The regulation known as Solvency II takes effect on January 1st 2016. It will have a considerable impact on the insurance sector in the EU. In the third of our four blogs outlining data issues arising from Solvency II, we look at potential consequences of using different methodologies for calculating insurers’ capital requirements.
Besides having a direct impact on the size of capital requirements themselves, the choice of methodology will affect the nature of the data needed for Solvency II compliance. Most insurers are currently focused on implementing the Standard Formula in time to comply with the January 2016 deadline. Implementing internal models is more time-consuming and requires significant resources and expertise. Internal models are also subject to detailed regulatory approval processes and regular reviews.
Insurers can however update their chosen methodology at a later date. It is expected that many will do so, given that internal models lead to potentially lower capital requirements.
One aim of regulators is for internal models used within the insurance industry to tend towards the sophistication of the internal ratings-based (IRB) approach adopted by most banking institutions under Basel II.
With regulators placing a strong emphasis on the quality of data used to drive internal models, potential benefits in terms of reduced capital requirements must be weighed up against the cost of additional resources and data needed. Depending on the model adopted, data requirements can include:
- Default statistics – historical or modelled
- Loss given default (LGD) or recovery measures – historical or modelled
- Ratings transition probability matrices
- Public and private company fundamentals
- Company relationships and ownership data
- Macroeconomic data
- Equity pricing and volatility data
- Credit Default Swap pricing data
- Yield curves and credit curves
- Security Terms and Conditions data
- Information on corporate actions
Understanding and Verifying Model Inputs and Outputs
Beyond ensuring high data quality, testing and benchmarking model inputs and outputs will also be necessary to ensure models properly reflect the insurer’s risk profile and that appropriate data governance is in place.
Furthermore, insurers need to demonstrate that senior management understand the model’s methodology, dynamics, assumptions and limitations. This is important to keep in mind, given that many insurers will turn to third parties to assist them in fulfilling these requirements.
To learn more, read our latest whitepaper “Solvency II: Understanding the Data Implications”. Don’t miss our final blog next week, which will look at the interaction of Solvency II requirements with data needs driven by other regulations.