Input data and information sources
Claimants, repair shops and experts supply claims, photographs, invoices and reports. Claim files identify insured people, injured parties, witnesses and number plates.
AI-assisted insurance claims handling
Also known as: AI-assisted claims handling
The system analyses claim documents and photographs, checks entitlement against the policy, flags inconsistencies and prepares settlement.
Understand the use case
What defines this use case?
Language models and image recognition extract information from claims, invoices, expert reports and photographs and compare the claim with the policy. Insurers use this for motor and property claims; Allianz states that people still decide coverage and payment.
This page describes a typical system design. Actual data flows and permissions depend on your implementation.
- System design
- Analysis and processing
- Ability to act
- Action permissions depend on integration
- Fields of use
- insurance
- Also known as
- AI claims settlement, claims automation
How it works, step by step
System design and data flows
Input data
Claims, photographs, invoices and reports enter the claim file alongside policy data.
Claimants, repair shops and experts supply claims, photographs, invoices and reports. The system analyses damage photographs alongside invoices, expert reports and policy data.
Processing
The system reads documents, analyses images, checks the claim against the policy and estimates damage.
The system assigns notifications and documents to a claim and extracts or evaluates relevant information. This can produce prioritisation, a recommendation for review or a processing proposal. Payment or rejection is a further process step with its own rules and approvals.
Results
The system proposes settlement, flags inconsistencies and drafts correspondence if needed.
The result describes the defined model task. A match, category or forecast acquires its professional significance only in conjunction with the underlying data and intended use.
Use and impact
Claims staff decide coverage and payment.
Integration determines whether the result is simply displayed, adopted after approval or written directly into a business application. These variants differ in their data flows and permissions.
Explore the system design: Select a component to see its task, incoming and outgoing data, and role in the use case. The guided walkthrough explains each phase. Then use Yes, Uncertain and No to represent your specific system variant.
8 review first30 clarify7 baseline threats
- Present
- Depends on your system
- Absent
- Review first
- To clarify
How to use the model
The diagram shows the typical architecture. Select a component to see what happens there and adapt its features to your system. Enable “Analysis guidance” to see which threats need reviewing for this architecture. The page's checklist continues to describe the typical case.
Changes from the typical case
None yet. Each change will appear here.
Where most threats arise
- 1 review first, 13 clarify
- 6 clarify
- 5 review first
- 4 clarify
In this system design: 8 to review first, 30 to clarify, 7 baseline threats.
Architecture as a list
External access: Depends on your system
To clarify
Input: Part of every AI system
Review first
- Multimodal Attacks Review priority
- Model Denial of Service
- Privacy Attacks
- Sensitive Information Disclosure
- Exfiltration from ML Application
Baseline for every AI system
Third-party content: Present
AI model: Part of every AI system
To clarify
- Sleepy Agent (Time/Event-Triggered Hidden Instructions)
- Prompt Injection: Direct
- Prompt Injection: Indirect
- Jailbreaks
- Meta Prompt Extraction
- Misalignment
Baseline for every AI system
Provider service: Present
Review first
To clarify
Document collection: Depends on your system
To clarify
Training and feedback: Depends on your system
To clarify
Tools and interfaces: Depends on your system
To clarify
Result: Part of every AI system
Review first
- Insecure Output Handling Review priority
To clarify
Business application or equipment: Depends on your system
person: Part of every AI system
Review first
- Overreliance Review priority
Affected person: Present
Unapproved AI services: Depends on your system
To clarify
Supply chain and technical foundations: 5 baseline threats applying to every AI system
Baseline for every AI system
How it differs from similar use cases
Intelligent document processing (IDP)
Routes incoming correspondence and transfers information without checking entitlement
Life and health insurance risk assessment and pricing
Assesses a person's risk and premium before policy inception
Data and information
Processing and operating environment
Language models and image recognition generally come from a provider.
Storage and further use
Depending on design, the system retrieves policy terms and agreements from a document collection to check entitlement. Damage estimation models often learn from the insurer's own claims; language models are usually used unchanged.
Results and data sharing
The insurer determines whether it automatically approves small payments or only presents a proposal. Cloud services send claim files and photographs outside the organisation's environment.
Using a provider does not automatically determine where processing takes place. Storage location, subcontractors, deletion periods and use for training must be checked for the specific deployment.
Assess risks and determine measures
From understanding the system to analysing risks
- Define the system boundary
Record data, participants, interfaces and permissions. Resolve uncertain characteristics in the model.
- Investigate threats
Relate review candidates to actual data flows: what could happen, and who would be affected?
- Assess risk
Assess impacts and likelihood in the deployment context. The number of review candidates is not a risk score.
- Provide evidence of measures
Select appropriate controls, assign responsibilities and define effectiveness checks. Document residual risks and approvals.
