This paper addresses the critical decision-making problem of valuing life insurance reserves under extreme demographic uncertainty, such as pandemics, climate-induced shifts, and economic crises. We introduce a novel framework that integrates Bayesian deep learning with measure-theoretic actuarial science to overcome the limitations of classical models. By modeling financial and demographic risks through product measures (μ ⊗ ν) and incorporating Lévy processes for mortality shocks, our approach captures the complex interaction between stochastic discounting and discontinuous demographic dynamics. The neural architecture, a stochastic multilayer perceptron with Bayesian Bernoulli dropout and Lévy-distributed noise injection (εₗ ∼ Levy(1.7)), ensures Radon-Nikodym compatibility via a measure-preserving neural operator, guaranteeing actuarial interpretability and decision robustness under Solvency II regulatory standards.

We validate the framework empirically using EIOPA mortality data across multiple stress scenarios, including a simulated COVID-19-style pandemic shock with tripled arrival rates. Results demonstrate significant improvements: a 63% reduction in mean absolute error (MAE) and a 77% decrease in Kullback–Leibler (KL) divergence compared to classical models. Under pandemic stress, our model achieves a 64% error reduction, requiring 22% less economic capital under the 99.5% Value-at-Risk (VaR) Solvency II requirement. Additionally, we integrate fairness metrics via Pearl's do-calculus, achieving a fairness score of ε = 0.0031—well below the EIOPA regulatory threshold (<0.005)—with an adjusted average treatment effect (ATE) for gender of -0.0031 ± 0.0004, eliminating demographic bias.

The framework offers a scalable, transparent, and ethically-aware solution for reserve modeling and decision-making in volatile demographic environments, with projected quantum-inspired complexity of O(√N) for large portfolios.

This study examines the impact of life insurance market development on economic growth in
Cameroon, the second-largest market in the FANAF zone in terms of premiums issued in 2021.
Despite the notable growth of the Cameroonian life insurance sector, its potential impact on
economic growth remains largely unexplored, as national empirical research has primarily focused
on banking institutions. This study addresses a significant empirical and methodological gap in the
existing literature by investigating two fundamental questions: the direction of causality between
life insurance development and economic growth (supply-leading, demand-following, or feedback
hypothesis), and whether this relationship is symmetric or asymmetric. Using annual data covering
the period from 1992 to 2020 and advanced econometric methods (ARDL, NARDL, FMOLS,
DOLS, and CCR), we establish the existence of a positive and significant long-run relationship
between life insurance penetration and GDP per capita. Our results reveal an intrinsic asymmetry:
the expansion of the life insurance market generates a greater positive impact on economic growth
compared to contraction periods. The Granger causality test, adapted to asymmetric modeling,
confirms the existence of a feedback relationship: economic growth initially stimulates sector
development, which in turn influences growth asymmetrically. These findings underscore the need
for proactive regulation and targeted awareness policies to maximize the stabilizing role and
catalytic effect of life insurance in the Cameroonian economy.

This study provides the first empirical evidence of an asymmetric effect of life insurance
development on life expectancy in Cameroon, a country where life expectancy at birth remains at
63.8 years in 2023, significantly below international standards. Drawing on the theory of human
capital and the health production function developed by Grossman (1972), we posit that the
relationship between life insurance market development and longevity is nonlinear, characterized
by a ratchet effect where gains in health capital are partially irreversible. To test this hypothesis, we
employ the autoregressive distributed lag (ARDL) approach and the nonlinear ARDL (NARDL)
model developed by Shin et al. (2014), using annual data covering the period 1992–2020. The
analysis is complemented by the fully modified ordinary least squares (FMOLS) method to ensure
structural robustness of the long-run estimates. The empirical results reveal a significant long-run
cointegrating relationship: only expansions of the life insurance market significantly improve
longevity, while contractions have no statistically detectable effect. This fundamental asymmetry
suggests that life insurance development creates permanent improvements in health outcomes that
persist even during subsequent economic downturns. These findings definitively reject the
conventional symmetry hypothesis and highlight the structural and stabilizing role of life insurance
in protecting human capital. The policy implications call for differentiated interventions according
to the economic cycle and a targeted market penetration strategy to maximize sustainable health
gains in emerging economies, particularly in Sub-Saharan Africa where insurance markets remain
largely underdeveloped.

