Building bespoke, insurance‑grade models for complex perils where standard tools fall short.
Standard catastrophe models often fail to capture the nuances of complex, secondary, or highly localized perils. We designed stochastic risk models to fill these critical gaps through bespoke, empirical, and insurance-grade probabilistic modelling. We build customized hazard and loss models from the ground up, specifically tailored to geographic regions or rare, complex threats such as drought, severe cyclones, tsunamis, or compounding earthquake sequences.
Underpinning this process is our comprehensive historical disaster loss database (CATDAT), which provides the rigorous empirical data required for precise model calibration. Thus, we deliver scientifically robust risk curves (AAL, EP), exhaustive scenario catalogues, and raw probabilistic components designed for your exposure requirements. This high-fidelity, R&D-driven approach empowers insurers, reinsurers, and corporate risk managers to quantify complex perils and price risk with scientific certainty.
Custom-built, high-fidelity model components tailored to specific regions or rare perils. We provide the mathematical foundation required to simulate thousands of stochastic events and calculate robust financial loss distributions.
Comprehensive outputs including Average Annual Loss (AAL) and Exceedance Probability (EP/OP) curves. We deliver exhaustive, transparent scenario catalogues that allow risk managers to thoroughly interrogate the model's tail-risk assumptions.
Raw probabilistic data structured for immediate operational use. Whether via direct API integration or custom licensing, we ensure our bespoke loss databases and stochastic event sets plug seamlessly into your existing underwriting platforms.
Standard vendor models often lack the resolution necessary to underwrite secondary perils, complex regional anomalies, or highly localized vulnerabilities. We build probabilistic catastrophe models from the ground up, utilizing cutting-edge empirical research and numerical simulations.
Whether quantifying compounding earthquake sequences, severe convective storms, or localized tsunami run-up, we provide insurers and reinsurers with transparent and robust models that capture the physics of complex threats, enabling accurate risk pricing where traditional tools fail.
Accurate catastrophe modelling requires robust historical calibration. We provide access to CATDAT, our proprietary, globally recognized disaster loss database. By licensing this meticulously curated dataset, which catalogs the socio-economic and structural impacts of historical natural disasters worldwide, we empower financial institutions to validate their own internal models, back-test risk assumptions, and calibrate their underwriting parameters against highly granular, empirical historical loss data.
The insurance industry is increasingly impacted by unmodeled secondary perils and compounding events, such as a severe earthquake triggering subsequent widespread fires or landslides. We mathematically couple independent hazard models to simulate these complex interactions, allowing reinsurers to quantify the true financial tail-risk of cascading, multi-peril disasters.
Parametric insurance requires absolute confidence in the underlying physical data. We design and validate the scientific indices that trigger these policies, whether based on precise seismic acceleration grids, localized flood depths, or complex agricultural drought indices. By minimizing basis risk through empirical precision, we help structure parametric products that are transparent, reliable, and fundamentally sound.
Large‑scale programmes defining resilience strategies for regions or sectors—solving undefined, “first‑of‑its‑kind” problems.
Investigating complex, cascading risks (e.g., pandemics, supply chain shocks).
Developing custom data dashboards for monitoring regional risk and resilience.