Key threats in the application context
- 3 particularly important
- 5 typical
- 30 depending on the system
- 7 additional threats for every AI system
A total of 45 threats from the threat catalogue may be relevant to this use case.
Also review: whether the system disadvantages groups
Check whether selection, ranking or assessment disadvantages people with protected characteristics, such as sex, age or origin.
Priority review points
Claimants control photographs and documents. An edited photograph or an invoice inconsistent with the image can inflate the estimate.
Staff approving many settlement proposals daily may start waving them through. Inconsistencies missed by the system then escape human review too.
The proposal passes directly into the claims system with amount and coverage assessment. Accepting an incorrect amount unchecked leads to overpayment or underpayment.
5 further review candidates in the typical design
Other relevant threats
Training data or an adopted base model can introduce hidden triggers for misbehaviour.
Listed because of this characteristic
- Uses another provider's model
Complex or high-volume inputs can exhaust model computing resources.
Listed because of this characteristic
- Processes text, images and speech together
Targeted queries can recover some personal or protected training data from the model.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Sensitive Information Disclosure
Confidential content can appear in answers, supplied context or logs.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Exfiltration from ML Application
Protected data can leak through tools, network connections, storage or insiders.
Listed because of this characteristic
- Processes confidential data
Up to 30 further threats may apply, depending on your system design. Nine threats need to be reviewed for every AI system. Two of these are particularly important here and are therefore listed above.
System features and open questions
Each Yes adds further threats to your checklist, up to 30 in total. If you cannot yet answer a question, include its threats in your review.
- Added by this Yes
- Also requires a Yes to another question
How your system is built
- Performs natural language tasks5 threats
- Calls other programs or interfaces2 threats
- Uses operational feedback2 threats
- Generates its own text1 threat, plus 13 only in combination with other characteristics
- Learns from your own data1 threat, plus 1 only in combination with other characteristics
- Searches its own document collection1 threat
- Formulates answers from retrieved documents1 threat
- Changes data or triggers processes1 threat
- Learns from examples with correct answers1 threat
- Optimises a specified metric1 threat
- Generates content from open-ended requests12 threats only in combination with other characteristics
How you operate the system
- Available to people outside the organisation4 threats, plus 2 only in combination with other characteristics
- Accepts queries directly through an interface3 threats, plus 1 only in combination with other characteristics
- Accessible over a network1 threat, plus 1 only in combination with other characteristics
- Incurs costs with each query1 threat
- Shares computing resources with other customers or applications1 threat
- Used alongside unapproved AI services1 threat
How your system is built
Does the system generate its own text, such as answers, drafts or summaries?
A language model summarises documents and drafts letters; image-only hail assessment returns values.
If yes, add
Does the system create new content from open-ended requests that people read, see or hear directly? Translation, reading aloud and transcription do not count.
Drafting emails to experts or claimants reaches people outside claims administration.
If yes, add
Can users give the system natural language tasks that it then performs?
A language model follows instructions, including ones in a submitted invoice or claim description.
If yes, add
Does the system search its own collection of documents or records for relevant content?
Depending on design, the system retrieves policy terms and agreements from a document collection to check entitlement.
If yes, add
Does the system formulate answers from retrieved documents?
If yes, add
Does the system call other programs, interfaces or search services itself, even if only to read?
If yes, add
Can the system change things itself, such as storing data, sending messages, initiating transactions or granting permissions?
The insurer determines whether it automatically approves small payments or only presents a proposal.
If yes, add
Does your organisation train or fine-tune the model itself using its own data?
Damage estimation models often learn from the insurer's own claims; language models are usually used unchanged.
If yes, add
Is the model trained on examples to which someone has assigned the correct answer?
Damage estimates learn from settled cases with known amounts; a language model does not require these.
If yes, add
Does operational feedback or output later feed into analysis or training?
Caseworker corrections can feed back as new training examples.
If yes, add
Is the system intended to maximise a specified metric, such as revenue, utilisation or clicks?
If yes, add
How you operate the system
These questions arise for almost every AI system.
Can people or programs query the model directly, for example through an interface?
If yes, add
Is the system accessible over a network or dependent on network services?
If yes, add
Do you pay per query or unit of computing time, so costs increase with use?
With a cloud language model, each analysed claim file costs money.
If yes, add
Does the system share servers or computing resources with other customers or applications?
If yes, add
Can people outside your organisation use the system, including without login?
If policyholders upload photographs and documents themselves through a portal, the system reaches people outside the insurer.
If yes, add
Do staff also use AI services for this task that your organisation has not approved?
If yes, add
Controls and implementation
Measures for the priority review areas are expanded. Each linked measure explains how to implement it.
Review priorityMultimodal Attacks2 measures
Review priorityOverreliance3 measures
Review priorityInsecure Output Handling3 measures
Backdoor ML Model2 measures
Model Denial of Service3 measures
Privacy Attacks3 measures
Sensitive Information Disclosure3 measures
Exfiltration from ML Application3 measures
Controls for the remaining threats are available on their pages in the threat catalogue.
