584.3 | 96.665 |
| 2013 | 24115.63 | 17874.11 | 6395.28 | 100.637 |
| 2014 | 26081.37 | 19430.46 | 7448.57 | 103.058 |
| Premium Forecast | Forecasted Claims (Calculated from Fitted Model based on Premium) | Forecasted Operating Expenses (Calculated from Fitted Model based on Premium) | Forecasted Combined Ratio (Calculated from Forecasted Premium, Forecasted Claims as well as Forecasted Operating Expenses) | |
| 2015 | 27982 | 20718.54 | 7820.583 | 101.991 |
| 2016 | 29814 | 22097.48 | 8324.932 | 102.040 |
| 2017 | 31583 | 23429.01 | 8811.938 | 102.083 |
| 2018 | 33291 | 24714.62 | 9282.15 | 102.120 |
| 2019 | 34939 | 25955.07 | 9735.845 | 102.152 |
| 2020 | 36530 | 27152.62 | 10173.85 | 102.180 |
| 2021 | 38065 | 28308.01 | 10596.43 | 102.205 |
| 2022 | 39547 | 29423.51 | 11004.43 | 102.227 |
| 2023 | 40978 | 30500.62 | 11398.38 | 102.247 |
| 2024 | 42358 | 31539.35 | 11778.3 | 102.265 |
| 2025 | 43691 | 32542.7 | 12145.27 | 102.281 |
| 2026 | 44977 | 33510.67 | 12499.31 | 102.296 |
The damped trend exponential smoothing has been fitted in the premium data for private non-life insurance companies and from the R-square value available from the fit, it is evident that the model has given a good fit. As visible from the forecasted results, the private non-life insurance companies will be growing in good pace, however, growth will be less than the public non-life insurance companies in terms of premium revenue. In the initial years, the combined ratio for private non-life insurance companies use to show huge variability over years but in the coming years the combined ratio will be around 102 (i.e., in order to earn 100 INR as premium income, they need to spend 102 INR only). It means most of the private insurance companies will run in profit and only some will be facing loss.
3.3. Total premium forecast
Figure 4: Log damped trend exponential smoothing fit on combined data
R-Square = 0.99
Table 5: Combined (public & private) non-life insurers' performance forecast results
| Year | Net Premium (in Crores INR) | Claims (in Crores INR) | Operating Expenses (in Crores INR) | Combined Ratio |
| 2000 | 8086.2 | 6907.349 | 2165.359 | 113.597 |
| 2001 | 8931.92 | 7549.335 | 2770.83 | 119.812 |
| 2002 | 10034.67 | 8386.433 | 3297.8 | 112.422 |
| 2003 | 11273.19 | 9326.594 | 4347.71 | 116.598 |
| 2004 | 12731.94 | 10433.93 | 4689.99 | 115.277 |
| 2005 | 14674.41 | 11908.46 | 5478.52 | 119.913 |
| 2006 | 17601.77 | 14130.61 | 5562.431 | 105.694 |
| 2007 | 20850.86 | 16597 | 6815.835 | 111.203 |
| 2008 | 24279.17 | 19199.43 | 8453.82 | 116.028 |
| 2009 | 27917.05 | 21960.95 | 9669.34 | 114.424 |
| 2010 | 34932.81 | 27286.61 | 12067.22 | 119.096 |
| 2011 | 44451 | 34511.87 | 13206.61 | 108.444 |
| 2012 | 52106.96 | 40323.51 | 15221.82 | 105.255 |
| 2013 | 60288.14 | 46533.84 | 17392.79 | 104.638 |
| 2014 | 66205.33 | 51025.58 | 20928.14 | 108.641 |
| Premium Forecast | Forecasted Claims (Calculated from Fitted Model based on Premium) | Forecasted Operating Expenses (Calculated from Fitted Model based on Premium) | Forecasted Combined Ratio (Calculated from Forecasted Premium, Forecasted Claims as well as Forecasted Operating Expenses) | |
| 2015 | 72583 | 55866.87 | 21980.62 | 107.253 |
| 2016 | 79558 | 61161.59 | 24024.99 | 107.074 |
| 2017 | 87318 | 67052.21 | 26299.45 | 106.91 |
| 2018 | 96092 | 73712.55 | 28871.11 | 106.755 |
| 2019 | 106164 | 81358.21 | 31823.21 | 106.61 |
| 2020 | 117889 | 90258.65 | 35259.81 | 106.471 |
| 2021 | 131711 | 100750.9 | 39311.04 | 106.340 |
| 2022 | 148197 | 113265.5 | 44143.08 | 106.215 |
| 2023 | 168073 | 128353.3 | 49968.74 | 106.098 |
| 2024 | 192279 | 146728.1 | 57063.52 | 105.987 |
| 2025 | 222051 | 169328 | 65789.69 | 105.884 |
| 2026 | 259017 | 197388.9 | 76624.42 | 105.789 |
The log damped trend exponential smoothing has been fitted in the premium data for non-life insurance companies and from the R-square value available from the fit, it is evident that the model has given a good fit. From the forecast value available, it is evident that the non-life premium income for the all non-life insurers will surpass 1 Lakh Crore INR in around year 2018/2019. During the early years after liberalization of non-life insurance industry, the sector as a whole use to run with little higher combined ratio (they need to pay more than 110 INR for claims as well as operating expenses in order to earn 100 INR as premium). However, the companies are gradually adapting to new competitive market drastically reducing different cost and so in the coming years the combined ratio will stay around 105 (they will be paying 105 INR as claims and operating expenses in order to earn 100 INR). This major achievement will be the private companies. Public companies also will reduce loss.
