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Non-life insurance

Premium rating and product design


Insurance markets are changing rapidly because of deregulation, new entrants and increasing consumer awareness. Innovative product design, new marketing techniques and timely product launches are required to stay ahead of the competition.

Sophisticated premium rating and the flexibility to respond quickly to changing market trends is increasingly recognised as the key to meeting this competitive challenge. Where existing tariff structures are likely to change in future, it is essential to start collecting statistical data well in advance so profitable rating structures for the new environment can be designed. We have extensive experience of designing products and rating structures for many different classes of business.

Watson Wyatt Pretium

In addition to providing consulting services, we also provide our own specialist premium rating software. Watson Wyatt Pretium is an analysis tool designed for actuaries and statisticians involved with premium rating strategies and portfolio management. The system allows rapid and efficient analysis of personal lines experience, and has over 500 users in more than 140 companies across 40 countries.

Analysis of the market

The first step in the process is to determine the exact nature of the market and the requirements of the customer base. We can assist you by:

  • estimating the size of the current and potential market

  • analysing market trends and profitability

  • determining the products and rating methods used by the competition

  • evaluating competing products in terms of price and cover

  • investigating who actually makes the decision to buy a particular type of product

  • identifying the principal reasons for buying the product

  • comparing the benefits of different distribution methods and advising on the most effective method for remunerating sales distribution channels.

Analysis of data

Accurate premium rating depends critically on collecting relevant data in an appropriate format, and then analysing this data using the right statistical techniques. Our approach addresses the central issues by:

  • advising on the design of databases and the collation of statistical information

  • analysing claims experience to determine the effect that rating factors have upon the risk

  • comparing these theoretical results with the relativities implicit in the existing rating structure and interpreting the results

  • considering the possible effect upon the portfolio of introducing particular rating changes

  • providing financial projections of future results, based on the calculated premium rates

  • undertaking sensitivity analyses.

Statistical techniques

As insurance markets become more sophisticated and as computing power increases, companies are using more advanced statistical techniques to analyse their business and design premium rating structures.

Across the EU, increasingly in the USA, and in many deregulating markets in the Asia-Pacific region, generalised linear models (GLMs) are being used for classes of business that have large numbers of possible rating factors, such as motor insurance.

GLMs show the true effect of rating factors, taking into account other correlated factors, and can be used to determine the risk factors that discriminate most effectively between good and bad risks. In addition, these statistical techniques can be used to investigate the effect upon policyholder retention of measurable factors, including the change in proposed premium and the competitiveness of the premium. Such analysis can provide valuable insight into how best to manage future rating changes.

Statistical techniques can also be used to model and price excess–of–loss reinsurance contracts. In this case simulation methods are generally used, based on fitted claim numbers and amounts distributions. Aggregate models can be considered to assess whether the excess–of–loss premium charged is sufficient to cover the expected costs of claims, brokerage and an adequate loading for adverse contingencies and profit.

Watson Wyatt has considerable experience of using such statistical techniques having assisted clients in this area in over 20 countries worldwide.

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