In this study, we propose a dynamic and forward-looking model for solvency capital allocation in insurance companies, combining convex dynamic mixtures of dynamic copulas with multiresolution analysis through wavelets. Our model simultaneously captures the temporal variation of both dependence parameters and copula weights, while also enabling forecasts of the Solvency Capital Requirement (SCR) over different time horizons. This approach overcomes the limitations of standard models, which assume static dependence structures, by incorporating complex, time-varying dynamics that are sensitive to market shocks and extreme events. We apply the model to real data from a large Brazilian insurer, using publicly available microdata from SUSEP. The analysis focuses on the “pricing” submodule of the “non-life underwriting” module, accounting for dependence among incurred claims across major business lines. The results show that the standard model tends to overestimate capital requirements by approximately 13%, whereas our dynamic model provides more parsimonious and risk-aligned estimates. Moreover, the proposed framework reacts prospectively to systemic shocks, adjusting required capital during periods of heightened claims severity. The contribution of this study is two-fold. Methodologically, it advances the theory of dynamic copulas by allowing simultaneous variation in parameters and weights. Practically, we offer a quantitative tool that improves capital calibration, strengthens prudential resilience, and supports strategic decision-making by managers, regulators, and insurers, serving as technical basis for insurers to develop internal models.

Contagion risk refers to the propagation of shocks originating in a specific economic sector (or country) to other sectors or regions. During periods of crisis, this risk tends to intensify, such that a localized adverse event can generate significant losses throughout the system. Traditionally, contagion has been measured through intersectoral dependence, usually assessed using correlations. However, although correlations capture the intensity of dependence, they present important limitations, particularly their symmetry, which prevents the identification of contagion direction. To overcome these limitations, this study proposes a robust approach to identify and measure contagion risk by combining asymmetric dependence measures, causal inference via vine copulas, and wavelet coherence analysis. This combination allows for the following: (i) capturing nonlinear coherences through the flexibility of vine copulas; (ii) incorporating causal and temporal relationships into contagion dynamics; (iii) measuring contagion intensity while removing dependence symmetry; and (iv) analyzing contagion direction across multiple time scales. The methodology was applied to intraday data from various segments of the U.S. economy between 2005 and 2025, focusing on the interactions between the banking and insurance sectors, which are central to the propagation of financial shocks. The results indicate that the banking sector tends to transmit long-term shocks to the insurance sector during crises, whereas in stable periods, both sectors jointly absorb shocks from other parts of the system. In the short term, both sectors respond to shocks originating in areas directly associated with the epicenters of crises, such as the real estate sector during the subprime crisis and the pharmaceutical sector during the COVID-19 pandemic.

Litigation has become an increasingly relevant source of financial pressure for insurers, affecting both operational performance and long-term solvency. Although prior studies have shown that litigation raises operating costs and contributes to premium increases, the literature still lacks a technical, quantitatively grounded study to measure its joint impact on pricing and solvency capital. This study fills this gap by developing an analytical model, based on classical Risk Theory and the exponential premium principle, that derives the optimal sharing of litigation costs between policyholders and shareholders. The model incorporates litigation as an explicit operational cost, links it to the insurer’s adjustment coefficient and ruin probability, and derives closed-form solutions for (i) the optimal solvency capital that minimizes premiums, and (ii) the corresponding minimum premium that ensures technical and economic equilibrium. Using data from the Brazilian insurance market, we calibrate the model across multiple scenarios and quantify both the absolute and percentage effects of litigation on premium loadings and equity-capital requirements. The results show that litigation impacts premiums more strongly than solvency capital, although this effect varies across market segments. Larger insurers are able to absorb litigation costs more effectively through equity capital, partially shielding policyholders from premium increases, while smaller insurers tend to pass on a greater share of these costs via premiums. Beyond offering numerical evidence, this study introduces a formal actuarial criterion for the measurement of judicial provisions, addressing a longstanding gap in both regulation and accounting practice. The model provides insurers and regulators with an objective, replicable, and prudentially consistent tool for allocating litigation costs and managing related liabilities. By quantifying litigation’s dual impact on pricing and solvency, this study contributes to more efficient market functioning, enhanced transparency, and improved alignment between actuarial, accounting, and managerial perspectives.

Introduction:
Climate change has intensified the frequency and severity of weather-related insurance losses, particularly in portfolios exposed to flood and hydrological risk. Classical ruin models generally assume stationary claim dynamics and therefore do not explicitly account for environmental drivers of risk. This study develops a Climate-Adjusted Ruin framework for non-life insurance in which claim frequency and claim severity are modeled as functions of exogenous climate covariates.

Methods:
We consider a collective risk model and extend the classical Cramér–Lundberg framework by introducing climate-dependent claim frequency and severity components. The aggregate loss process is driven by a covariate-dependent counting distribution for claim arrivals and a conditional severity distribution linked to rainfall and temperature through regression-based specifications. The premium rate is fixed at the technical level calibrated under a baseline scenario, allowing an isolated assessment of the impact of climate deterioration on the insurer’s surplus. Finite-horizon ruin probabilities are evaluated via Monte Carlo simulation under alternative climate stress scenarios. Additionally, we construct risk maps and climate-adjusted capital maps describing, respectively, the sensitivity of ruin probabilities and required initial capital to varying environmental conditions.

Results:
The numerical analysis indicates that adverse climate conditions lead to simultaneous increases in expected claim frequency and loss severity, producing a nonlinear amplification of aggregate losses. This effect weakens surplus accumulation and results in a material increase in ruin probabilities over the planning horizon. The corresponding capital maps show a systematic upward shift in the minimum capital required to satisfy a fixed solvency target under joint precipitation–temperature stress scenarios.

