113 résultats pour « insurance »

Building up Cyber Resilience by Better Grasping Cyber Risk Via a New Algorithm for Modelling...

"We propose here an analysis of the database of the cyber complaints filed at the Gendarmerie Nationale.We perform this analysis with a new algorithm developed for non-negative asymmetric heavy-tailed data, which could become a handy tool in applied fields. This method gives a good estimation of the full distribution including the tail. Our study confirms the finiteness of the loss expectation, necessary condition for insurability."

Financing Constraints and Risk Management: Evidence From Micro‑Level Insurance Data

"Using data on credit scores matched with unique information on firm level commercial insurance purchases, we find that financing constraints lead to higher insurance spending. We adopt a regression discontinuity design and show that financially constrained firms spend 5–14% more on insurance than otherwise similar unconstrained firms. "

Prediction of Auto Insurance Risk Based on t‑SNE Dimensionality Reduction

"... we develop a framework based on a combination of a neural network together with a dimensionality reduction technique t-SNE (t-distributed stochastic neighbour embedding)... The obtained results, which are based on real insurance data, reveal a clear contrast between the high and low risk policy holders, and indeed improve upon the actual risk estimation performed by the insurer."

The Government Behind Insurance Governance: Lessons for Ransomware

"This paper analyzes how governments support insurance markets to maintain insurability and limit risks to society. We propose a new conceptual framework grouping government interventions into three dimensions: regulation of risky activity, public investment in risk reduction, and co-insurance."

Difference between integrated quantiles and integrated cumulative distribution functions.

"When developing large-sample statistical inference for quantiles, also known as Values-at-Risk in finance and insurance, the usual approach is to convert the task into sums of random variables. The conversion procedure requires that the underlying cumulative distribution function (cdf) would have a probability density function (pdf), plus some minor additional assumptions on the pdf. In view of this, and in conjunction with the classical continuous-mapping theorem, researchers also tend to impose the same pdf-based assumptions when investigating (functionals of) integrals of the quantiles, which are natural ingredients of many risk measures in finance and insurance. Interestingly, the pdf-based assumptions are not needed when working with integrals of quantiles, and in this paper we explain and illustrate this remarkable phenomenon."

Insurance and Enterprise: Cyber Insurance for Ransomware

"As businesses improved their resilience, cybercriminals adapted and ransoms escalated, calling insurability into question. Yet there remains little appetite for imposing restrictive conditionality in this highly competitive market. Instead, insurers have turned to governments to contain criminal threats and cushion catastrophic losses."