๐Ÿ›ก Business Case ยท May 2026

The AI-native Insurance Operating System

Insurance fraud costs ยฃ3.2B annually in the UK alone. Claims take 21 days when 4 hours is achievable. Underwriting misses risk signals visible only in aggregate data. InsuranceOS deploys 14 AI agents to detect fraud, accelerate claims, price risk accurately, and keep regulators satisfied.

14 Insurance AI Agents 97% Fraud Detection Rate FCA ยท Solvency II ยท GDPR Compliant 3.2d Settlement vs 21d Industry
Open Live Dashboard ARTlligence โ†—
ยฃ3.2B
Annual insurance fraud cost in the UK โ€” 80% organised and cross-claim, invisible to per-claim manual review
21 days
Industry average claims settlement โ€” InsuranceOS achieves 3.2 days for complex, 4 hours for straight-through
82%
Combined ratio achieved with AI underwriting โ€” vs 94% manual baseline. 34% better risk pricing accuracy.
ยฃ847K
Quarterly subrogation recovery pipeline โ€” 72% recovery rate vs 41% manual. Money left on the table no more.
The Root Problem

Insurance operates on manual processes built for a pre-digital world

Claims handlers review one claim at a time โ€” unaware of the network of connected claims surrounding it. Underwriters price risk from surveys and intuition, missing signals only visible in aggregate. Fraud rings exploit the gap between what's visible per-claim and what's visible across the portfolio.

๐Ÿ” Fraud is organised โ€” detection isn't
Organised fraud accounts for 80% of UK insurance fraud losses. Rings operate across multiple insurers, using shared loss adjusters, solicitors, and repair networks. Manual per-claim review cannot see the network. InsuranceOS does โ€” detecting rings invisible to human reviewers.
โฑ Claims delays destroy customer trust
21-day average settlement drives poor NPS (3.1/5 industry average). 65% of claims are simple and could settle in hours โ€” but sit in the same queue as complex cases. AI triage separates them in 60 seconds, enabling 4-hour straight-through processing.
๐Ÿ“Š Underwriting misprices risk
Manual underwriting misses signals in satellite imagery, financial distress indicators, telematics, and network connections between risks. AI underwriting combines all sources โ€” producing a 34% improvement in pricing accuracy and a 12-point combined ratio improvement.
โš–๏ธ Subrogation money left uncollected
Recovery opportunities are consistently missed when adjusters are focused on settlement. Subrogation AI reviews every settled claim for third-party liability โ€” capturing 72% of recoverable amounts vs 41% manual. For a mid-size insurer: ยฃ340K additional quarterly recovery.
๐Ÿ“‹ Regulatory burden is growing
FCA Consumer Duty, Solvency II, GDPR, Lloyd's reporting โ€” the compliance burden grows every year. Manual compliance monitoring misses conduct risk until an FCA review finds it. InsuranceOS monitors every decision for fair customer outcomes continuously.
๐Ÿ“ˆ Reserving is always a quarter behind
Quarterly actuarial reserve reviews mean reserve deficiencies develop for months before they're visible. Actuarial Intelligence monitors reserve adequacy daily โ€” flagging emerging development trends before they become material problems.
14 Insurance AI Agents

