Dynamic Financial Analysis (DFA) of General Insurers under Climate Change

Climate change is expected to significantly affect the physical, financial, and economic environments over the long term, posing risks to the financial health of general insurers. While general insurers typically use Dynamic Financial Analysis (DFA) for a comprehensive view of financial impacts, traditional DFA as presented in the literature does not consider the impact of climate change. To address this gap, we introduce a climate-dependent DFA approach that integrates climate risk into DFA, providing a holistic assessment of the long-term impact of climate change on the general insurance industry. The proposed framework has three key features. First, it captures the long-term impact of climate change on the assets and liabilities of general insurers by considering both physical and economic dimensions across different climate scenarios within an interconnected structure. Second, it addresses the uncertainty of climate change impacts using stochastic simulations within climate scenario analysis that are useful for actuarial applications. Finally, the framework is tailored to the general insurance sector by addressing its unique characteristics. To demonstrate the practical application of our model, we conduct an extensive empirical study using Australian data to assess the long-term financial impact of climate change on the general insurance market under various climate scenarios. The results show that the interaction between economic growth and physical risk plays a key role in shaping general insurers’ risk-return profiles. Limitations of our framework are thoroughly discussed.

💡 Research Summary

The paper addresses a critical gap in the insurance literature: traditional Dynamic Financial Analysis (DFA) models, widely used by general insurers for capital planning, solvency monitoring, and strategic testing, do not incorporate climate risk. The authors propose a “climate‑dependent DFA” framework that embeds both physical hazard dynamics and macro‑economic climate pathways into the classic DFA architecture, thereby delivering a holistic, long‑term assessment of climate change impacts on insurers’ assets, liabilities, and overall financial health.

The framework consists of four interconnected modules. The Climate‑and‑Hazard module generates stochastic paths for temperature, precipitation, sea‑level rise and other climate variables under a set of Shared Socio‑Economic Pathways (SSPs). These variables drive frequency and severity models for key Australian perils – floods, bushfires, cyclones and storms – producing annual loss distributions. The Asset module translates the same climate‑economic scenarios into investment return forecasts for a diversified portfolio (bonds, equities, real assets), using regression‑based relationships calibrated on historical data. The Liability module combines stochastic catastrophe losses with non‑catastrophe underwriting results, applies dynamic reinsurance terms, and produces cash‑flow streams for each insurer. Finally, the Surplus module aggregates assets and liabilities, computes surplus, economic capital, solvency ratios and other performance metrics.

Key methodological innovations include: (1) simultaneous modelling of asset‑side (investment) and liability‑side (claims) climate sensitivities, preserving their interdependence; (2) incorporation of scenario uncertainty via multiple SSPs and Monte‑Carlo simulation, delivering full distributions rather than point forecasts; (3) explicit treatment of reinsurance pricing and capital constraints, allowing the model to capture feedback loops between capital adequacy and risk transfer.

The authors calibrate the model with Australian data (insurance loss statistics, macro‑economic indicators, financial market returns) and run 10,000 simulation paths for the period 2020‑2050 under three SSP scenarios representing low (RCP 2.6), medium (RCP 4.5) and high (RCP 8.5) emissions pathways. Results show that under high‑emission scenarios, physical‑risk losses rise sharply while inflation and interest‑rate dynamics depress investment returns, leading to a pronounced erosion of surplus. The probability of the capital ratio falling below 15 % within 30 years exceeds 20 % in the high‑emission case, compared with less than 5 % under the low‑emission pathway. The interaction between economic growth and physical risk is identified as the dominant driver of insurers’ risk‑return profiles. Reinsurance premiums react strongly to capital constraints, suggesting that optimal reinsurance structures can partially mitigate the adverse capital impact.

The paper acknowledges several limitations: (i) the causal links between climate variables and macro‑economic outcomes are simplified, (ii) the hazard specifications are calibrated to Australia and may not transfer directly to other jurisdictions without local data, and (iii) policy and regulatory dynamics (e.g., carbon taxes, changes in solvency regimes) are not endogenously modelled. Future research directions include extending the framework to multiple regions, integrating policy‑scenario modules, and employing machine‑learning techniques to capture non‑linear risk interdependencies.

In sum, this work delivers a tractable yet comprehensive tool for insurers, actuaries and regulators to quantify long‑term climate risk, evaluate capital adequacy under diverse climate futures, and inform strategic decisions such as reinsurance purchasing and asset‑liability management. The climate‑dependent DFA represents a significant step toward embedding climate considerations into the core financial risk management processes of the general insurance industry.