The article https://www.bafin.de/ref/19587768 from BaFin, the German insurance regulator, provides an overview of the role and challenges of internal models in the insurance industry, especially in the context of Solvency II. Internal models are statistical tools that use historical data to simulate future outcomes and to calculate solvency capital requirements (SCRs). The article explains that internal models can offer advantages for insurers, such as better risk management, more accurate capital allocation, and competitive edge. However, internal models also entail significant costs and risks, such as model uncertainty, validation issues, and regulatory approval. The article also describes the supervisory process and criteria that BaFin applies to assess and approve internal models for insurers in Germany. Historical time series of non-financial and financial parameters are collections of data points that are recorded over a period of time, usually at regular intervals. Historical time series of non-financial and financial parameters can also be used to set up undertaking-specific parameters (USPs) and internal models for insurers. USPs are the parameters that reflect the specific characteristics of an insurer’s portfolio or risk profile, such as loss ratios, lapse rates, or mortality rates. Internal models are the mathematical representations or simulations that an insurer uses to measure and manage its risks and capital requirements. Two of the international financial reporting standards that insurers need to comply with are IFRS 17 and IFRS 9. IFRS 17 is the standard that specifies how insurers should recognise, measure, present, and disclose insurance contracts. IFRS 9 is the standard that specifies how insurers should recognise, classify, measure, impair, and hedge financial instruments. IFRS 17 and IFRS 9 require insurers to use internal models that have already been implemented for financial reporting purposes.
These internal models are based on historical time series of non-financial and financial parameters that reflect the current market conditions and expectations. These internal models also incorporate forward-looking information and scenarios that capture the uncertainty and variability of future cash flows. By using these internal models for financial reporting purposes, insurers will also obtain USPs that also might be used for Solvency II purposes. For example, insurers can use the same assumptions for both IFRS 17 and Solvency II calculations if they are consistent with their USPs. This can reduce the complexity and inconsistency of using different valuation methods for different purposes. This can also increase the transparency and comparability of their financial statements and solvency reports. Therefore, historical time series of non-financial and financial parameters are not only useful for financial reporting but also for solvency purposes. By using these data sources in a coherent and integrated manner across the financial and regulatory reporting regimes, insurers can create USPs and internal models that reflect their unique characteristics and capabilities. By doing so, insurers can improve their performance, resilience, and competitiveness in the insurance industry. This is of particular importance when the insurance industry is facing increasing challenges and opportunities in the era of digitalisation, innovation, and regulation. To remain competitive and resilient, insurers need to leverage data and analytics to optimise their risk management and ultimately and foremost their business.