By 2024, the non-life insurance sector will be getting around INR 2 Lakh Crore INR as premium revenue and which will be a major boost to the insurance sector as well as the economy as a whole.
4. SUMMARY
The study shows two underwriting cycles visible in all 3 categories of Indian non-life insurance industry - public non-life insurers, private non-life insurers as well as for combined. For public non-life insurers, the cycles are - 2001-2002 to 2007-2008 and 2008-2009 to 2014-2015. For private non-life insurers, the cycles are - 2000-2001 to 2007-2008 and 2008-2009 to 2014-2015. For the combined non-life insurers' performance also, the cycles are - 2001-2002 to 2006-2007 and 2007-2008 to 2014-2015.
As visible from the forecasted results, the public non-life insurance companies will touch earning 1 Lakh Crore INR premium revenue by 2022-2023. The non-life insurance sector (both public and private together) will be fetching 1 Lakh Crore INR premium revenue by 2019 and 2 Lakh Crore INR as premium revenue from 2024 FY giving huge boost to the economy. As revealed from the forecasted results, the public insurance companies will become more economic in terms of operations and will maintain around 110 is combined ratio value in the coming years which will be a very good achievement for them. Private non-life insurance companies will maintain the combined ratio at around 102 due to their optimized operational capabilities.
This study is on future prospects of the Indian non-life insurance sector in the coming years. There is scope of further research in the granular level of non-life insurance sector like fire insurance, marine insurance, auto-insurance among others.
| Funding: This study received no specific financial support. |
| Competing Interests: The authors declared that they have no conflict of interests. |
| Contributors/Acknowledgement: All authors participated equally in designing and estimation of current research. |
| Views and opinions expressed in this study are the views and opinions of the authors, Asian Journal of Empirical Research shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. |
References
Aquino, J. G. (2008). The underwriting cycle: Measurement & analysis. Benfield Research Publication.
Bedi, H. S., & Singh, P. (2011). An empirical analysis of life insurance industry in India. International Journal of Multidisciplinary Research, 1(7), 62-73. view at Google scholar
Handbook of Indian Insurance Statistics (2014-2015). Published by insurance regulatory and development authority of India publication. Retrieved form https://www.irdai.gov.in/ADMINCMS/cms/frmGeneral_List.aspx?DF=Creport&mid=11.2.
Pal, S., & Mazumdar, S. (2013). Modelling and forecasting of the parameters influencing the under-writing cycle in Indian non-life insurance -a statistical approach to GIC experience. Indian Journal of Management Science, 3(2), 38-50.
Pal, S., & Mazumdar, S. (2014). Study on the underwriting cycle pattern and statistical analysis of the performances of the Indian public nonlife insurance companies in the last decade. International Journal of Management and Business Studies (IJMBS), 4(2), 17-23.
Rejda, G. E. (2009). Principles of risk management and insurance. 9th Edition, Pearson Education. view at Google scholar / view at publisher
Wang, S. S., Major, J. A., Pan, C. H., Leong, J. W. K. (2010). U.S. property-casualty: Underwriting cycle modelling and risk Benchmarks. Joint Research Paper of Risk Lighthouse LLC and Guy Carpenter & Company, LLC. view at Google scholar
Yu-na, Ji, Hai-tao, Zheng. (2009). Study on the existence of underwriting cycles in Chinese non-life market. Journal of Beijing University of Aeronautics and Astronautics (Social Science Edition), Volume 2009, Issue 4, Retrieved form http://en.cnki.com.cn/Article_en/CJFDTOTAL-BHDS200904000.htm