Every claims pack is different
Photos, handwritten forms, repair estimates in a dozen layouts and reports from different experts. A model tuned on one document type falls over on the next.
AI by industry · Insurance
AI for UK Insurance Claims and Underwriting Teams
Document models that read claims packs and broker submissions, language models that answer wording questions with the clause quoted, and scoring models that put work in the right queue — each tested on your own closed files, with handlers and underwriters deciding cover, price and settlement.
- Every extracted field links to its source page
- No automated claim declines or cover refusals
- Evaluated on your own closed files first
Why insurance AI stalls
Insurance AI stalls between the pilot and the claims floor.
The documents vary, the wordings are long and a decision may need explaining to a customer or the Financial Ombudsman Service. AI has to fit that reality, not fight it.
Where AI pays back in insurance
The steady wins come from reading and sorting, so handlers and underwriters spend their time on judgement:
- Turning claims packs into a structured, referenced summary
- Extracting risk data from broker submissions and schedules
- Answering wording questions with the clause quoted
- Prioritising work queues by complexity and urgency
- Flagging inconsistencies for a counter-fraud specialist
Wordings the model paraphrases
Answers about cover that paraphrase the wording instead of quoting it create risk. Staff need the exact clause, endorsement and schedule entry, not a confident summary.
Scores that look like decisions
A fraud or triage score shown without context can nudge a handler towards a decline. Where the model advises and the person decides has to be explicit in the design.
Outcome data that is hard to use
Closed files hold what a model could learn from, but settlement reasons, reserve changes and fraud outcomes are often free text or coded inconsistently.
What we build
Insurance AI, fitted to claims and underwriting.
Every capability works alongside your policy administration and claims systems, is evaluated on your own closed files, and leaves the decision with a handler or underwriter.
Claims pack summarisation
Document and language models that read the claim form, estimates, reports and photos, and produce a summary of facts, gaps and next steps with every point referenced.
Document processing
Submission and schedule extraction
Insured details, sums insured, locations, claims history and requested cover pulled out of broker emails, spreadsheets and PDFs into your underwriting workbench.
Workflow automation
Wording and endorsement search
Retrieval over policy wordings, schedules and endorsements for each product version, answering staff questions with the exact clause quoted and linked.
Knowledge search
Claim complexity scoring
Machine learning models trained on closed files that estimate how complex a new claim is likely to be, so it reaches the right handler first. Handlers can always re-route.
Predictive analytics
Damage photo assessment
Computer vision that identifies damaged areas and parts in motor or property photos to support the handler or engineer — never a settlement figure on its own.
Computer vision
Counter-fraud link analysis
Connections across claims, parties, addresses and phone numbers surfaced as a network for your counter-fraud team to investigate. A link is a lead, not a finding.
Discuss this
How we work
From closed files to live claims.
Our seven stages with insurance-specific work inside: evaluation on closed files where the outcome is known, a shadow period on live work, and decision boundaries your claims and underwriting leads sign off.
- 01
Discovery
We choose one line of business and one queue, then measure it: volumes, handling time, re-work and how outcomes are recorded in your claims or policy system.
A baseline for one queue
- 02
Strategy
We agree the technique, the data it may use — including how medical and conviction data is handled — and which decisions stay with handlers and underwriters.
Scope, data rules and decision boundary
- 03
UX & architecture
Handler and underwriter screens with the AI’s summary beside the source documents, simple corrections and clear labels on anything the AI produced.
Review screens and data flows
- 04
Development
Models and prompts built against closed files where the right answer is already known, and re-tested on every change.
A pilot measured on closed files
- 05
Testing
Field-level extraction accuracy, citation checks on wording answers, error rates by product and customer group, and behaviour when documents are missing or unreadable.
An evaluation pack for claims and compliance
- 06
Launch
A shadow period on live work — AI output visible to a small team but not relied on — before a staged switch-on with thresholds your leads control.
Shadow results and a staged release
- 07
Optimisation & support
Monitoring of corrections and overrides, updates when wordings and products change, and retraining as new outcomes are recorded.
Monitoring and model upkeep
Technology
Tools for claims and underwriting data.
Chosen for messy documents, long wordings and explainable scores — and connected to your core systems rather than replacing them.
Document AI
OCR and layout parsingVision-capable LLMsSpreadsheet and email parsingConfidence scoring
Language models
Anthropic ClaudeOpenAI GPT modelsGoogle GeminiLlama and Mistral (self-hosted)
Scoring and vision
XGBoost and LightGBMGraph and link analysisPyTorch vision modelsSHAP explanations
Retrieval
Version-aware wording indexesHybrid searchClause-level citationsRe-ranking
Integration
Policy and claims system APIsACORD-style data mappingEmail and portal intakeQueues and webhooks
Controls
Evaluation suitesSpecial category data redactionOverride and audit logsDrift monitoring
We are a software development company, not an insurer, claims adviser or regulator. Your claims, underwriting and compliance leads decide how AI is used; we build to their rules and provide the evidence.
