September 15th, 2010

Modeling Loss Reserve Risk

Posted at 1:00 AM ET

mango_smallA Conversation with Don Mango, Chief Actuary

Loss reserves are one of the most difficult risks on insurance companies’ balance sheets. What about loss reserving presents such difficulty?

Loss reserves are essentially forecasts of losses that are going to be paid five, 10 and 15 years from now. Since the future cannot be predicted with perfect accuracy, reserves, of course, are difficult to estimate.

Standard reserve methods in use today start by assuming that the future looks like the past and that data trends will continue. Typically, an actuary examines historic data, measures existing patterns and makes forecasts based on the assumption that those patterns will repeat.

Sometimes there is not enough data to measure the pattern reliably. And sometimes trends change and the pattern from the past simply does not repeat. The worst cases of adverse reserve development occur when there is a substantial unpredicted change in the trends. That possibility is one of the major reasons that loss reserves are uncertain.

Has the actuarial profession made advancements in the practice of loss reserving?

Modern computing has revolutionized statistics, but the actuarial methods of determining loss reserves have not evolved with the technology. Over the last 20 years, actuarial pioneers have worked to apply modern theories to loss reserves, but in practice, these ideas have not been widely adopted.

What hurdles need to be overcome for more advanced loss reserving methods to move into the mainstream?

New reserve methods will need to offer demonstrable advantages that override the natural instinct to keep time-tested ways. Insurers will want a solution with proven accuracy and results they can verify and explain. Programming advanced statistical methods may be too time-consuming for individual companies, so commercial software is needed. So far, there haven’t been many practical, cost-efficient solutions available.

Guy Carpenter has made progress in this area. Our Dynamic Reserve Model (DRMTM), for example, combines the advances made by leading actuarial pioneers with innovations from our own research and development to create a solution that meets the industry’s needs. The model uses advanced statistical methods to identify and display trends in an insurer’s data that were previously hidden. With a clearer picture of the past, our clients can make forecasts that are better informed and more transparent. DRM is intuitive and visual, which makes the analysis more easily understood by all audiences.

Both Solvency II and fair value of loss reserves require companies to measure loss reserve risk. How are companies currently approaching this?

The Mack and Bootstrapping techniques are the dominant methods companies use to measure loss reserve risk. They are both statistical generalizations of a rigid version of the most basic loss reserve technique - the chain-ladder method.

Sometimes the results look reasonable. In general, however, the Mack and Bootstrapping techniques have serious shortcomings that can create both understatements and overstatements of loss reserve risk. When data is thin, the instability overwhelms the calculation, and the risk is overestimated. When the data is steady, the assumption that the future will replicate the past misses a major aspect of loss reserve risk, and the risk is underestimated.

Guy Carpenter’s DRM addresses these limitations. DRM uses sound statistical principles to build stable models, while it also has the flexibility to model changing trends. The loss reserve risk measures thus include the risk of unpredictable changes in the future. This risk can be correlated across lines of business and between reserves and new business.

How important is loss reserve risk in capital modeling?

Loss reserve risk can account for the greatest drain on corporate capital. Furthermore, the factors that drive loss reserve risk also contribute to underwriting risk for long-tail business as well as correlation with the underwriting cycle. Common systemic drivers create correlations among lines of business, between reserves and new business. Modeling these systemic drivers is a significant part of a meaningful capital model for an insurer that writes long-tail business.

How can Guy Carpenter’s DRM enhance an insurer’s loss reserve risk modeling and, subsequently, optimize capital management?

Insurers may initially be drawn to DRM for its ability to measure loss reserve risk, especially because they are increasingly required to report these numbers. But in adopting DRM, they will also embrace a model of loss reserve measurement that offers improved insight backed by better diagnostics.

DRM will help insurers navigate long-tail risks in a dynamic environment. The essential premise of a dynamic approach is that trends may change. With both pictures and numbers, DRM measures and displays the real trends in an insurer’s data - whether they have been steady or changing. This better understanding of the past leads to important advantages: forecasting decisions that are well-informed, transparent and explainable; reserve risk modeling that includes the risk of a changing future; and, the risk drivers that DRM measures can be incorporated into a capital model, so that insurers can manage the risks of long-tail business in the context of corporate capital requirements.

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