Problem 01
Models can't be re-baselined
The training data was never versioned. Twelve months in, you can't reproduce the model the regulator approved, and without that baseline, you can't approve a new one either.
Solutions · Financial Services
Underwriting that clears the supervisory college.
ML-driven underwriting with explainability in the architecture and model-risk evidence (CP24/2 · SR 11-7 · SS1/23) as a primary deliverable.
Solutions we provide
Six underwriting workloads, made defensible.
Every workload reads from the same versioned feature store and ships the explainability a supervisory college expects. Start with the model costing you most.
Why most ML underwriting stalls
Four architectural failures that end in a regulator's no.
Most ML underwriting stalls on the engineering around the model, not the model itself, four gaps that compound until supervisory review says no.
Compliance is not a model feature. It's the feature store and the audit trail.
- 6 mo
- Silent feature drift before a degraded model is noticed
- 0
- Reproducible builds, so the approved model can't be recreated
- 2 wks
- Pre-launch scramble when the model card is written retroactively
Most vendor builds fail one test: the model card doesn't match the actual training run.
- Problem 02
Features drift unobserved
Production features drift away from the training distribution and the model degrades silently. By the time anyone notices, six months of bad underwriting decisions are already baked in.
- Problem 03
Explainability is bolted on
SHAP or LIME is bolted on after launch, inconsistent from one decision to the next. The adverse-action notices it generates fail FCA conduct review when they're examined.
- Problem 04
Model-card is written retroactively
The model card is drafted in the final two weeks before launch. The supervisory college rejects it because it doesn't match the actual training run it claims to describe.
What changes once it ships
Four things a regulated lender can take to the board.
Not features: outcomes a chief risk officer, head of credit, or model-validation lead can defend in the room.
Every decision is explainable.
Each approval and decline is bound to per-decision feature attributions and a reason code, consistent across decisions, not a notebook plot. Adverse-action notices pass FCA conduct review because they say exactly why.
- Per-decision SHAP at scale
- Reason codes from a versioned mapping
- Adverse-action notices generated per decision
- Counterfactual explanations on request
Approve more at the same risk.
A decoupled serving layer lets you shadow-score a challenger against the incumbent, promote on evidence, and lift approvals without lifting losses, 23% more approvals at the same default rate in year one.
- +23% approval rate at the same default rate
- Champion / challenger with shadow scoring
- A/B traffic split before promotion
- Real-time and batch from one serving layer
It survives re-baselining.
A versioned, point-in-time-correct feature store is the audit trail; the model is just the surface. Twelve months on, you can reproduce the exact model the regulator approved, and detect the drift that would otherwise degrade it silently.
- Versioned datasets + code = reproducible builds
- Point-in-time-correct feature store
- Continuous PSI drift detection per segment
- Swap the model without re-baselining the audit
It clears the supervisory college.
Model-risk evidence is a primary deliverable, not a final-stage scramble. Every release ships a model card that matches the actual training run, formatted to CP24/2, SR 11-7, or PRA SS1/23: 100% first-pass clearance at second-line review.
- 100% first-pass second-line clearance
- Model card matches the actual training run
- CP24/2 · SR 11-7 · PRA SS1/23 formatting
- Bias / fairness gates in the training pipeline
Why BritonOne Technology
Why regulated lenders choose BritonOne Technology
Not features: four reasons a chief risk officer or model-validation lead can defend in the room.
Supervisory-college-fluent by design
CP24/2, SR 11-7 & SS1/23 evidence is a primary deliverable, not a late scramble.
Explainable, never a black box
Per-decision SHAP and reason codes, consistent and ready for FCA review.
First model live in eight weeks
A phased path from data audit to a supervisory-cleared production model.
The model card matches the training run
Versioned data and code make the approved model always reproducible.
How we build
From audit to supervisory-cleared production.
Five stages, each with a bounded output you can hold us to. The model-risk evidence is built alongside the model, never a final-stage scramble.
- Week 1–2
Discovery
We review the data estate, map the regulatory surface (CP24/2, SR 11-7, SS1/23), and commit to a bounded scope and a costed roadmap, not an open-ended retainer.
- Week 3–5
Feature store & pipeline
The versioned, point-in-time-correct feature store goes live, the training pipeline is made reproducible (DVC + Git), and the first model is trained against hold-out and temporal validation.
- Week 6–7
Serving & explainability
The decisioning API and SHAP-at-scale explainability service go live, shadow-scoring the new model against the incumbent so promotion is on evidence, not faith.
- Week 8
Model card & review
The model card (matching the actual training run), the validation report, and the SS1/23 / CP24/2 supervisory-review pack are produced and signed off by second-line risk.
