AI in insurance claims works best as a fast, careful assistant to the adjuster, not a replacement for one. It reads the first notice of loss, pulls the facts out of invoices, estimates and reports, checks the file against the policy and the checklist, flags what looks unusual, and drafts the next letter. The adjuster spends their time on judgment, negotiation and the customer, which is the part of the job that actually needs them.
For a Canadian property and casualty, life or group benefits insurer, the opportunity is large because claims work is high-volume, document-heavy and rule-based. The risk is also real: decisions about claims affect people at a bad moment, and regulators are paying close attention to how AI is used. This guide covers the use cases that pay off first, the systems involved, how to keep a person in the loop, and what a realistic rollout looks like.
Where does AI help most in insurance claims?
Claims handling is a chain of steps, and AI fits some links much better than others.
First notice of loss and intake
Claims arrive by phone, email, web form, broker portal and mobile app, often with photos and attachments. An AI intake step reads the submission, identifies the policy and the type of loss, extracts the date, location, parties and description, and creates or updates the claim in the claims system. Missing information triggers a request to the claimant or broker immediately, instead of when an adjuster opens the file days later.
Document review and extraction
Repair estimates, contractor invoices, police reports, medical notes, receipts and proof-of-loss forms are where adjusters lose hours. Document processing turns them into structured data with a confidence score on every field: amounts, dates, line items, providers, diagnoses or damage descriptions. Low-confidence fields go to a person. For more on how this works across industries, see our guide to intelligent document processing.
Triage and assignment
With the facts extracted, the system can score complexity and severity against your own rules and history, route simple claims to a fast-track queue and complex or injury claims to experienced adjusters, and flag files that need a field visit. Assignment that used to take a supervisor’s morning happens in minutes, and the reasoning is recorded.
Coverage and completeness checks
AI can compare the loss description with the policy wording, endorsements and limits, and list the clauses an adjuster should look at, with the relevant text quoted. It can also check the file against your documentation checklist. It should not make the coverage call. It should make sure the adjuster has everything needed to make it quickly.
Fraud indicators and special investigations referral
Models can surface patterns worth a second look: inconsistent dates across documents, reused photos, a provider that appears on an unusual number of claims. The output is a referral with evidence to your special investigations unit, never an automatic denial. A flag that a person reviews protects honest claimants and keeps investigators focused.
Recovery and subrogation
Third-party liability is easy to miss in a busy file. AI can read the narrative and documents, identify likely recovery opportunities and prepare the subrogation package for review.
Correspondence and status updates
Acknowledgement letters, document requests, status updates and payment confirmations can be drafted from the file and your approved templates, in English or French, for an adjuster to approve or for automatic sending where the content is routine.
Which systems does claims AI need to connect to?
| System | What the AI reads | What it writes |
|---|---|---|
| Claims platform (Guidewire ClaimCenter, Duck Creek, Sapiens or in-house) | Claim, policy, coverage, reserves, notes | New claims, extracted data, tasks, notes, draft letters |
| Policy administration | Wording, endorsements, limits, deductibles | Nothing, read only |
| Document management and email | Attachments, correspondence | Classified and indexed documents |
| Broker and customer portals | Submissions and uploads | Status updates, document requests |
| Estimating and vendor platforms | Estimates, invoices | Discrepancy flags for review |
| Payments | Payment status | Payment requests for approval, never direct release above a threshold |
The integration work is usually the largest part of the build, and it is where projects succeed or stall. We plan it from the first week, using each system’s APIs with permissions scoped to exactly what the workflow needs.
How do you keep a person in the loop?
Human-in-the-loop design is what makes claims AI deployable. In practice, it means four rules.
- Recommendations, not decisions, for anything adverse. Denials, reduced settlements and coverage disputes are always decided by an adjuster. The AI prepares the evidence.
- Confidence thresholds. Every extracted field and every classification carries a confidence score. Below the threshold, a person checks it before it reaches the claim file.
