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Insurance Forecast Methodologies

Introduction

In this report, we aim to look at operating conditions within the insurance sector of the country in question in an original and insightful way. Using the considerable amount of data BMI has accumulated as a result of its economic, financial and general research, we identify how fast the insurance sector – measured by gross premiums – could grow between now and the end of the forecast period.

We also take note of the competitive landscape within the country’s insurance sector. We do this by identifying the number of cross-border insurers that are active in each of the two segments (non-life and life), identifying the number and strength of local insurers, and quantifying how the premiums are likely to grow over the forecast period.

Many of the insights are generated because the report looks at the country’s insurance sector in a global and a regional context.

In most instances, the details of the operations of the various companies that dominate the insurance sector will not be available to the public. Insurance companies whose operations are confined overwhelmingly to one country and who are listed – and therefore compelled to disclose considerable details of claims and other operating costs – are the exception rather than the rule.

To overcome this challenge we look in some detail at how the country’s insurance sector compares with those of similar countries. Similarity is in the eye of the beholder. Often, but not always, comparisons with neighbouring countries will be relevant. In other instances it makes sense to compare with countries that are in completely different parts of the world and/or are quite dissimilar in most respects other than in their insurance sector.

The other way in which we seek to overcome the frequent shortage of relevant information about particular companies is to consider how the latest figures compare with long-term trends.

Background

We assume that each country’s insurance sector consists of two segments. One is the life insurance segment, including long-term savings products and products that make payment in the event of the death (or permanent disability) of the insured life. The other is the non-life insurance segment (sometimes called the property/casualty segment), which involves all short-term products and the insurance of other risks.

If the national health insurance system in a country operates in such a way that health insurance premiums are collected by companies that are also clearly active in either the non-life or the life segments, we classify such health insurance premiums as accruing to the non-life segment. If the national health insurance system in a country operates in such a way that health insurance premiums are collected by specialist companies that are not otherwise active in either the non-life or the life segment, we do not include such premiums in either the non-life or the life segment.

In some of the countries we cover, inwards reinsurance is a minor, but significant, part of some companies’ business. We classify inwards reinsurance premiums as accruing to the non-life segment. However, we classify outwards reinsurance as an expense. Therefore, our premium figures – for both segments – are gross written premiums.

In most of the countries whose insurance sectors are examined by BMI, the non-life segment is larger and better developed than the life segment. Accordingly we look first at the non-life segment in the text and the tables.

Premiums are translated into US dollar using exchange rates sourced from BMI’s database, which we disclose.

Forecasts

In forecasting, we consider the two segments separately.

We assume that non-life premiums are driven by growth in nominal GDP and by changes in non-life penetration (i.e. non-life premiums as a percentage of GDP). With estimated, or actual, GDP data from BMI’s database, and the estimated, or actual, non-life premium information we are able to calculate the non-life penetration rates. We then forecast what non-life penetration will be in the final year of the forecast period. To do this, we take into account actual/estimated current penetration, penetration in nearby countries, and penetration in countries where GDP per capita is similar. We then assume that penetration will change at an even rate over the forecast period. We multiply the projected non-life penetration for each year of the forecast period by BMI’s forecasts of nominal GDP in those years. We use BMI’s forecast exchange rates to derive the forecast non-life premiums in local currency terms. We use BMI’s forecast population figures to derive the forecast non-life density (i.e. per-capita premiums).

We assume that life premiums are driven by growth in population and by changes in life density (i.e. per-capita premiums). With estimated, or actual, population figures for the last calendar-year from BMI’s database, and the estimated, or actual, life premium information for that year, we are able to calculate the life density. We then forecast what life density will be in the final year of the forecast period. To do this, we take into account current actual/estimated density, density in nearby countries, and density in countries where life insurance is at a broadly similar level of development. We then assume that density will change at an even rate over the forecast period. We multiply the projected life density for each year of the forecast period by BMI’s forecasts of population in those years. We use BMI’s forecast exchange rates to derive the forecast life premiums in local currency terms. We use BMI’s forecast GDP figures to derive the forecast life penetration (i.e. life premiums as a percentage of GDP).

Analysis Of Competitive Conditions

This section aims to derive insights about the competitive conditions prevailing in the non-life and life segments of each of the countries surveyed by BMI. We have looked at the global presence of each of around 60 cross-border insurance companies. In each case, we have examined the information available from the corporate website to answer this question:

What appear to be the developing insurance markets, whether BMI surveys them or not, in which this company is active?

In each case, we have assessed the non-life and life segments separately. Often cross-border insurance companies follow different strategies, either globally or within a region, in the two segments.

We accept that there are some developing markets that BMI does not at present cover and that have been omitted from the tables in this section. It is likely that we will add countries to the tables in the future.

In general, we have assumed that a cross-border insurance company is active in the non-life and/or the life segment in a particular country if its website indicates that it has operations in that country. If the company appears only to have a representative office, or appears to service non-life or life customers from a remote location, we have assessed that it does not have a presence.

We have treated cross-border companies that are primarily reinsurers differently to other international companies. We have only shown them as having a presence in a particular country if their website indicates that they operate as non-life and/or as life insurers in that country. Most reinsurance business is carried out by reinsurers in their home countries (often developed countries that BMI does not cover) or in offshore centres such as Bermuda or London.

Not all cross-border insurance companies are multi-national groups based in developed countries. Several insurance companies that would count as dominant local players in one of the countries surveyed by BMI have operations in other (usually nearby) countries. In general, these companies are shown in the tables as cross-border companies. For each country, we have used information from publicly available sources identify major companies that are purely local.