Use cases
Where insurance teams use AI day to day.
Specific tasks with a named owner, from the first notification of loss to the renewal.
Estimate line-item checks
Compare repair estimates with photos and typical costs for the repair described, and flag unusual items for the handler or engineer to query.
Property schedule clean-up
Standardise addresses, occupancy and construction data in property schedules and geocode each location, flagging gaps for the underwriter to chase.
Renewal comparison notes
Compare expiring and renewal terms — limits, excesses, endorsements — and draft a plain summary of changes for the broker to check before it goes to the client.
Claim update drafting
Draft customer updates from the claim file in plain English, with next steps and expected timings, for the handler to edit and send.
Complaint root-cause grouping
Group complaints by cause — delays, communication, settlement — across products so compliance can see patterns. Each complaint is still handled by a person.
Bordereaux anomaly checks
Spot out-of-pattern rows in premium and claims bordereaux — unusual values, duplicates, missing fields — for the team to query with the coverholder.
Why Techsleight
What working with us is actually like.
No inflated numbers — just how we run projects, and what you can hold us to.
Product engineering, not ticket-taking
We ask what the software is for before we estimate it — and we will tell you when something should not be built, or should be bought instead.
AI where it earns its place
LLM features, retrieval and automation built with evaluation, guardrails and cost controls, and plain software where that is the better answer.
Full-stack under one roof
Design, frontend, backend, mobile, cloud and QA in one team, so nothing falls between suppliers.
UK-focused delivery
UK business hours, estimates in pounds, and a contract with a UK company. Our engineers are based in the UK and India.
Flexible engagement
A fixed-scope project, dedicated developers or a monthly retainer — and you can move between them as the work changes.
You own everything
Code, IP, cloud accounts and documentation are yours from day one. We sign an NDA before discovery if you need one.
Support after launch
We stay on for fixes, upgrades and new features, or hand over cleanly to your in-house team with the documentation to match.
FAQs
Questions we get asked.
Straight answers on scope, cost, timelines and how we work. If yours is not here, ask us directly.
Which parts of a claim can AI handle?
The reading and preparing: extracting details from forms and estimates, summarising reports, matching the loss to the relevant policy wording, spotting missing documents and suggesting which handler or team should take it. Decisions on cover, reserves and settlement stay with your handlers, and we design the screens so that is clear to everyone.
How do you evaluate AI on our claims files?
On closed files where the outcome is known. We compare the system’s extractions, summaries and routing suggestions with what your handlers recorded, field by field and by claim type, and review the misses with your claims lead. The same test set is re-run on every change, and results are shared before live use.
How do you avoid unfair outcomes from pricing or claims models?
We measure error rates and outcomes across customer groups, look for proxies such as postcode or occupation that can stand in for protected characteristics, and report what we find to your team with options. Fair value and fair treatment are judged by your firm under its own governance; we give you the data to do it.
How is medical and criminal conviction data handled?
With extra care. This data is restricted to the people and processes that need it, redacted or summarised before general AI processing where the task allows, and logged whenever it is accessed. Your DPO decides the lawful basis and conditions under UK GDPR; we build the controls and document the data flows for your DPIA.
Can AI answer policyholders’ questions about cover?
For general questions, yes — from approved content and with the clause quoted: what a policy covers, how to make a claim, where to find a document. Questions that depend on a specific claim, or sound like a complaint, go to a person. We avoid anything that reads as advice unless your firm has designed for it.
Can the models cope with different wording versions?
Yes. Each wording, schedule and endorsement is indexed with its product and version, so answers come from the version that applied to the policy in question. When a new wording is issued, it is added to the index and the evaluation set is re-run before staff rely on it.
Can you add AI without replacing our core systems?
Yes, and that is usually the better route. AI services sit alongside your policy administration and claims platforms, reading documents at intake and writing structured results back through the vendor’s APIs or file exchange. Where a platform offers no interface, we agree a workable alternative during discovery.
Do you work with brokers and MGAs as well as insurers?
Yes. Brokers tend to start with submissions and renewal comparisons, MGAs with bordereaux checks and underwriting workbenches, and insurers with claims intake and wording search. The approach is the same; what changes is the data each firm holds and who signs off.
How much does an insurance AI pilot cost?
A discovery sprint to choose the queue, check your closed-file data and agree the decision boundary starts from £2,000. The pilot is then quoted at a fixed price, including the shadow period. Model and API fees are paid to the provider and scale with document volumes; we estimate them up front.
Start a project
Have a claims or underwriting queue in mind?
Tell us the line of business, the documents involved and roughly how many cases a month. We will come back with the technique that fits, how we would test it on your closed files and where people stay in charge.
What happens next
- 1A senior engineer reads your brief within one working day, and replies with questions or a first view.
- 2A 30-minute call to understand the goal, constraints and what good looks like — no sales script.
- 3A written proposal with scope, milestones, team and a GBP estimate you can take to your board.
Techsleight Labs is a trading name of Krapton IT Consultancy.
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