Importance of Capital Models for Insurers

Capital Modeling for Parametric Insurance As parametric insurance gains traction, insurers face specific challenges in capital modeling and regulatory capital navigation. 1. **Regulatory Uncertainty**: The treatment of parametric insurance under frameworks like Solvency II and SAM remains ambiguous. Insurers must engage proactively with regulators to establish appropriate methodologies. Regulators have the challenge of how to shoe-horn parametric insurance into a regulatory framework that was not designed with this in mind. 2. **Line of Business Allocation**: Fitting parametric products into traditional lines of business is complex. Many parametric products resemble inwards non-proportional reinsurance more than direct insurance, with payouts triggered by specific events. Even then, there is no guarantee that the standard premium volatility factors are appropriate. Insurers may need to explore Undertaking/Insurer Specific Parameters (USP / ISP) or transition to partial internal models. 3. **Portfolio Size and Trigger Remoteness**: The risk profile changes significantly with smaller portfolio sizes and trigger remoteness. As triggers become more remote, the capital required relative to premium increases. At a certain point, the 99.5th VaR can fall well outside the 3-sigma range, challenging standard deviation-based approaches. 4. **Diversification Effects**: Understanding correlation between parametric triggers, and at different levels of triggers, means approaches like copula modeling might be necessary. Student t copulas are a likely candidate. 5. **Attritional vs. Catastrophic Losses**: The binary nature of parametric triggers blurs the line between attritional and catastrophic losses. 6. **Time Series vs. One-Year Capital View**: While sensor data forms a time series that could be modeled using techniques like SARIMAX or GARCH-X, the one-year capital view required by regulations doesn't necessarily need to incorporate this time series structure. 7. **Climate risk and trends**: An advantage of parametric insurance is the typical clean time-series sensor records (necessary for pricing and risk management). However, the continued relevance of historical records is at risk given climate change for many key parametric coverages. 8. **Demonstrating Appropriateness**: The Head of Actuarial Function (HAF) faces the challenge of demonstrating that the chosen capital approach appropriately reflects the risk profile of parametric products. The approach needs to work within the regulatory framework, but the result must still be reasonable. As the parametric insurance market evolves, so too must our approach to capital modeling. The challenges are significant, but so are the opportunities for innovation and more accurate risk assessment. #ParametricInsurance #CapitalModeling #InsurTech #RiskManagement #Solvency2 #SAM #ISP #USP #HAF #InternalModel #RiskAppetite

The Capital Adequacy Ratio (CAR), also known as the Solvency Ratio, for general insurers reflects the financial stability and ability of an insurer to meet its long-term liabilities and obligations, especially in the event of large claims. What Capital Adequacy Ratio Reflects Financial Health: A measure of how well an insurer can handle unexpected losses. Indicates the insurer’s capability to absorb shocks from claims or market downturns. Risk Coverage: Demonstrates whether the insurer has sufficient capital to cover underwriting, operational, and investment risks. Regulatory Compliance: Regulatory bodies like IRDAI in India mandate a minimum solvency ratio (usually 1.5 or 150%) to ensure insurers maintain a financial buffer. Confidence for Stakeholders: A higher ratio signifies a safer and more reliable insurer, inspiring confidence among policyholders, investors, and regulators. What a Negative Capital Adequacy Ratio Means A negative solvency ratio indicates a significant financial crisis within the insurer. Key implications include: Deficient Capital: The insurer lacks sufficient capital to cover its liabilities. Indicates that liabilities exceed the value of assets, resulting in a negative net worth. Inability to Settle Claims: The insurer might struggle to settle large or catastrophic claims, jeopardizing policyholder interests. Regulatory Non-Compliance: Failing to meet the minimum solvency margin could lead to regulatory interventions such as restrictions on issuing new policies or conducting specific operations. Risk of Insolvency: A negative CAR raises concerns about the insurer’s long-term survival and could lead to liquidation if not addressed. Loss of Credibility: Policyholders, investors, and stakeholders may lose confidence, leading to reputational damage and possible policy cancellations. Steps to Address Negative Solvency Capital Infusion: Raising funds through equity, debt, or external investments to restore solvency levels. For 3 PSUs- Government Intervention is must now. Reinsurance Support: Offloading risk through reinsurance agreements to reduce liabilities. Cost Optimization: Streamlining operations and reducing expenses to improve profitability. Product Portfolio Rebalancing: Focusing on profitable and low-risk insurance products to stabilize finances. Having better claim ratio always does not mean efficiency, whole claim management needed to be assessed.

Value at Risk (VaR) in Insurance vs. Banking: A Structural Comparison Value at Risk (VaR) is a widely used risk measure in both banking and insurance, but its application and methodology differ significantly between the two industries. While banking focuses on market risk and portfolio sensitivity, insurance VaR is far more complex, incorporating multiple risk factors beyond just asset price fluctuations. VaR in Insurance: A Multi-Factor Challenge Unlike banking, where VaR primarily captures market risk through asset price movements and volatility, insurance VaR is deeply integrated into capital modeling, claims risk, and long-term liability assessment. Key components of insurance VaR: • Claims risk – The severity (size) and frequency (occurrence rate) of claims directly impact an insurer’s financial stability. • Tail risk and catastrophe modeling – Extreme loss events (hurricanes, earthquakes, pandemics) require generalized Pareto distributions (GPD) and extreme value theory (EVT) to assess risks beyond typical market VaR. • Regulatory capital requirements – Insurance VaR ties into Solvency II (Europe) and IFRS 17, where insurers must quantify risk exposure across multiple time horizons. • Asset-Liability Management (ALM) – Unlike banks, which focus on short-term asset price changes, insurers must match long-term liabilities with appropriate investments, making VaR a key tool in managing risk-adjusted returns. Example: A property and casualty (P&C) insurer calculating VaR cannot rely solely on market returns. Instead, it must incorporate policyholder behavior, inflation risks, litigation trends, and catastrophic loss probabilities. The complexity of policy claims exposure makes insurance VaR a far more intricate modeling challenge than banking VaR. The Convergence of Actuarial Science & Quantitative Finance With increasing regulatory complexity, there is a growing overlap between: • Banking quants specializing in risk modeling, credit VaR, and market VaR • Insurance actuaries focusing on capital modeling, claim risk, and tail event probabilities Why is this important? • Insurance companies are incorporating VaR-like methods to evaluate economic capital, aligning more with banking stress testing frameworks. • Banks are integrating actuarial-style long-term modeling for balance sheet risks, including asset-liability management and IFRS 9 lifetime loss estimation. • The demand for quants in insurance risk modeling is increasing as insurance risk frameworks become more complex. Where do you see the future of risk modeling heading? Will quants play a bigger role in insurance? Let’s discuss. #QuantitativeFinance #QuantTrading \#FinancialModeling #RiskManagement #DataScience #MachineLearning #FinTech #FinancialEngineering #PortfolioManagement #QuantResearch #TradingStrategy #FinancialAnalytics #HedgeFunds #InvestmentBanking