The rise of risk-based solvency regimes has prompted insurers to look for actuarial modelling solutions that are capable of being recalculated a large number of times in order to examine the impact of stressing particular assumptions or investigating a range of scenarios. Although traditional actuarial models were already commonly being used for this purpose, these models simply weren’t capable of delivering the volume of different stress/scenario results required in an acceptable timeframe.
Due to regulatory drivers such as the Use Test under Solvency II, and commercial drivers such as the rise of the Risk Management discipline, senior management were looking to use the results of the calculations for decision-making in real time, so a model solution was required that could deliver a large number of results under different scenarios almost instantaneously.
At present, the regulatory drivers have subsided due to the uncertainty currently surrounding the future of risk-based capital regimes such as Solvency II. Although some actuaries and insurance company CEOs are now feeling that Solvency II has been a drain on resources offering little discernible benefit, a recent survey of Chief Risk Officers found that they are overwhelming grateful to risk-based capital regimes like Solvency II for putting risk management at the heart of insurers’ decision-making process. Techniques such as risk factor polynomial fitting have greatly enhanced the risk manager’s toolkit; for example, Least Squares Monte Carlo with its powerful stepwise regression technique has effectively given risk managers an algorithm for the evaluation of the significance and interaction of their risk factors.
Within the FIS Insurance Risk Suite, various techniques are available for performing proxy type modelling.
Flex Modelling in the FIS Insurance Risk Suite Asset Liability Strategy Library
The Insurance Risk Suite Asset Liability Strategy library offers a flexing approach to modelling the liabilities. In this case, the cashflows from a detailed deterministic model are passed into the ALM model. Based on the known behaviours of the company, the cashflows are adjusted to allow for changes in economic conditions or similar.
Our replicating portfolio solution was first launched in a stand-alone module in 2008, and is now incorporated into the FIS Insurance Risk Suite Proxy Fitting Library. Replicating portfolio techniques involve finding portfolios of assets that closely match the cashflows from a set of liabilities. There are many uses for replicating portfolios in insurance, such as:
- Proxy models which can be re-evaluated much faster than explicit models
- Performance attribution
- Linking to other asset systems
- Assistance in formulating hedging and other investment strategies.
Risk Factor Polynomials
Our first offering in polynomial proxy fitting was launched in 2012, and our capabilities in this area have now also been incorporated into the Insurance Risk Suite Proxy Fitting library. The uses of curve fitting techniques in insurance include:
- Proxies for explicit models that can effectively be revalued instantaneously
- Analysis of the impact and interaction of the risk factors affecting your business
- Linking fitted proxy formulae into other models for rapid recalculation of results
Click here to find out more about our Insurance Risk Suite Proxy Fitting Library.