Predictive modeling

The insurtech's proprietary algorithms generate historical coastal data and provide a current view of coastal flood risk, impact drivers and resilience solutions.

The models at the time overestimated the strength of the levees. They also underestimated the exposure of commercial properties.

Climate risk models, which are widely used in the insurance industry, don't typically make detailed projections for extreme heat.

Catastrophe risk information from government bodies and academic institutions would make it possible to insure more homeowners based on more granular assessment, according to insurance industry professionals.

Why next-gen risk models are the key to market resilience.

Even the most-sophisticated scientific models fail to capture all the risk factors or properly account for the increasing impact of climate change.

Bloomberg Green found in an exclusive analysis that risk models often disagree with each other on fundamental assessments of vulnerability.

Applying AI to insurance claims processing improves its predictive analytics, use of predictive models and control of costs.

Insurance industry leaders must proactively develop risk mitigation strategies in order to optimize the use of Gen AI for insurers and their customers, alike

Climate change presents uncertainty for insurance companies but also creates opportunities for technological innovation

Chief underwriting officer finds ways to do more with technology and coverage of risks.

Digital Insurance spoke with three actuaries about emerging technologies and the challenge of explainability within complex models.

AI-powered catastrophe and risk models use varied sources, including aerial imagery.

Rebuilding artificial intelligence algorithms to explicitly integrate transparency and overcome black box limitations.

Predictive models are a tool to address a business problem, not a solution.

The shortcomings of emerging "climate services" are concerning to some observers.

The predictive model is powered by AI analysis based on data from over 1,500 wildfire losses.

How do you maximize efficiency in the application, quoting, and claims process and not invite new or additional fraudulent activity?

One example of a typical deep learning application is pattern recognition applied to digital images.

Current actuarial techniques are good at quantifying in the aggregate – but less so in the detail.