AI for Insurance Catastrophe Exposure & Portfolio…

AI for Insurance Catastrophe Exposure & Portfolio Optimization

Geospatial accumulation control, pricing, facultative/treaty optimization, and reinsurance strategy built on high-quality exposure data and AI-driven analytics.

Carriers and reinsurers must manage accumulation across perils (wind, quake, flood, fire), comply with solvency rules, and stay profitable. AI improves exposure data quality, clusters risk, predicts tail losses, and optimizes portfolio mix with constraints. The result: lower model error, better ROE, reduced PML/TVaR spikes, and confident buying of reinsurance.

Accumulation dashboards with heatmaps and thresholds.

Optimization of facultative and treaty layers; attachment/limit selection.

Capital allocation by line/territory; growth guardrails.

live demo · related simulation● LIVE

Quality rules: ranges, construction/occupancy validation, sums insured

Versioned hazard/peril layers; lineage and reproducibility for regulators.

Peril-specific ML scores; clustering to detect accumulations.

Frequently asked questions

What is Explainability: SHAP/reason codes for underwriters?

Explainability: SHAP/reason codes for underwriters.

What are scenario sets related to uncertainty and stress testing, and how do they incorporate tail dependence modeling?

Scenario sets are collections of potential future events used in stress tests. They often include detailed analysis of tail risk – the possibility of extreme, low-probability losses – to ensure robust financial models.

What is the intended implementation roadmap for mysimulator.uk?

The mysimulator.uk platform offers a phased approach to implementation, starting with basic setup and progressing through advanced features like scenario management and risk reporting. Users can tailor their journey based on specific needs and desired capabilities.

How should exposure data be cleaned, geocoded, and enriched with COP information, and what quality assurance measures are needed?

To ensure accurate simulation results, exposure data requires thorough cleaning to remove inconsistencies, geocoding to pinpoint locations, and enrichment with relevant COP (Covered Operations Portfolio) details. Establishing clear QA rules and interactive dashboards is crucial for ongoing monitoring and validation.

▶ Try it live

Everything above runs in your browser — open Stock Price — GBM and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Stock Price — GBM simulation