What problem does it solve?
Travel insurance claims are numerous and document heavy. A single trip can produce an airline delay certificate, hotel and taxi receipts, a baggage report, a foreign hospital invoice and a doctor's note, in several languages and formats. Many insurers still review each claim by hand: the US travel insurer in the evidence below handled around 400,000 claims a year manually, each taking ten days to three weeks.
Assistance is the other half. Travellers call from abroad, from other time zones, with a missed connection, a lost passport or a medical emergency. Some calls are simple questions about cover and what to do next, but they can sit in the same queue as the emergencies that need a person immediately, and a single storm, strike or airline disruption can affect many travellers at once.
- Shift Technology states that only about 7% of insurance claims are processed straight through, because most claims data is unstructured and does not fit rules based systems.AI in Action: From zero to 50%+ automation in travel insurance (2025)
How does it work?
- Recognise the traveller and the policy. The agent identifies the policy from the booking or policy reference and confirms the trip details and the cover.
- Triage the situation first. Medical emergencies, safety threats and vulnerable travellers go to the assistance team immediately; everything else continues with the agent.
- Answer cover questions from the wording. The agent explains what the policy covers for this event, citing the policy wording, and tells the traveller what documents to keep.
- Take the claim and read the documents. It collects the facts, reads uploaded receipts and certificates, checks them against the event (for example the flight delay) and the limits, and lists anything missing.
- Settle or hand over. Simple claims within set limits and a clean fraud check are settled automatically; others go to a handler with the documents already extracted and summarised.
- Audience
- Customer facing
- Autonomy
- Supervised agent
- Adoption
- Emerging
- Channels
- Web chat, WhatsApp, Mobile app, Phone and voice, Email
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Automation rate | Too few to pool | Not pooled: up to 70% | 0plus 1 up to | 1 organization |
Value drivers: Customer experience, Lower cost to serve, Speed and cycle time, Inclusion and access.
Indicative value
A travel insurer handling 100,000 claims a year
USD 600,000 to USD 3.2 million
Claims handling cost avoided per year
How this is calculated
Formula: claims * automationShare * costPerClaim. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Claims per year claims, claims per year | 100,000 | 100,000 | The reference insurer. |
| Share of claims decided without a handler automationShare, fraction of claims | 0.2 | 0.4 | Conservative against the evidence on this page (Shift Technology reports that an unnamed US travel insurer raised its automation rate from 0% to 57%), because the mix of simple delay and baggage claims differs by portfolio. |
| Internal cost of handling a simple claim by hand costPerClaim, USD per claim | 30 | 80 | Editorial assumption covering handler time and document review; replace with your own claims expense data. |
What it leaves out: Claims handling cost only. It leaves out assistance calls answered by the agent, faster payment and its effect on renewals and partner satisfaction, fraud impact, and the cost of the platform and integration.
Who already uses it?
2 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Allianz Partners
United States · Insurance · 2026
Allianz Partners' director of partnerships told a US travel advisor consortium in April 2026 that AI assistance now handles 65 to 70% of all its claims. She said this has cut claims turnaround time from about 14 days, the average for the consortium's member agencies, to three to four days, with some claims turned around in a matter of hours.
- Automation rate: up to 70%, Share of claims using AI assistance, stated at Signature Travel Network's Horizon Club, April 2026
"We are right now using AI assistance for 65 to 70% of all of our claims, which has brought our claims turnaround time down from about 14 days, which was the average for Signature partners, to three to four days,"
Claimed by: organization - Cycle time: up to 4 days, Claims turnaround time with AI assistance, for Signature Travel Network member agencies
"We are right now using AI assistance for 65 to 70% of all of our claims, which has brought our claims turnaround time down from about 14 days, which was the average for Signature partners, to three to four days,"
Claimed by: organization
General Insurance Association of Singapore
Singapore · Insurance · 2017
Member insurers of the General Insurance Association of Singapore analyse travel and motor claims together with Shift Technology's AI, which finds connections between people, providers and claims that look genuine when each insurer sees them alone. The data analytics initiative started in 2017 with 25 insurers; fraud alerts are issued to members and prompt joint investigations. The public page gives no outcome figures.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Policy wordings, limits and exclusions per product and distribution partner
- Historical claims with documents and decisions, per claim type
- Assistance protocols for medical, security and travel emergencies
- Flight status or disruption data where delay claims are covered
Systems to integrate
- Policy administration and distribution partner systems for policy lookup
- Claims management and payment systems
- Assistance centre case management and telephony
- Flight status or travel disruption data services
- Fraud detection, including industry data sharing where available
Complexity: Medium
The claim types are standardised and well suited to document extraction, but the agent must work in many languages and time zones, integrate with policy and claims systems that are often run by partners or assistance companies, and route medical emergencies without delay.
