arXiv subjects

Daniel Nkameni

Publications and source records attributed to Daniel Nkameni.

4 recordsLinked to original sources

arXiv2026

Cloud failure and cyber insurance: calibration of stress scenarios and diversification

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.

q-fin.RM↗

arXiv2026

A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence

According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion USD between 1970 and 2000 to 180--200 billion USD between 2001 and 2020. Reports from organizations such as the IFOA and the WWF highlight the need for the insurance sector to adapt to this rapidly evolving context by developing medium- to long-term strategies that go beyond the one-year horizon of prudential regulations such as Solvency II. This paper introduces an artificial intelligence framework based on Conditional Generative Adversarial Networks (Conditional GANs) to generate future spatio-temporal trajectories of climatic indices. The approach focuses on the Soil Wetness Index (SWI), a key indicator used in France to assess drought severity. Drought accounts for approximately 30% of the indemnities paid under the French natural catastrophe insurance scheme. The proposed model, SwiGAN, simulates plausible drought propagation patterns up to 2050 for a region of France particularly exposed to this hazard. By generating realistic sequences of SWI maps, SwiGAN provides insights into drought dynamics under climate change scenarios and supports the design of adaptive risk management and insurance strategies. The methodology is also generalizable to other climate-related perils and actuarial applications such as economic scenario generation.

cs.LG↗

arXiv2026

Combination of traditional and parametric insurance: calibration method based on the optimization of a criterion adapted to heavy tail losses

In this paper, we address the problem of providing insurance protection against heavy-tailed losses, for which the expected loss may not even be finite. The product we study is based on a combination of traditional insurance up to a given limit and a parametric (or index-based) cover for larger losses. This second component of the coverage is computed from covariates available immediately after the loss occurs, allowing claim management costs to be reduced through rapid compensation. To optimize the design of this second component, we use a criterion adapted to extreme losses, that is, to loss distributions of Pareto type. We support the calibration procedure with theoretical results establishing its convergence rate, as well as empirical evidence from both a simulation study and a real-data analysis on tornado losses in the United States. We also propose a two-step optimization procedure as a potential solution to the issue of data scarcity in the tails of loss distributions. We conclude by empirically demonstrating that the proposed hybrid contract outperforms a traditional capped indemnity contract.

q-fin.RM↗

arXiv2026

Index insurance under demand and solvency constraints

Index insurance is often proposed to reduce protection gaps, especially for emerging risks. Unlike traditional insurance, it bases compensation on a measurable index, enabling faster payouts and lower claim management costs. This approach benefits both policyholders, through quick payments, and insurers, through reduced costs and better risk control due to reliable data and robust statistical estimates. An important difference with the concept of Cat Bonds is that the feasibility of such coverage relies on the possibility of mutualization. Mutualization, in turn, is achieved only if a sufficiently high number of policyholders agree to subscribe. The purpose of this paper is to introduce a model for the demand for index insurance and to provide conditions under which the solvency of the portfolio is achieved. From these conditions, we deduce a product that combines index and traditional indemnity insurance in order to benefit from the best of both approaches. We illustrate our results with a practical example involving the design of an index insurance product in the field of cyber insurance.

q-fin.RM↗