Integrated risk management for Short-term insurers

In the last few years, non-life insurance corporations in the US, Canada and Europe have experienced, among other things, pricing cycles accompanied by volatile insurance profits and increasing catastrophe losses contrasted by well performing capital markets, which gave rise to higher realized capital gains. These developments impacted shareholder value as well as the solvency position of many non-life companies.

One of the key strategic objectives of any company is to satisfy its owners by increasing shareholder value over time. In order to achieve this goal it is necessary to get an understanding of the economic factors driving shareholder value and the cost of capital. This does not only include identifying the factors but investigating their random nature and interrelations to be able to quantify earnings volatility.

Once this has been done various business strategies can be tested in respect of meeting company objectives. There are two primary techniques in use today to analyze financial effects of different entrepreneurial strategies for non-life insurance companies over a specific time horizon. The first one – scenario testing – projects business results under selected deterministic scenarios into the future.

Results based on such a scenario are valid only for this specific scenario. Therefore, results obtained by scenario testing are useful only insofar as the scenario was correct. Risks associated with a specific scenario can only roughly be quantified. A technique overcoming this flaw is stochastic simulation. Thousands of different scenarios are generated stochastically allowing for the full probability distribution of important output variables, like surplus, written premiums or loss ratios.

Objectives

This approach is not an academic discipline per se. It borrows many well-known concepts and methods from economics and statistics. It is part of the financial management of the firm. As such, it is committed to management of profitability and financial stability. While the first task aims at maximising shareholder value, the second one strives at maintaining customer value.

Within these two seemingly conflicting coordinates our approach tries to facilitate and help justify or explain strategic management decisions with respect to:

* Strategic asset allocation;
* Capital allocation;
* Performance measurement;
* Market strategies;
* Business mix;
* Pricing decisions and;
* Product design amongst a host of management decision-making variables.

Two fundamental questions

This listing suggests that the approach goes beyond designing an asset allocation strategy. In fact, portfolio managers will be affected by decisions as well as underwriters. Concrete implementation and application of such a model depends on two fundamental and closely related questions to be answered beforehand:

1. Who are the primary beneficiaries of such an analysis (shareholders', management, policyholders)?
2. What are the company individual objectives?

The answer to the first question determines specific accounting rules to be taken into account as well as scope and detail of the model. For example, those companies only interested in getting a tool for enhancing their asset allocation on very high aggregation level will not necessarily target a model that emphasizes every detail of simulating liability cash flows. A cost benefit analysis of asset or liability studies might reveal that costs fall on shareholders but benefits on management or customers.

Our general conclusion is that company individual objectives – in particular with respect to the target group – have to be identified and formulated before starting the analysis.

MODELLING Approach

The risks affecting the financial position of an insurer can be categorized in various ways. For example, we could use pure asset, pure liability, asset or liability and business risks. We believe that a model should at least address the following risks:

* Pricing or underwriting risk (risk of inadequate premiums and impact of underwriting cycles);
* Reserving risk (risk of insufficient reserves);
* Credit risk (counterparty default);
* Operational risk;
* Investment risk (volatile investment returns and capital gains); and
* Catastrophes and non-catastrophic losses.

We could have also mentioned currency risk and some more. A critical part of the model is the interdependencies between different risk categories, in particular between risks associated with the asset side and those belonging to liabilities.

Any given company is "decomposed" or grouped into simulation units. The approach in which the decomposition is done is determined primarily by the intended use of the model and care is taken to ensure that the decomposition is at a sufficiently granular level so that strategic decision-making may be done.

In most instances the decomposed framework follows the following taxonomy: In some instances, a geographic classification may be done after the 'Group' level. Differentiated product lines could also be incorporated at the lowest level e.g. division into new and existing business etc.

Simulation Framework

The starting point for the simulation is the budgeted volume of product sold. The "time unit" of simulation is generally monthly but can vary if deemed necessary.

Our approach is to simulate volumes and its consequent cashflows at the most granular level of decomposition and then aggregate the results. As illustrated in Fig. 2, the simulation results in simulated Balance Sheets, Cash Flow Statements and Income Statements at the second lowest level of decomposition. Since these are simulated, we can compute the volatility or range of our Balance Sheets, Cash Flow Statements and Income Statements – which allow us to computed expected returns, risk to returns, economic capita required etc.

Our simulation process.

Our simulation model consists of three major parts, as shown in Figure 3. The stochastic scenario generator produces realizations of random variables representing the most important drivers of business results. A realization of a random variable in the course of simulation corresponds to fixing a scenario.

The second data source consists of company specific input (e.g. mean severity of losses per line of business and per accident year), assumptions regarding model parameters (e.g. long-term mean rate in a mean reverting interest rate model), and strategic assumptions (e.g. investment strategy).

The last part, the output provided by the model, can then be analysed by management in order to improve the strategy, i.e. make new strategic assumptions. This can be repeated until management is convinced by the superiority of a certain strategy. Interpretation of the output is an often neglected and non-appreciated part in such modelling.

For example, an efficient frontier leaves us still with a variety of equally desirable
strategies. At the end of the day management has to decide for only one of them and selection of a strategy based on preference or utility functions does not seem to provide a practical solution in every case.