- 1
Separate emergencies from everything else
Before automating anything, define the signals that send a traveller to a person at once (medical, safety, minors, vulnerability) and test them in every supported language.
- 2
Automate the simplest claim types first
Travel delay, baggage delay and small cancellation claims have predictable documents and clear limits. Leave medical expenses abroad for a later wave.
- 3
Ground cover answers in the policy wording
Load each product's wording and partner variations into the knowledge base, cite the clause in every answer, and refuse when the wording does not settle the question.
- 4
Check documents against the event
Match receipts, dates and amounts to the insured event and the limits, and use external data, such as flight status, where it confirms the claim.
- 5
Plan for disruption peaks
Airline strikes and storms can produce a sudden surge of claims and calls. Load test the agent and the document pipeline for those peaks.
Guardrails
- Immediate handover to the assistance team for medical emergencies, safety threats and vulnerable travellers
- Automatic settlement only inside limits per claim type with a clean fraud check
- Cover answers cite the policy wording and never promise payment before the claim is assessed
- Automatic declines avoided; unclear claims go to a handler with an explanation to the traveller
- Medical and passport data masked in logs and prompts, with retention aligned to claims files
KPIs to instrument
- Time from emergency signal to a person on the line
- Share of claims settled automatically, per claim type
- Time from claim submission to payment
- Accuracy of automatic decisions on an audited sample
- Satisfaction after claims and assistance conversations, per language
Human in the loop
Assistance coordinators and medical teams handle every emergency. Claims handlers decide every claim outside the automatic limits and review a weekly sample of automatic settlements, and product owners approve each new product wording before the agent answers on it.
Common failure modes
- An emergency handled like a question
- A traveller describing chest pain is asked for a policy number. Detect medical and safety signals first and test them in every language.
- Wrong wording for the product
- Partner specific variations of cover are mixed up. Keep wordings per product and partner, and check which applies before answering.
- Automation that invites fraud
- Fast payment of small claims attracts invented receipts and repeat claims. Keep fraud checks and industry data sharing in the flow.
- Collapse during mass disruption
- The agent and the document pipeline are not sized for a strike or a storm. Load test for peaks and plan a degraded mode.
What are the risks and rules?
EU AI Act
Limited risk (transparency)
A customer facing agent must disclose that it is AI (Article 50), unless this is obvious from the context. Travel insurance claims handling is not listed in Annex III; point 5(c) covers risk assessment and pricing in life and health insurance, not the handling of claims. Handing a traveller who reports a medical emergency to the assistance team is not the classification of emergency calls or the patient triage in point 5(d), as long as the agent only hands over and does not set medical priorities. Claim decisions based solely on automated processing are subject to GDPR Article 22 (and its UK equivalent), and medical data is special category data under Article 9.
Rules that apply
Guidance
- Article 50, transparency obligations for providers and deployers of certain AI systems (European Union, Europe). Travellers must be informed that they are interacting with an AI system unless this is obvious from the context.
- Opinion on Artificial Intelligence governance and risk management (European Insurance and Occupational Pensions Authority, Europe). Addressed to national supervisors (August 2025); sets expectations for governance, fairness and human oversight of AI systems used across the insurance value chain, including claims.
Controls to put in place
- AI disclosure at the start of every conversation, in the traveller's language
- Tested emergency routing rules with response time monitoring
- Documented automatic settlement limits per claim type under change control
- Audit log of every automated decision with documents and rules applied
- Regular review of answers per product wording and language
Frequently asked questions
- How far can travel insurance claims be automated?
- Travel claims suit automation because claim types and documents are standardised. Shift Technology reports that an unnamed US travel insurer handling about 400,000 claims a year raised its automation rate from 0% to 57% (46% pay and 11% deny decisions), with 98% accuracy on pay decisions; the same vendor puts straight through processing across insurance claims at about 7%. Start with delay, baggage and small cancellation claims and leave medical expenses abroad for a later wave.
- Is fraud a bigger risk when travel claims are paid faster?
- It can be, because fast payment rewards invented receipts and claims repeated across insurers. In Singapore, 25 insurers set up a shared data analytics initiative with the General Insurance Association of Singapore in 2017; according to a 2022 Shift Technology case study, its member insurers analyse travel and motor claims with Shift Technology's AI to find connections between people, providers and claims that can look genuine in isolation.
- Should an AI agent handle medical emergencies abroad?
- Only to recognise them and connect the traveller to a person at once. The agent can collect the policy and location details in parallel, but decisions on treatment, evacuation and guarantees of payment belong to the assistance and medical teams.
How to cite this page
Blits.ai AI Use Case Library, "AI agent for travel insurance claims and assistance", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/travel-insurance-claims-and-assistance-agent. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published