AI Act provisions
-
Art. 4: AI literacy
For every AI system Applies since 2 February 2025.
-
Art. 53 para. 1 point b, Annex XII: Embedded general-purpose model
Depending on use Review trigger since 2 August 2025.
-
Art. 50 para. 2: Marking artificially generated content
Depending on use Review trigger since 2 August 2026.
Applies to every AI system
Art. 4: AI literacy
Applies if staff or appointed representatives operate or use AI systems.
Applies since 2 February 2025.
Obligations and details for Art. 4: AI literacy
- Who bears the obligations
- Provider, Deployer, Product manufacturer, Provider of a general-purpose AI model
Detailed obligations
May apply in particular uses
Art. 50 para. 2: Marking artificially generated content
Applies if the system generates letters to claimants or experts beyond assisting with existing texts.
Applies since 2 August 2026.
Obligations and details for Art. 50 para. 2: Marking artificially generated content
- The general condition is
- Applies if the system generates synthetic text, images, audio or video.
- Exception
- Exceptions include ordinary editing assistance that does not substantially alter inputs or their meaning.
- Points to consider
- Systems marketed before 2 August 2026 must be updated by 2 December 2026.
- Who bears the obligations
- Provider, Product manufacturer, Provider of a general-purpose AI model
Detailed obligations
Art. 53 para. 1 point b, Annex XII: Embedded general-purpose model
Applies if a large language model or another third-party general-purpose model analyses documents or drafts correspondence.
Applies since 2 August 2025.
Obligations and details for Art. 53 para. 1 point b, Annex XII: Embedded general-purpose model
- The general condition is
- Applies if another provider's general-purpose AI model is incorporated, such as a large language model.
- Points to consider
- The model provider bears its obligations. It must give you the Annex XII information; use it for your own documentation.
- Who bears the obligations
- Provider of a general-purpose AI model
Detailed obligations
Generally does not apply
Annex III no. 5 point c: Life and health insurance
Claims handling assesses damage that has occurred, rather than life or health insurance risk or pricing.
Obligations and details for Annex III no. 5 point c: Life and health insurance
- The general condition is
- Applies if the system assesses risk or sets prices for people's life or health insurance.
- Who bears the obligations
- Provider, Deployer, Product manufacturer
Detailed obligations
Classify your system with the quick checker All obligations in the obligations compass
Cross-cutting threats
Review these threats for every AI system, whatever its purpose.
Atypical system features
View 7 further questions
If any of these features does apply to your system, add the listed threats.
Does the system use a knowledge graph storing concepts and their relationships?
If yes, add
Does the system remember content from earlier conversations or tasks and reuse it later?
If yes, add
Does the system determine its next steps itself based on earlier results?
If yes, add
Does the system delegate tasks to other AI agents or incorporate their results?
If yes, add
Is the system connected to tools or data sources through the Model Context Protocol (MCP)?
If yes, add
Do documents from several customers, departments or permission groups share one search store?
If yes, add
Does the system generate code, scripts or database queries for further use?
If yes, add
Sources
- Report of a deployment
Allianz, AI at Allianz: faster correct payouts, 4 February 2025 According to the deployer
Allianz describes a large-language-model assistant comparing records and images with policies while specialists decide coverage and payment.
- Authority, court or parliament
EIOPA, Report on the digitalisation of the European insurance sector, 2024
In 2023, 50 per cent of surveyed non-life insurers and 24 per cent of surveyed life insurers used AI.
- Report of a deployment
procontra, Claims, incoming mail, customer service: how insurers use AI According to the deployer
Eight of ten surveyed insurers already use AI in claims handling: Allianz, Alte Leipziger, Axa, Ergo, R+V, VHV, VKB and Volkswohl Bund.
Related use cases
Also in the field of Banking and insurance
- Credit scoring and creditworthiness assessmentA model estimates repayment likelihood from payment and customer data, helping determine whether a person receives credit and on what terms.
- Payment fraud detectionA model assesses each payment for suspected fraud in real time and stops suspicious payments or refers them for review.
- Money laundering detection and sanctions screeningBanks search account and payment data for money laundering patterns and check all parties against sanctions lists.
- Life and health insurance risk assessment and pricingInsurers estimate an applicant's mortality or illness risk to determine whether to accept them and what premium to charge.
Often used together with
- AI agentAn AI agent breaks a task into steps and completes it largely independently using tools, interfaces and its own permissions in business applications.
- Customer service chatbots and voicebotsA bot independently handles customers' or citizens' requests in chat or by phone and passes difficult cases to people.
Updated: 30 September 2026. Review checklist rules version 0.3.2, threat catalogue version 2026.10.24.2. How these pages are structured