The Credibility Report
Actuarial Intelligence for Insurance Professionals
What’s in this edition
Primary-source market updates (no aggregator links) plus the latest actuarial-relevant arXiv papers (score ≥ 15, last 14 days).
📰 Headlines (primary sources)
Insurance Industry And Country Risk Assessment: Canada Mortgage Insurance - S&P Global
Read source → • S&P GlobalRead More about: World Insurance
Read source → • Swiss Re InstituteRead More about: sigma 2/2025: World insurance in 2025: a riskier, more fragmented world order
Read source → • Swiss Re InstituteRead More about: sigma 1/2025: Natural catastrophes: insured losses on trend to USD 145 billion in 2025
Read source → • Swiss Re InstituteRead More about: sigma Resilience Index 2024: Encouraging resilience gains, but more is needed
Read source → • Swiss Re Institute🔬 Research Spotlight (arXiv)
Cloud failure and cyber insurance: calibration of stress scenarios and diversification
arXiv • Score: 28 • 2026-07-21
The expansion of the cyber insurance market remains exposed to the threat of accumulation events that could simultaneously affect a large number of policyholders. Although few such catastrophes have been observed so far, apart from worldwide cyberattacks such as WannaCry and NotPetya in 2017, the nature of cyber risk makes their occurrence plausible. Stress-testing tools are therefore needed to assess whether an insurance portfolio can withstand such crises. In this perspective, the European Insurance and Occupational Pensions Authority (EIOPA) has identified cloud outage as one of the key scenarios to consider in cyber insurance stress-testing frameworks. In this paper, we propose a framework to model and calibrate cloud-outage scenarios and to measure the diversification of a cyber insurance portfolio. We also show how this diversification can protect against accumulation risk and provide underwriting guidelines to reduce the vulnerability of a portfolio to cloud-outage scenarios.
Open paper →Equilibrium analysis in a multi-agent reinsurance chain
arXiv • Score: 24 • 2026-07-17
This paper investigates a multi-layer reinsurance chain within a stochastic differential game framework involving m competing insurers and n reinsurers. Specifically, Stackelberg differential games are employed to characterize the strategic interactions between reinsurance buyers and sellers at each layer of the chain. In addition, a non-zero-sum game model is established to capture the competitive behavior among insurers. Both insurers and reinsurers are allowed to invest in a risk-free asset and a risky asset. To examine the heterogeneity of reinsurance chains under different contract types, the analysis is conducted separately for proportional reinsurance and excess-of-loss reinsurance. By combining dynamic programming and game theory, closed-form equilibrium strategies for investment and reinsurance are derived by solving the extended Hamilton-Jacobi-Bellman (HJB) systems under the mean-variance (MV) criterion. Numerical analysis is conducted to explore the impact of key parameters on the equilibrium strategies. The results indicate that intensified competition in the insurance market leads to a reduction in the safety loadings of reinsurance contracts at each layer of the reinsurance chain.
Open paper →Auditing the Risk Claims of Distributional Reinforcement Learning
arXiv • Score: 15 • 2026-07-13
Distributional reinforcement learning agents learn full return distributions that are increasingly read at face value: for interpretability, risk-sensitive control, and safety monitoring. We ask a question theory anticipates but that has not been measured directly: are the risk claims of a trained distributional agent true? Our audit combines a decision-relevant screening metric (the excess Wasserstein gap between the top two actions, which equals the mass by which first-order stochastic dominance is violated), ground truth from snapshot-restart Monte Carlo, and a statistical harness (permutation nulls, bootstrap refutation, FDR control) without which the audit itself manufactures false conclusions. Across QR-DQN, C51, and IQN on MinAtar (33 runs), 40-95% of the strongest claimed risk trade-offs are refuted at 95% confidence, the placement of the strongest claims is statistically indistinguishable from truth-blind, and essentially no claim is confirmable: for these agents, the learned "risk" reflects a training artifact rather than environment stochasticity. The artifact is structural (fully formed early in training, uncorrelated with final score, idiosyncratic to each seed) and appears unchanged at full-Atari scale, with every top Breakout claim of a pretrained near-state-of-the-art QR-DQN refuted. Positive controls of known magnitude confirm 96-100% of real claims (correlation 0.89-0.92): the reading measures the agents, not the audit. Acting on the heads' CVaR advice at their most-flagged states ranges from beneficial to significantly worse than chance. Neither training for risk nor ensembling removes the artifact, and recalibration passes the audit only by nullifying the claims: the head is uninformative, not merely miscalibrated. We release the toolkit and document two silent pitfalls that produced convincing but wrong audits of our own.
Open paper →✅ Practical Takeaways
- For P&C pricing and capital work, refresh wildfire and severe-convective-storm accumulation scenarios rather than relying on last year’s peril mix.
- For property underwriting, test whether post-wildfire resilient rebuilding standards justify explicit mitigation credits or revised rebuild-cost assumptions.
- For MTPL frequency models, benchmark zone-level coordinates and environmental features against the existing tariff variables before adding more complex image embeddings.
- For health insurance valuation, run stochastic inflation and interest-rate sensitivity alongside deterministic best-estimate calculations.
Until next time—stay credible.
— The Credibility Report
Edition 030 | Prepared July 26, 2026 (UTC)