In a new white paper, Algorithmics, an IBM Company, discusses the practicalities of applying a curve fitting methodology with three leading European insurers. These firms have adopted curve fitting for their current regulatory reporting and plan to make it central to their Solvency II internal models. The paper, âCurve Fitting For Calculating Solvency Capital Requirements Under Solvency II: Practical insights and best practices from leading European Insurersâ, explores the advantages, challenges and limitations of the curve fitting method.
Curt Burmeister, Vice President, Risk Solutions, Algorithmics, commented: âCurve fitting is an effective way of creating a liability proxy but making the methodology robust and auditable for Solvency II is a challenge. We have worked with these and other European insurers to provide a controlled, robust environment for their curve fitting-based modeling and aggregation. Ultimately this is about understanding risk and using it to make better business decisions.â
According to the paper, most insurers choose between curve fitting and portfolio replication methodologies as a âliteâ way of modeling their overall balance sheet for Solvency II. Interestingly, in the UK, more firms have chosen to adopt curve fitting than elsewhere in Europe. While portfolio replication can model market risk, it is difficult to apply to insurance risk. By contrast, curve fitting can represent the value of the life insurance companyâs balance sheet as a function of all the risk factors affecting it, not just market risk.
Curve fitting, also known as formula fitting, is based on developing a formula that mimics the behaviors of best-estimate liabilities as calculated by an actuarial system under a range of scenarios. This means that curve fitting can replicate the results of full liability models under a large number of random scenarios, producing results in a fraction of the time.