- After go-live
Monitor & extend
Continuous drift monitoring with PSI alerts, automated quarterly post-market surveillance, and additional models added under the same evidence and re-approval workflow.
Success stories
Programmes we have shipped
3.1x
Insurance
Underwriting copilot for a speciality insurer
Drafted first-pass quotes from submission packs 3.1x faster, every clause cited back to the wording it came from.
PythonAzure OpenAILangChainPostgrespgvector
Country · UK
£14M
Insurance
GenAI opportunity assessment for an insurance group
Mapped a £14M annual benefit pipeline across 40 use cases, sequenced by risk.
StrategyValue modellingRisk assessment
Country · DE
60→1
Insurance
Underwriting analytics dashboards for an insurer
Replaced 60 spreadsheets with one governed underwriting board pack for an insurer.
Power BITabularAzuredbt
Country · UK
100%
Insurance
Test automation for an insurance quote platform
Automated quote, coverage, and claims journeys for a US insurer, holding 100% of critical paths green across a fast release train.
SeleniumJavaTestNGJenkins
Country · US
61%
Legal
Clause-level contract drafting for a commercial law firm
Cut first-draft time on regulated commercial contracts by 61%, each clause traceable to firm precedent.
TypeScriptAWS BedrockAnthropicOpenSearchReact
Country · UK
18%
Foodtech
Churn prediction for a foodtech subscription platform
Cut voluntary churn 18% in two quarters with next-best-action scoring for a meal-subscription platform.
PythonXGBoostSnowflakedbtMLflow
Country · UK
22%
Retail
Demand forecasting for a grocery retailer
Cut waste 22% across 900 stores with SKU-level probabilistic forecasts.
PythonPyTorchDatabricksDelta Lake
Country · FR
47%
Insurance
Motor damage assessment from photos for an insurer
Settled 47% of motor claims from images alone, with fraud flags surfaced inline.
PythonPyTorchDetectron2AWS
Country · UK
3x
Insurance
Claims platform rebuild for a general insurer
Replaced a 15-year-old claims system, tripling throughput per handler.
C#.NETAzureSQL ServerReact
Country · UK
4.8★
Retail
Loyalty and shopping app for a retailer
Launched a retail loyalty app to a 4.8-star rating and 200k users in 90 days.
SwiftKotlinReact NativeAWS
Country · UK
60%
Retail
Lakehouse build for a grocery retailer
Unified 40 sources into a governed lakehouse, cutting report build time 60% for a grocery retailer.
DatabricksDelta LakeSparkTerraform
Country · UK
45%
Automotive
Cloud warehouse migration for a carmaker
Migrated a legacy warehouse to the cloud, cutting query cost 45% for a carmaker.
SnowflakedbtFivetranTerraform
Country · DE
38%
Healthcare
Claims and billing analytics for a hospital group
Unified claims and billing data, cutting rejected-claim rework 38% for a hospital group.
SnowflakedbtFHIRPower BI
Country · UK
3x
Foodtech
Self-service analytics for a foodtech platform
Self-service models tripled analyst throughput at a food-delivery platform.
LookerdbtBigQuery
Country · FR
30+ edge cases caught
Fintech
Exploratory and regression testing for a customer-management platform
Exploratory and regression testing across pipelines, analytics, and customer records surfaced more than thirty edge-case defects that scripted suites had missed, hardening a German fintech's CRM before a major rollout.
WebTestRailJiraPostman
Country · DE
p95 latency flat
Fintech
Scalability and endurance testing for an analytics platform
Kept p95 latency flat as audience data and concurrency scaled, proving the analytics dashboards would not slow as customers grew.
k6LocustGrafanaDatadog
Country · US
Who we are
About usBritonOne Technology is a full-cycle engineering company that builds and operates production software for regulated estates. Since 2017, we have shipped programmes that clear audit on the first pass across banking, insurance, wealth, healthcare, and biotech. Our teams pair deep domain knowledge with disciplined engineering, treating compliance, security, and resilience as first-class deliverables. From architecture through to live operations, we stay accountable for the systems we build, measuring success by uptime, audit outcomes, and defensible business results.
60+Senior engineers across UK and EU
Why choose us
Engineer experience, average9+ yrs
Specialist replacement window48h
Code and IP ownership, day one100%
Surprise invoicesZero
What underwriting clients say
Words from the regulated teams we shipped with.
Anonymised under MNDA, verifiable on reference call. Each quote is from a senior owner who carried the engagement through second-line review.
The model card matched the training run
“BritonOne Technology shipped the only underwriting model where the model card matched the actual training run. That sounds basic; it's not. Most vendor builds we've seen fail that test, and fail their supervisory review because of it.”
Head of RiskTier-2 UK retail bank
FAQ
Common questions about smart underwriting.
