Insurance capital modelling has remained largely unchanged for 40 years — static, deterministic estimates that assume the future will statistically resemble the past, and miss the tail risk that drives solvency and strategic value. Intellegri replaces retrospective analysis that takes months with real-time intelligence.
Current actuarial tools act as a bottleneck. In run-off and M&A, pricing is often based on single-model extrapolations that hide reserve volatility — creating information asymmetry between buyers and sellers. In strategic planning, boards are asked to make forward-looking decisions using backward-looking summaries.
Bayesian methods could solve this by mapping the full distribution of outcomes — but they have historically been too computationally intensive to run at the speed of business, leaving executives to rely on "gut-driven" risk appetite statements.
Intellegri commercialises the Big Hypotheses Model (BHM) — a sovereign-scale inference engine that turns risk management from a retrospective exercise into a live, adaptive process. By overcoming the computational limits of standard Bayesian methods, we deliver three solutions.
For run-off, M&A, and legacy transactions. Value portfolios in days rather than months. A full probability distribution of reserve risk — not a single "best estimate" — eliminates information asymmetry between buyers and sellers, letting deal-makers price the tail accurately and structure transactions on future volatility, not just past performance.
Discuss a transaction →A strategic risk layer that sits above your existing internal models — a sandbox for the C-suite and Board. Run real-time what-if scenarios — entering a new line of business, changing reinsurance structures, adjusting risk appetite — and immediately see the impact on capital efficiency and solvency. Risk management becomes a forward-looking decision dashboard.
Explore the sandbox →A dynamic, probabilistic representation of your risk system that lives and learns. Unlike static actuarial triangles, the Digital Twin continuously updates exposures, claim behaviour, and severity distributions as new data arrives — creating a feedback loop between pricing and reserving, and using regime-switching logic to detect tail events like mass torts or social inflation far earlier than traditional smoothing.
Meet the twin →From a retrospective exercise into a live, adaptive process. A conversation, not a pitch deck.
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