109 résultats pour « insurance »

Insurance in a Changing Climate: A Retrospective Study of Water‑Related Claims and Pricing Strategies in Norway

This study examines climate change's impact on water-related home insurance claims in Norway using a unique dataset. It develops a statistical model to address claim data challenges, reveals geographical and seasonal risk patterns, and evaluates pricing strategies. The findings provide insights for insurers to adapt to evolving climate risks.

Stochastic Loss Reserving: Dependence and Estimation

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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.

How loss reduction costs and testing for severity risk affect insurance decisions

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.

Advancing Pay‑as‑You‑Drive Insurance with Bayesian Models: Risk Prediction and Factor Causal Mapping

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.

FIRE CLAIM SIZE ESTIMATION USING MATHEMATICAL METHODS: MONTE CARLO SIMULATION & SCENARIO ANALYSIS

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.