The 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 — built by over 80 post-doctoral researchers at the University of Liverpool to answer questions that cannot be wrong. Intellegri has repurposed this defence-grade engine exclusively for financial services.
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 a broad range of insurance portfolios proves that 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.
The BHM bridges the gap between accuracy and speed, utilising patented processes to deliver results in minutes rather than months.
Hierarchical Bayesian learning pools information across portfolios. This stabilises 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.
Discuss your portfolio →Unlike black-box AI or neural networks, the BHM is a glass box. Fully explainable, transparent reasoning behind every output — ensuring auditability and building trust with stakeholders.
Discuss auditability →We address the historic computational limit of Bayesian modelling. By trading model complexity for parallelism, our patented architecture targets a speed increase of up to 86,400× — transforming capital modelling from a months-long batch process into a near real-time calculation.
Discuss integration →Validation confirms the BHM is the only solution that sits in the economically optimal zone — accurate, but not misleadingly precise.
Legacy chain-ladder methods — hidden insolvency risk from understated tails.
Accurate, honest uncertainty. Capital released safely.
Traditional Bayesian methods — trapped capital, reduced returns.
We move beyond single-point estimates to map the full probability distribution of outcomes — pricing the tail accurately, quantifying uncertainty to release trapped capital without increasing insolvency risk.
A probabilistic digital twin of your risk portfolio. Run real-time scenarios — adding liabilities, changing reinsurance structures — and see the immediate impact on solvency before committing capital.
By accurately quantifying uncertainty, firms 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.