Conclusions:
These findings suggest that solvency assessments based on stationary assumptions may significantly underestimate ruin risk in climate-sensitive portfolios. The proposed framework provides an actuarially interpretable and operational approach for integrating environmental covariates into ruin theory, supporting enhanced stress testing, capital adequacy assessment, and climate-aware risk management.

Introduction: Differences in crash severity driven by geography and population density are highly relevant not only for traffic authorities, but also for insurance companies. In European countries, populations are concentrated in high‑density areas, while the most severe injuries and fatalities occur in low‑density regions. This study quantifies how much of the severity gap between high‑ and low‑density areas is explained by differences in the distribution of observable risk factors (composition effect) versus differences in how these factors influence outcomes (structure effect).

Methods: Crash severity is measured using a novel monetary approach based on the aggregate Value of a Statistical Life (VSL) for all casualties. The analysis evaluates differences across the full distribution of crash severity. A counterfactual regression method is applied to decompose differences into composition and structure components.

Results: Using Spanish crash data from 2021 to 2023, results show that differences across density regions in road type, collision type, age, sex, and the involvement of two‑wheelers explain a substantial share of the severity gap up to the median. At higher severity levels, differences in the impact of these factors become increasingly important. Summarizing, the composition effect dominates up to the 60th–70th percentiles of the severity distribution, indicating that differences in the distribution of observable characteristics account for most moderate severity gaps. Beyond these percentiles, the structure effect becomes more influential, suggesting a growing role for unobserved factors in the most severe crashes.

Conclusions:
The findings indicate that insurers should integrate population density into premium design to better reflect heterogeneous risk profiles and ongoing demographic trends such as population ageing and geographic relocation in Europe. Counterfactual analysis provides insurers with a valuable tool for identifying the specific sources of the observed severity differences between high‑ and low‑density areas, thereby enabling more accurate risk assessment and more efficient pricing strategies

In high-risk environments, traditional indemnity insurance is often unaffordable or ineffective, despite its well-known optimality under expected utility. We compare excess-of-loss indemnity insurance with parametric insurance within a common mean–variance framework, allowing for fixed costs, heterogeneous premium loadings, and binding budget constraints. Motivated by the disaster insurance and risk-sharing literature, we show that, once these realistic frictions are introduced, parametric insurance can yield higher welfare for risk-averse individuals, even under the same utility objective and without relying on behavioral assumptions.

The key factor is that indemnity insurance, typically written on a full-value basis, is difficult to scale down: affordability is restored only by increasing deductibles, which can render coverage economically irrelevant. By contrast, parametric insurance allows for explicitly bounded, first-dollar protection that remains feasible even under tight budget constraints. As a result, small but timely payouts can meaningfully improve welfare by alleviating liquidity constraints and supporting early recovery, even when they cover only a fraction of total losses.

We characterize the conditions under which parametric insurance dominates indemnity insurance and show that this advantage is non-monotonic in the available premium budget. It emerges when indemnity insurance becomes impractical, weakens as affordability improves, and disappears once both contracts are unconstrained. Our results reconcile classical insurance theory with the growing use of parametric risk transfer in high-risk settings, highlighting its role as a welfare-enhancing instrument when full indemnification is no longer feasible.

Introduction: Growing scholarly interest in the governance–risk nexus has yet to produce consensus on whether voluntary social disclosure meaningfully reduces firm-level risk, particularly in emerging markets where ownership structures differ markedly from Western contexts. This study examines whether corporate social disclosure reduces firm risk in Saudi Arabia and whether concentrated ownership moderates this relationship.

Methods: Using a balanced panel of 86 non-financial firms listed on the Tadawul over 2019–2024 (N = 516 firm-year observations), we constructed a 48-item Social Disclosure Index hand-collected from annual reports and aligned with GRI standards and Saudi CMA guidelines. Firm risk was captured through annualised stock return volatility, systematic risk (beta), and idiosyncratic volatility. Fixed-effects regressions with cluster-robust standard errors address within-firm heterogeneity, while Two-Step System GMM estimation accounts for potential endogeneity of disclosure decisions.

Results: Greater social disclosure is associated with significantly lower total and idiosyncratic risk, with weaker but directionally consistent effects on systematic risk. Ownership concentration attenuates this risk-reducing benefit: the interaction between disclosure and block-holding is positive and significant, and the disclosure effect becomes statistically indistinguishable from zero only at approximately 93% ownership concentration. Subsample analyses show that the disclosure–risk association is substantially stronger among firms with below-median ownership concentration.

Conclusions: The findings demonstrate that the risk-reduction benefits of corporate social disclosure are conditional on ownership structure. In highly concentrated environments, disclosure becomes less effective, providing evidence that transparency outcomes are fundamentally governance-contingent in emerging markets.