In order to achieve a rough gauge of competitive conditions, we have compared the ranking of each segment, in terms of the number of cross-border companies that are active, with the segment’s ranking in terms of total forecast growth in premiums over the forecast period. Total forecast growth is simply the difference between forecast premiums at the end of the period and actual or estimated premiums in the last calendar-year. If, for instance, a segment has a comparatively large number of cross-border firms but a comparatively low ranking in terms of forecast growth in premiums, we would see conditions as naturally competitive. At the very least, we would consider it reasonable to conclude that conditions would be more competitive than in the relevant segment of another country which has a meaningfully lower number of cross-border firms and/or a significantly higher ranking in terms of forecast growth in premiums.

Sources

If we have final (or provisional) local currency figures for non-life and life premiums from the regulator and/or national trade association(s) for the last calendar-year, we use them. If such numbers are not available, we estimate premiums on the basis of information published by regulator(s) and/or trade association(s). If no numbers from such official sources are available, we attempt to identify how premiums had grown for suitably large samples of companies operating in the country in question. If we are unable to find any information – either from the official sources or from samples of companies in the insurance sector – we would assume that premiums grew in line with trends set in the previous two years.

 

Insurance Business Environment Ratings Methodology

Ratings Overview

Conceptually, BMI’s Insurance Business Environment Rating system provides a globally-comparative, numerically-based assessment of the Risk/Return trade-off for the industry in each state covered in BMI Reports. In order to provide clients with a detailed assessment of this trade-off, the overall rating is comprised of two distinct sub-ratings:
Limits of Potential Returns: Evaluates the industry’s current size and growth potential, and also assesses broader industry/state characteristics that may enable/inhibit the industry’s development.
Risks to Realisation of Potential Returns: Evaluates issues within (a) the Insurance sector, and (b) the broader political/economic/business environment, that indicate the level of uncertainty surrounding the realisation of potential returns.

These ratings are themselves comprised of sub-ratings:

• The ‘Limits’ Rating is comprised of:
• Domestic Market: This evaluates industry growth/size dynamics of both the Life and Non-Life sectors
• Country Structure: This evaluates the broader economic/socio-demographic environment

• The ‘Risks’ Rating is comprised:
• Market Risks: This is a subjective evaluation of industry regulatory and competitive issues
• Country Risk: This evaluates the industry’s broader Country Risk exposure, which is based on industry-specific vulnerabilities that may be assesses using any combination of the 56 components of BMI's proprietary Political, Economic and Business & Operational Risk Ratings

Weighting

Given the number of indicators/datasets used, it would be inappropriate to give all sub-components equal weight. Consequently, the following weight has been adopted.

Component	Weighting
Limits of Potential Returns	70%
- Insurance Market	- 60%
- Life	- 50%
- Non-Life	- 50%
- Country Structure	- 40%
Risks to Realisation of Potential Returns	30%
- Market Risks	- 40%
- Country Risk	- 60%

 

Indicators

The following indicators have been used. Overall, the rating uses three subjectively-measured indicators, and 41separate indicators/datasets.

Indicator	Rationale
Limits to Potential Returns
Insurance Market
Life Premiums, US$mn (current year)	Indication of overall sector attractiveness. Large markets are considered more attractive to new entrants than smaller ones.
Life Premiums, US$mn (end of forecast period)	Indication of growth potential. Nominal value of change is deemed more instructive than % y-o-y change as an indicator of state attractiveness.
Life penetration, %	Premiums expressed as % of GDP. This is an indicator of market maturity.
Cross-border firms operating in Life sector	Proxy for market openness. Scores are, however, affected by our calculation of sector value per foreign market entrant, which indicates whether market is currently crowded.
Non-Life Premiums, US$mn (current year)	Indication of overall sector attractiveness. Large markets are considered more attractive to new entrants than smaller ones.
Non-Life Premiums, US$mn (end of forecast period)	Indication of growth potential. Nominal value of change is deemed more instructive than % y-o-y change as an indicator of state attractiveness.
Non-Life penetration, %	Premiums expressed as % of GDP. This is an indicator of market maturity.
Cross-border firms operating in Non-Life sector	Proxy for market openness. Scores are, however, affected by our calculation of sector value per foreign market entrant, which indicates whether market is currently crowded.
Country Structure
GDP per capita, US$	A proxy for wealth. High income states receive better scores than low income states.
Active population	Those aged 16-64 in each state, as % of total population. A high proportion suggests that market is comparatively more attractive.
Corporate Tax	A measure of the general fiscal drag on profits.
GDP volatility	Standard deviation of growth over economic cycle (7-yr period). This is used as a proxy for economic stability.
Financial infrastructure	Measure of financial sector’s development, a crucial structural characteristic given industry’s reliance on risk calculation.
Risks to Realisation of Potential Returns
Market Risks
Barriers to entry	Subjective evaluation of de facto/de jure barriers imposed by the government on new market entrants.
Regulatory environment	Subjective evaluation against BMI-defined criteria. This indicator evaluates predictability of operating environment.
Country Risk
Short term financial risk	Rating from BMI’s Country Risk Ratings (CRR). It evaluates currency volatility.
Short-term external risk	Rating from BMI’s CRR. It denotes the state’s vulnerability to externally-induced economic shock, which tend to be the principal triggers of economic crises.
Policy Continuity	Rating from BMI’s CRR. It evaluates the risk of a sharp change in the broad direction of government policy.
Legal framework	Rating from BMI’s CRR, to denote strength of legal institutions in each state – security of investment can be a key risk in some EM.
Bureaucracy	Rating from BMI’s Country Risk Ratings to denote ease of conducting business in the state.

 

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