Understanding the Challenge
Insurance capital modelling has remained largely unchanged for 40 years. The industry relies on static, deterministic estimates that assume the future will statistically resemble the past. This approach fails to capture "tail risk"— the extreme scenarios that drive solvency and strategic value. Whether pricing a legacy transaction or stress-testing a portfolio, insurers are forced to rely on retrospective analysis that takes months to produce, rather than real-time intelligence.
The Limits of Static Modelling
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, leading to information asymmetry between buyers and sellers. In strategic planning, boards are asked to make forward-looking decisions using backward-looking summaries. While Bayesian methods could solve this by mapping the full distribution of outcomes, they have historically been too computationally intensive to run at the speed of business, leaving executives to rely on "gut-driven" risk appetite statements.
Powered by the Big Hypotheses Model
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 can deliver the following solutions:
- Reserve Pricing - Transaction Certainty: For Run-off, M&A, and Legacy transactions. We allow firms to value portfolios in days rather than months. By providing a full probability distribution of reserve risk rather than a single "best estimate," we eliminate information asymmetry between buyers and sellers. This allows deal-makers to price the "tail" accurately and structure transactions based on future volatility, not just past performance.
- Scenario modelling - The Strategy Sandbox: A strategic risk layer that sits above your existing internal models. This is a "sandbox" environment for the C-Suite and Board. Executives can run real-time "what-if" scenarios — such as entering a new line of business, changing reinsurance structures, or adjusting risk appetite — to immediately see the impact on capital efficiency and solvency. It turns risk management into a forward-looking dashboard for decision support.
- Digital Twin - The Living Model: 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. It creates a feedback loop between pricing and reserving, using regime-switching logic to detect "tail events" (like mass torts or social inflation) far earlier than traditional smoothing methods.
