138 résultats
pour « insurance »
“In this report we look at the steps taken by banks and insurers since 2021 to respond to the impacts of climate change, and we set out how our regulatory work has evolved in that period. We also look ahead to the planned release, later in 2025, of a consultation paper seeking views on an update to our supervisory statement (SS) 3/19.”
“As the latest climate-related crisis unfolds in Los Angeles, Treasury releases most comprehensive data on homeowners insurance in history, along with report detailing higher costs to homeowners and insurers of elevated climate perils.”
The PRA's new policy on solvent exit planning for insurers aims to ensure orderly market exits. Applicable to most UK insurers, it requires them to develop and implement Solvent Exit Analyses and, when necessary, detailed Execution Plans. The policy comes into effect on June 30, 2026.
Insurers face complex risk dependencies in loss reserving. Additive background risk models (ABRMs) offer interpretable structures but can be restrictive. Estimation challenges arise in models without closed-form likelihoods. Using a modified continuous generalized method of moments (CGMM), comparable to Maximum Likelihood Estimation (MLE), addresses these challenges in certain loss reserving models, including stable distributions.
This paper explores moral hazard in insurance when individuals test for risk severity. It highlights how regulations and loss reduction costs impact behavior. Monetary costs lead to uniform loss reduction, while convex costs drive higher-risk individuals to reduce losses more. Insurers can incentivize risk discovery and reduction through tailored contracts.
The lack of risk transfer stems from structural forces that deter innovation in insurance policies, leading to inefficient risk management and hindering market development. Policy responses can help address these issues.
This study explores a Bayesian approach to Pay-As-You-Drive (PAYD) insurance, using Naive Bayes classifiers and Bayesian Networks for risk assessment. It achieved 87.5% accuracy in predicting risk and improved interpretability over traditional models, optimizing pricing strategies and promoting affordable coverage by dismissing geographic grouping in insurance pricing.
This report uses UK fire statistics to model insurance claims for a company next year. It estimates the total sum of claims by modeling both the number and size of fires as random variables from statistical distributions. Monte Carlo simulations in R are used to predict the probability distribution of total claim costs.
“... we construct a novel factor to measure the aggregate physical climate risk in the financial market and discuss its applications, including the assessment of insurers’ exposure to climate risk and the expected capital shortfall of insurers under climate stress scenarios.”
A meta-analysis of contingent valuation studies on voluntary insurance for low-probability, high-impact risks finds demand lower than expected. Stated willingness to pay (WTP) averages 87% of expected losses. Factors like loss probability information positively affect WTP, while income and age negatively influence it. Cultural and methodological factors also impact WTP.