- Authority limits. Straight-through payment is limited to simple claim types under an amount you set, with clear rules and full logging. Anything above goes to an approval queue.
- Everything is explainable after the fact. For each action, the system records the inputs, the documents used, the model version, the output and who approved it. When a customer, auditor or regulator asks why, you can answer.
This design also helps with Québec’s Law 25. A decision based exclusively on automated processing of personal information must be disclosed to the person, explained on request and open to review by a staff member. Meaningful adjuster review keeps most claims decisions outside that rule, and the logs let you meet it where you do automate. Our Law 25 and PIPEDA AI compliance guide explains the requirements in more detail.
What regulations apply to AI in claims?
- Privacy law. Claims files are full of personal and often sensitive information, including health information in injury, disability and life claims. PIPEDA, provincial private-sector laws and Law 25 govern collection, use, consent, safeguards, transfers and incident reporting.
- OSFI Guideline E-23 (Model Risk Management). Final in September 2025 and effective May 1, 2027, it explicitly covers AI and machine learning models at federally regulated insurers: inventory, risk rating, validation, monitoring and governance across the model lifecycle.
- AMF Guideline on the use of artificial intelligence. Published in April 2026 and effective May 1, 2027, it applies to insurers authorized in Québec. It expects board and senior management accountability, a central inventory of AI systems with risk ratings, monitoring of bias and discriminatory variables, clear explanations to clients affected by AI-assisted decisions, disclosure when clients interact with AI, and timely access to a person on request.
- Third-party risk. OSFI Guideline B-10, in effect since May 2024, applies when a federally regulated insurer relies on vendors, including AI and cloud providers.
- Fair treatment of customers. The Canadian Council of Insurance Regulators and the Canadian Insurance Services Regulatory Organizations published joint guidance on the conduct of insurance business and fair treatment of customers in 2018. It covers claims handling and settlement, complaints and the protection of personal information, and it applies whatever technology you use.
The common thread: inventory your AI systems, rate their risk, test them for accuracy and bias on your own data, keep people accountable for decisions, and be able to explain any outcome. Those are good engineering practices anyway. Our security and data handling defaults, including Canadian hosting, private model endpoints and full audit logs, are designed with this in mind.
A realistic rollout for claims AI
| Phase | Weeks | What happens | Decision at the end |
|---|---|---|---|
| Discover | 1–2 | Pick one line and one workflow (for example auto physical damage intake), collect a few hundred closed files, agree on metrics such as cycle time and touch time | Is this the right workflow? |
| Prototype | 3–5 | Extraction and triage run on real historical files, measured against what adjusters actually did | Is accuracy good enough to save time safely? |
| Build | 6–11 | Claims platform integration, approval queues, logging, privacy impact assessment, model risk documentation, testing with adjusters | Ready for a controlled launch? |
| Launch and improve | 12+ | One team or region first, weekly accuracy review, then expansion | Which workflow next? |
Measure what adjusters and customers feel: time from first notice to first contact, time spent per file, number of document requests, reopen rates and complaints. Agree on the baseline before you start, so the result is a number, not an impression.
Common mistakes to avoid
- Starting with fraud scoring. It is appealing, but labels are sparse, false positives harm honest customers and the value is hard to prove. Intake and document work build the data foundation first.
- Skipping the adjusters. The people who handle claims today know the exceptions. Involve two or three of them from week one, and let them set the confidence thresholds.
- Ignoring French. In Québec and for many national carriers, claims documents and correspondence arrive in both languages. Test on both from the start.
- Treating the model as the product. Most of the work, and most of the value, is in integration, review queues, logging and monitoring.
Getting started
The best first project is usually a narrow, high-volume claim type where documents drive most of the work and decisions follow clear rules. Prove it there, then extend the same building blocks (intake, extraction, triage, correspondence) to the next line.
We build these systems for insurance carriers, MGAs and brokers as AI agents that work inside your claims tools, with approval steps where they belong. To see what a first project could look like for your claims operation, try our free AI blueprint, or book a call with a senior engineer.
