One of the major challenges facing European insurers in the lead up to Solvency II is how to report their solvency capital numbers to regulators more frequently, in more detail and in a shorter time frame than ever before? Calculating solvency capital faster demands some form of portfolio compression technique in order to avoid historical approaches that are time consuming, expensive and computationally demanding. There are a number of proxy modeling techniques insurers can choose as their primary technique for calculating Solvency Capital Requirement, but with the shortage of practical knowledge available to insurers, the question many are asking is: ’which technique suits my business needs the best?’
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, ‘Curve Fitting For Calculating Solvency Capital Requirements Under Solvency II: Practical insights and best practices from leading European Insurers’, explores the advantages, challenges and limitations of the curve fitting method.
Curt Burmeister, vice president, Risk Solutions, Algorithmics, commented: “Curve 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.”
According to the paper, most insurers choose between curve fitting and portfolio replication methodologies as a ‘lite’ 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 company’s 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.
