Page 59 - India Insurance Report 2023- BIMTECH

India Insurance Report - Series II 47

Increasing customers’ digital usage would enable insurers to develop end-to-end digital experiences
that leverage technological applications, which, in turn, is expected to drive growth for the industry.
Our forecast model has predicted that the insurance market will reach over 3,16,000 crore by fiscal 2030,
with new online distribution models such as business-to-consumer (B2C), business-to-business (B2B),
and business-to-business-to-consumer (B2B2C) set to be key drivers of growth.

Realizing the importance of the key trends shaping the industry and the need to develop a strategic
perspective to understand future business trends and action plans to meet emerging challenges and
opportunities, the above research paper has been written. The methodology developed considered the
blend of historical trends of the industry data and the country’s economic growth combined to shape
the industry’s future business trends. Accordingly, suitable statistical techniques were applied. A brief
detail about the methodology is presented below.

2. Methodology of the study

Since the main purpose of this exercise is to look into the overall Business Projections for the
General Insurance Industry, analyse the trends, and offer solutions wherever possible, we will concentrate
on aggregate business figures instead of product-by-product forecasts. Also, since the general insurance
market is undergoing a structural shift, higher weights were given to current values and lower weights to
the past experience. Thus, we will need to experiment with the methodology itself. We will need to
hedge across methods of forecasting. Accordingly, the business forecast analysis for the General Insurance
Industry was carried out using the following methods:

a) “Economic growth forecasts” have first been worked out with a view to identifying the principal
determinants of the demand for General insurance.

b) Forecast models based on time-series properties with declining historical weights to the past data
and increasing weights to the recent experiences were applied to obtain unbiased parameters and
improve forecasting efficiency.

c) Both sets of forecasts have been blended wherever relevant to produce likely future scenarios.

Chart (1) below shows the behaviour of general industry premiums with reference to GDP (Gross
Domestic Products) data. The explanatory model underlying the fit shows a stable relationship with
respect to each of the two variables: Industry Growth and the GDP data.

As one of the primary purposes of this paper is to predict the industry premium in the next 5 to 10
years and also discuss the futuristic trends of various portfolios or business segments of the general insurance
industry, we initially collected the industry historical premium data from 2001 to 2022, studied the historical
trends of every portfolio and also by superimposing the current or futurist trends to derive, the final
forecast values for every major line of insurance business were estimated. Since most general insurance
business trends are not linear and some of their business( lines) are affected by seasonality factors and
stronger economic influence, we have used the Auto-Regressive Integrated Moving Average (ARIMA)
Method. We found that the used ARIMA models with AR lags of 1 or 2 provide satisfactorily significant
goodness of fit with lower error values. The models’ data and diagnostic results are given in the Appendix.