Personal and commercial lines
Intake, coverage against your policy pack, cited outcomes for review, shadow mode before auto-approval.
Claim It Quick · AI claims management
Claims work that does itself.
Claim It Quick runs the claim end to end: intake, document reading, coverage against your policy pack, reconciliation and a cited outcome, with missing information chased and conflicts flagged. Your handlers keep control. Automation follows shadow-mode evaluation — recommendations run in parallel with handlers, not live, until agreement is measured.
01 / Claim It Quick
What the product does on every claim
One path from intake to a cited outcome your handlers can approve, edit or override.
- 01
Intake
Upload or connect Outlook, Gmail, Salesforce or your claims system.
- 02
Read & check coverage
Read documents against your policy pack; flag conflicts; chase missing information.
- 03
Reconcile
Match invoices to authorisations and tariffs; suggest pay, hold or query.
- 04
Cited outcome
Recommend authorise or refer with sources. Never auto-declines.
Claim It Quick · product walkthrough5:27 · music, no voiceover
Filmed on real claims, with anonymised information.Watch on YouTube ↗
The review workspace
Sources and controls in one place
Assessment, cited clauses and handler actions sit together in the review workspace.
Built on governed context
Governed data, shared controls, then claims agents
Connect approved data once, reuse oversight and evaluation, then scope decision logic — the claims-agent layer — to your estate. Hosting can use Amazon Web Services (AWS), Microsoft Azure, Databricks or your own infrastructure.
| What each layer does | Typical components |
|---|---|
| 03Claims agents Rules and wording for a line of business. |
|
| 02Shared controls Oversight, evaluation and integrations reused across agents. |
|
| 01Governed data & context Approved data with origin and permissions intact. |
|
Inside Claim It Quick
From intake to a cited outcome
The desk work Claim It Quick completes before your handlers decide.
01Arrival
Intake from email, portal or claims system
Upload documents, or connect Outlook, Gmail, Salesforce or your claims system so processing starts automatically.
02Read
Document review against your policy pack
Reads rules, limits and definitions across the file. Conflicts are flagged, not chosen silently. Lookups land as dated, cited sources.
03Decide
Cited recommendation to authorise or refer
Recommends authorise, or refer with a lean, with every reason cited to the page. Never auto-declines. Handler corrections become rules once a senior approves them.
04Prove
Shadow mode before auto-approval
Recommendations run alongside handler decisions. Auto-approve only when agreement clears the bar you set.
The problems claims teams face
Why desks stay overloaded
Capacity, claims leakage (unnecessary or drifted payouts), fraud and regulation hit the same team. Sources in References.
- 72%UK insurers disrupted by labour shortages
- ~20%average claims attrition; ~6 years’ experience lost per exit
- 40.7 daysaverage to final payment, US property claims
- 2–5%of payouts estimated as claims leakage (unnecessary or drifted payments)
- £1.34bnfraudulent UK claims detected in 2025
- 32%of storm claims paid in the Financial Conduct Authority (FCA) sample
Market conditions, not a forecast for your book. Citations in References.
How Claim It Quick responds
Map each pressure to the product
Intake, reading, coverage, reconciliation and a cited outcome — so handlers spend time on judgement.
| Pressure | What Claim It Quick does | What stays with your team |
|---|---|---|
| Handler capacity | Assembles evidence; chases missing information; starts from email or your claims system. | Exceptions, customer conversations and final judgement. |
| Inconsistent decisions | Applies your policy pack and cites the clause or page behind each outcome. | Seniors approve corrections before they become rules. |
| Leakage and invoice drift | Reconciles invoices to authorisations and tariffs; suggests pay, hold or query. | Exception review and payment decisions that need a person. |
| Fraud and high-risk files | Flags conflicts and fraud indicators into a human queue; never auto-declines. | Investigation, decline and referral. |
| Cycle time | Automates routine processing; straight-through approval (no human touch inside agreed bands) only after shadow mode. | Thresholds, case types and when automation is switched on. |
| Consumer Duty and audit | Full audit trail: sources, recommendations, overrides and who decided. | Outcomes management information (MI), vulnerable-customer handling and complaints. |
See it on your claim types
Bring a document pack. We will run it in shadow mode against your wording.
Book a demo ↗See also the healthcare claims case study.
Where teams use it
Scoped to your policy pack and claim types
The same pattern for insurers, managing general agents (MGAs), third-party administrators (TPAs) and healthcare payers — configured to your wording, tariffs and authority limits.
High-volume desks
Evidence and invoice reconciliation so handlers focus on exceptions. Declines stay with a person.
Authorisations and tariffs
Prior work settled routine claims in minutes with a full audit trail. Complex cases reach a handler with evidence assembled.
Built for regulated claims
Human control, cited evidence, your infrastructure
Processing prepares the file; adjudication recommends with sources; handlers make the judgement. Declines always stay with a person.
Declines
Human review required
Never auto-declines. Declines, referrals and fraud flags go to your team.
Evidence
Cited to the policy clause
Every recommendation carries reasons linked to the page they came from.
Deployment
On your infrastructure
Runs on AWS, Azure, Databricks, or your own estate.
