Confident insurance pricing for thin-data risks
Pricing gets difficult when a risk segment has too few historical claims. Bayesian Risk Intelligence helps underwriters estimate risk by learning from related, better-populated segments while formally incorporating expert judgement.
Price the risk. Understand the uncertainty. Keep the underwriter in control.
- 1Sparse risk segmentlimited claims history
- 2Related risk segmentsbetter-populated data
- 3Hierarchical Bayesian model+ expert priors
- Premium
- £12,400
- Credible range
- £9,800–£15,600
- Confidence
- Moderate
Illustrative example only. Values shown are for demonstration and do not represent customer results.
Thin-data pricing
Built for insurance segments where historical claims data is limited.
Uncertainty-aware
Every estimate is accompanied by a credible range and a confidence signal.
Explainable by design
Outputs are designed to show the influence of data and expert judgement.
Human-in-the-loop
Low-confidence estimates can be flagged for underwriter review.
Pricing risk when the data is thin
Insurers and MGAs launching or pricing emerging and specialty risks often face insufficient historical claims data. Conventional pricing approaches can struggle when an individual segment simply does not contain enough observations.
- Cyber
- Climate-exposed property
- Parametric
- Gig economy
- New SME products
What that can lead to
- Manual actuarial loading to cover the unknown
- Slower pricing decisions
- Greater uncertainty around the final number
- Difficulty confidently entering new risk classes
Borrowing statistical strength
Bayesian Risk Intelligence uses hierarchical Bayesian modelling so a sparse segment can draw on related, better-populated segments. In plain terms: when one risk segment has limited data, the model can learn from statistically related segments rather than treating the sparse segment as an isolated dataset.
Expert underwriter judgement can also be formally incorporated into the model as statistical priors, so professional experience becomes part of the modelling process rather than an informal manual adjustment.
Built for explainable, uncertainty-aware pricing
A statistical pricing layer for underwriting teams — transparent by construction, and honest about what the data can and cannot support.
Hierarchical Bayesian modelling
The statistical core estimates loss frequency and loss severity for sparse insurance risk segments while borrowing information from related, better-populated segments.
Expert judgement
Underwriter knowledge can be formally represented as statistical priors, so professional judgement becomes part of the modelling process rather than an informal manual adjustment.
Confidence intervals
Pricing outputs are accompanied by credible intervals and confidence ranges rather than presenting a single number without any indication of uncertainty.
Confidence scoring
The platform can indicate when an estimate is sufficiently uncertain that human underwriter review is appropriate.
Explainable pricing
The Bayesian approach is designed to make the reasoning behind pricing outputs more transparent and auditable.
Continuous updating
As new claims and policies are received, the model is designed to update its posterior estimates rather than relying only on static historical assumptions.
Related segments
- Professional services
- UK SME
- Cyber SME
Recommended premium
£12,400
Credible range £9,800 – £15,600
- Confidence
- Moderate
- Expert prior
- Included
- Human review
- Required
Demonstration interface with example values. Not customer data or results.
More than a point estimate
The difference is not just the number produced — it is how much context the underwriter receives alongside it.
Traditional approach
- Limited segment data
- Manual assumptions / loading
- Single pricing estimate
Bayesian Risk Intelligence
- Limited segment data + related segment information + expert judgement
- Hierarchical Bayesian modelling
- Recommended premium + credible interval + confidence signal
From sparse data to a defensible pricing decision
- 1
Connect relevant insurance data
The platform works with relevant claims, policy and exposure information.
- 2
Structure the risk
Risks are organised into related segments so the model can understand relationships between sparse and better-populated groups.
- 3
Combine evidence
The hierarchical Bayesian model learns from the target segment and from related segments.
- 4
Add expert judgement
Underwriter knowledge can be formally incorporated through statistical priors.
- 5
Estimate the risk
The model estimates loss frequency, loss severity and the posterior probability distribution.
- 6
Generate a price + range
The output is a recommended premium together with a credible interval and a confidence score.
- 7
Review when needed
If uncertainty is too high, the output can be flagged for human underwriter review.
- 8
Update as new data arrives
As claims and policies accumulate, the model can update its estimates.
Imagine a new insurance segment
An MGA wants to price a new cyber insurance segment for a relatively small group of businesses. Historical claims data for that exact segment is limited.
Instead of relying only on the small dataset, the hierarchical Bayesian approach can draw statistical information from related cyber and SME segments while incorporating relevant expert judgement. The underwriter receives more than a single number — they see how confident the estimate is.
Example output (illustrative)
- Recommended premium
- £12,400
- Credible range
- £9,800 – £15,600
- Confidence
- Moderate
Example figures for illustration only.
The model informs. The underwriter decides.
The platform is designed to support underwriting decisions, not to replace professional judgement. It provides a price together with uncertainty and confidence information, so underwriters can see when a model output can be relied upon and when additional human judgement is appropriate.
Designed for emerging and specialty segments
The platform is built for lines where the pricing problem is a data problem.
Cyber insurance
A fast-evolving exposure where historical claims patterns are short and shifting, making conventional pricing evidence thin.
Climate-exposed property
Changing hazard behaviour means past loss experience for a specific location or peril may be limited or unrepresentative.
Parametric insurance
Structures are often new and narrowly defined, so individual programmes rarely carry deep claims histories.
Gig-economy insurance
Emerging working patterns create novel exposure profiles with few directly comparable historical observations.
New SME insurance products
Newly launched SME propositions start with little or no claims data of their own, yet still need a defensible price.
Subscription pricing linked to premium volume
Pricing is structured around premium volume under management, billed as a recurring monthly subscription.
Tier 1
£1,400/ month
Billed monthly
- For
- Small MGA
- Premium volume
- £4–5m GWP
Designed for smaller MGAs working with thin-data insurance segments and looking for Bayesian pricing intelligence.
Tier 2
£2,600/ month
Billed monthly
- For
- Mid-sized MGA
- Premium volume
- £8–10m GWP
Designed for mid-sized MGAs requiring broader use of the platform across their pricing activities.
Tier 3
£5,000/ month
Billed monthly
- For
- Large / multi-line carrier
- Premium volume
- £15m+ GWP
Designed for larger or multi-line insurance organisations managing larger premium volumes and more complex pricing needs.
Pricing is structured around premium volume under management.
Approx. 0.25%–0.35% of premium under management.
Engagements can begin with a time-boxed proof-of-value pilot on a single segment before moving to the appropriate subscription tier.
Designed for explainability and governance
Insurance pricing operates within a regulated environment. The platform is built with transparency, auditability and data governance in mind.
Transparency
Pricing outputs are designed to be inspectable, with the influence of data and priors visible rather than hidden inside an opaque model.
Model validation
Designed to support customers' own regulatory, model-validation and governance requirements, including FCA and PRA expectations that apply to them.
Data governance
Built with data governance in mind, including the handling expectations set out under UK GDPR and ICO guidance.
Bayesian Risk Intelligence is not a regulated insurer and makes no claim of approval, endorsement or certification by any regulator or supervisory body.
Bring more confidence to thin-data pricing
Explore how Bayesian risk intelligence can support pricing decisions across emerging and specialty insurance segments.
Questions from underwriting teams
Straight answers on methodology, outputs, governance and commercial model.