Sovereign-Scale Intelligence
The Big Hypotheses Model (BHM) is not a standard insurance model. It is a general-purpose Bayesian intelligence system, originally funded by the UK Government to forecast complex, uncertain systems for national security. Developed by over 80 post-doctoral researchers at the University of Liverpool, it answers questions that cannot be wrong. Intellegri has repurposed this defence-grade engine exclusively for financial services, providing a level of probabilistic rigour previously unavailable to the insurance market.
The Failure of Static Models
Current capital models, such as the industry-standard Mack Chain Ladder, assume the future will statistically resemble the past. As a result, they fail to accurately capture "tail risk"— the extreme events that drive insolvency. Our validation against 332 insurance portfolios proves that these legacy methods significantly understate risk by being over-confident, while traditional Bayesian methods are often over-cautious, reducing potential returns. Insurers are left choosing between hidden insolvency risk or trapped capital.
Defence-grade capabilites
The BHM bridges the gap between accuracy and speed, utilizing patented processes to deliver results in minutes rather than months:
- Hierarchical "Borrowing of Strength": The model uses hierarchical Bayesian learning to pool information across portfolios. This stabilizes estimates for sparse or volatile data (such as new lines of business) by "borrowing strength" from broader datasets, preventing the noise that leads to reserving errors.
- "Glass Box" Methodology: Unlike "Black Box" AI or Neural Networks, the BHM is a "Glass Box." It provides fully explainable, transparent reasoning for every output. This ensures auditability and builds trust with stakeholders.
- High Performance Computation: We address the historic computational limit of Bayesian modelling. By trading model complexity for parallelism, our patented architecture targets a computational speed increase of up to 86,400×. This breakthrough is designed to transform capital modelling from a months-long batch process into a near real-time calculation, enabling decision-grade agility.
The Economically Optimal Zone
Validation confirms that the BHM is the only solution that sits in the "economically optimal zone"— accurate, but not misleadingly precise:
- Tail Risk Mastery: We move beyond single-point estimates to map the full probability distribution of outcomes. This allows insurers to price the "tail" accurately, quantifying uncertainty to release trapped capital without increasing insolvency risk.
- Live Digital Twins: The model can create a probabilistic "Digital Twin" of your risk portfolio. This allows executives to run real-time scenarios as adding liabilities or changing reinsurance structures to see the immediate impact on solvency before committing capital.
- Optimised Return on Equity: Validated to sit in the "economically optimal zone", the BHM prevents the destruction of shareholder value caused by over-cautious legacy models. By accurately quantifying uncertainty, we allow firms to safely release trapped capital and redeploy it into profitable growth or underwriting opportunities, transforming risk management from a cost centre into a driver of value.
This is not just a better model; it is a complete reframing of how the industry understands uncertainty. Intellegri transforms risk management from a retrospective compliance exercise into a live, forward-looking strategic advantage, delivering certainty for uncertainty.