Data
No training on client data
Your claims data is not used to train models.
More in the UK AI claims software buyer's guide and on processing, adjudication and handling.
Evaluation in your operation
Shadow mode before any automation
Shadow mode means recommendations run in parallel with handlers (not live) until agreement is measured. Enable auto-approval only where that agreement clears your bar. Declines stay with a person.
- 01
Scope
One workflow and line of business. Agree the policy pack, baseline metrics and thresholds. A typical first pilot stays on that single scope.
- 02
Run in shadow mode
The agent recommends with evidence; handlers decide as today. Record agreement and overrides.
- 03
Review exceptions
Decision quality, cycle time and outcomes by case type. Investigate disagreements.
- 04
Enable for eligible types
Switch on approval where evidence supports it. Keep monitoring and escalation.
Weekly evaluation
Measures to track
By case type, with override reasons beside the numbers.
- Decision quality
- Agreement with handlers and override reasons.
- Operations
- Time to decision and share eligible for automation.
- Control
- Citations, guardrails and audit-trail completeness.
- Customer outcomes
- Complaints, vulnerable-customer flags, fair-value exceptions.
- Economics
- Cost per decision versus the manual baseline.
Framework: Data Hatch whitepaper, chapter 6.
Evidence from prior engagements
Prior results; establish your baseline in a pilot
Historical observations from prior Data Hatch engagements — not a forecast for your operation.
Weeks → hours
Routine claims decisions
Faster decisions on routine claims in prior engagements.
8×
Lower cost per claim
Prior engagement result. Your baseline is set in shadow mode.
20%
Book revenue recovered
On historical data in prior engagements; not a projected rate.
Source: whitepaper, p. 15; notes, p. 19. Sample sizes not specified; establish yours in a pilot.
Read the UK healthcare claims case studyWhitepaper 2026
Download the whitepaper ↗
Architecture for enterprise AI agents in regulated insurance
Governed context layer, parallel (shadow-mode) evaluation and human oversight — for chief information, technology and operating officers and claims leaders.
References
Industry sources cited on this page
Published market findings — not a forecast for your operation.
- Insurance Times (1 July 2025), citing Gallagher Bassett, The Carrier Perspective: 2025 Claims Insights: 72% of UK insurers facing disruptions in efficient claims management due to labour shortages.
- Deloitte Insights (18 September 2025): average claims attrition around 20%; each exit associated with nearly six years of experience lost.
- JD Power, 2026 U.S. Property Claims Satisfaction Study (17 March 2026): average 40.7 days to final payment for homeowners claims.
- Celent, The Challenge of Turning AI Value Creation into Value Realization: claims leakage estimated at approximately 2–5% of payouts industry-wide.
- Association of British Insurers (6 October 2026): £1.34 billion of fraudulent claims detected in 2025.
- Financial Conduct Authority, Roadmap for retail insurance: in the FCA sample, only 32% of storm damage claims in 2024 resulted in a payment; see also the FCA’s home and travel claims handling review on outcomes monitoring under the Consumer Duty.
- NIST, AI Risk Management Framework: voluntary guidance on governing, mapping, measuring and managing AI risk — the shape of the oversight we reuse across claims agents.
- Information Commissioner’s Office, guidance on automated decision-making under UK GDPR (notice, representations, human intervention and contest).
Frequently asked questions
How Claim It Quick fits your claims operation
Start with your wording, your documents, and the decisions your team makes today.
Who is Claim It Quick for?
Claims handlers and operations leaders at insurers, managing general agents (MGAs), third-party administrators (TPAs) and healthcare payers who need to cut backlog and claims leakage without removing humans from declines and high-risk decisions.
Which claim types can it handle?
Configured around your policy pack, so it is not limited to one line. Our healthcare claims case study shows medical claims for a UK trust. A demo reviews your claim types and documents for fit.
Can AI decline an insurance claim in the UK?
Claim It Quick never auto-declines. The Data (Use and Access) Act 2025 allows solely automated decisions with significant effects only with safeguards (notice, representations, human intervention, contest — see ICO guidance). The FCA Consumer Duty requires avoiding foreseeable harm in claims handling. Keeping declines with a handler satisfies both.
Can it work with our existing systems?
Upload documents, or connect Outlook, Gmail, Salesforce or your claims system. Integration scope is agreed around your workflow. Deployment can use AWS, Azure, Databricks or your own infrastructure; client data is not used to train models.
Does it make decisions without a handler?
It recommends with reasons linked to sources. Declines, referrals and fraud flags always go to your team. Straight-through approval is optional, only within thresholds you set, and only after shadow mode shows agreement with handler decisions.
How would a pilot start?
Agree claim types, policy pack and evaluation criteria; run in shadow mode against your team’s decisions; review disagreements before enabling any auto-approval.
Book a demo
Put Claim It Quick on the claims that consume your desk
A live demo with the engineers who built it.
Tell us which claim types drive backlog or leakage. We will run the workflow in shadow mode against your documents and policy pack.
What we will cover
Prefer email? sales@datahatch.ai ↗
01 Your claim types, volumes and document sources
02 End-to-end handling: intake, citations and review
03 Shadow-mode evaluation and a path to auto-approval