Claims ยท Underwriting ยท Fraud ยท Compliance

Fraud
๐Ÿ” Fraud Detection
Network analysis across claimants, adjusters, solicitors, and garages. Detects organised rings. Cross-references IFB database. 97% detection, 3.2% false positive rate.
Claims
โšก Claims Triage AI
Classifies every claim into straight-through, complex, fraud-suspected, or review in 60 seconds. 65% STP rate. Routes to correct workflow automatically.
Claims
๐Ÿ“„ Document Intelligence
Extracts structured data from police reports, medical records, invoices, and surveys. Validates consistency and cross-references against policy terms and prior claims.
Underwriting
๐Ÿ“Š AI Underwriting Engine
Risk scoring from telematics, satellite, financial signals, and claims history. Recommends premium and terms. 82% combined ratio vs 94% manual. Underwriter approval required.
Underwriting
๐Ÿ“ˆ Actuarial Intelligence
Daily reserve adequacy, loss ratio by line, pricing adequacy signals, emerging risk detection. Replaces quarterly blind spots with continuous visibility. Chief Actuary sign-off required.
Underwriting
๐ŸŒ Catastrophe Risk Monitor
PML estimates from live weather and geological data. Portfolio accumulation of correlated risks monitored continuously. Reinsurance adequacy alerts.
Operations
๐Ÿ’ฌ Customer Intelligence
Churn prediction 90 days before renewal. Coverage gap cross-sell identification. NPS monitoring. Retention: 94% with AI vs 81% pre-AI.
Operations
โš–๏ธ Subrogation AI
Reviews every settled claim for recovery opportunity. Calculates likelihood and value. Drafts letter of claim. 72% recovery rate vs 41% manual. Solicitor review required.
Compliance
๐Ÿ“‹ Regulatory Compliance
FCA Consumer Duty, Solvency II, GDPR, Lloyd's reporting โ€” all monitored continuously. Examination-ready evidence at all times. Zero conduct risk flags.
"InsuranceOS doesn't automate away human judgement โ€” it gives adjusters and underwriters the intelligence they couldn't see before: the network behind a claim, the satellite imagery behind a risk, the fraud ring that spans 4 insurers."
โ€” InsuranceOS ยท AI-native Insurance Operating System
Financial Impact

For a mid-size insurer ยท ยฃ200M GWP

Fraud Prevented (Annual)
ยฃ8.4M
97% detection ร— fraud exposure
Combined Ratio Improvement
โˆ’12pts
94% โ†’ 82% ยท ยฃ24M P&L impact
Claims Cost Reduction
โˆ’18%
Faster settlement + fraud prevention
Subrogation Recovery
ยฃ1.4M
Quarterly uplift ยท 72% vs 41%
Retention Improvement
+13pts
94% vs 81% ยท ยฃ4.2M premium retained
Insurance AI Governance

Advisory intelligence ยท Human authority

โšก
No autonomous claims decisions
All settlement, decline, and reserve decisions require authorised adjuster or underwriter approval. AI recommends and routes โ€” humans decide and bind. FCA Consumer Duty requires explainable outcomes; every AI decision is documented.
๐Ÿ”
Fraud: suspicion not verdict
All fraud flags are presented as concerns requiring investigation โ€” never automated declines. IFB referral and SIU escalation follow defined protocols. The decision to decline or pursue recovery is always made by a qualified claims manager.
๐Ÿ“ˆ
Actuarial: qualified sign-off
All reserve and pricing recommendations require Chief Actuary review. AI provides analysis โ€” qualified actuaries certify reserves per Solvency II requirements. No AI output substitutes for actuarial sign-off where regulatory requirements demand it.
โš–๏ธ
FCA Consumer Duty compliance
Every AI-assisted claims and underwriting decision is monitored for fair customer outcomes. Pricing decisions include explainability documentation. Vulnerable customer signals trigger enhanced human review โ€” never automated outcomes.
๐Ÿ”
GDPR data minimisation
Claims data processed under explicit purpose limitation. Retention schedules enforced. Special category health data handled under Article 9 safeguards. Data subject access rights fulfilled within 30 days automatically.
๐Ÿ“Š
Solvency II capital adequacy
SCR and MCR calculations updated daily. Reinsurance adequacy monitored against PML estimates. ORSA process supported with continuous risk monitoring data. Board-level risk dashboard always current.
Implementation Roadmap

Full deployment in 10 weeks

Phase 1 ยท Week 1โ€“2
Foundation
Data governance & GDPR framework
Claims system integration
Policy admin system connected
IFB fraud database link
Phase 2 ยท Week 3โ€“4
Fraud & Claims
Fraud Detection Engine live
Claims Triage AI active
Document Intelligence deployed
SIU escalation workflow built
Phase 3 ยท Week 5โ€“7
Underwriting
AI Underwriting Engine pilot
Actuarial Intelligence live
Catastrophe Monitor active
Subrogation AI deployed
Phase 4 ยท Week 8โ€“10
Full Operations
Customer Intelligence live
Regulatory Compliance active
FCA Consumer Duty monitoring
AgentOps ROI dashboard live