Nos derniers articles

"... we study two optimisation settings for an insurance company, under the constraint that the terminal surplus at a deterministic and finite time T follows a normal distribution with a given mean and a given variance."

"An efficient Bayesian Markov Chain Monte-Carlo method is developed to estimate the unknown parameters to address the computational complexity. Our empirical application to the mortality data collected for the Group of Seven (G7) nations demonstrates the efficacy of our approach."

"... blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities."

"Using variation across insurers within the same country, and across countries for the same insurance group, we show that market risk insurance via guaranteed return products is more prevalent in countries with more lax capital requirements. Moreover, we show that the interest rate exposure of insurance companies increased as interest rates declined in recent years, and this effect is more pronounced for companies with a larger share of guaranteed return products. "

"... in order to reach positive changes to take place and for intellectual property insurance to become an increasingly preferred method for supplementary protection of intellectual property, the proposed approach is to be worked in three directions. ... Namely, the directions are building a unified system, with a strict methodology and at the same time creating a legal basis which to bring in detail and unequivocal means and approaches the way in which intellectual property insurance is carried out."

"... this paper proposes a new risk variable elimination method as well as a real-time road risk model design framework and concludes that claim history will be regarded as a "noise" factor and deprecated in the Pay-How-You-Drive model."

"Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation."

"... this book chapter evaluates how policymakers' approaches to systemic risk regulation in insurance have evolved since the crisis. It tracks how international standard-setting organizations and U.S. regulators initially relied on the entity-based approach, using discretionary methodologies for identifying specific nonbank firms, including insurers, that were systemically significant. It then shows how, in response to backlash, international and U.S. policymakers abruptly ceased entity-based designations and purported to shift their focus to an activities-based approach to nonbank systemic risk."

"We... apply two stochastic orders to some classic decision problems in economics and finance including a portfolio problem, two insurance problems, and four management decision problems and present a simple sufficient condition for monotone comparative statics of changes in risk under risk aversion."

"…. the surplus of an insurance company is routinely approximated by a Brownian motion, as opposed to the geometric Brownian motion used to model assets in finance. Furthermore, exposure to risk is controlled "downwards" via reinsurance, rather than "upwards" via risky investments. This leads to interesting qualitative differences in the optimal solutions."