AI-assisted claims intake and triage: an illustrative scenario

The situation

An insurer receives claims by phone, web form, email and broker submissions. Documents arrive as photos, PDFs and free-text descriptions, and key details are often missing. Adjusters spend time re-keying information and chasing gaps, and simple claims wait in the same queue as complex ones.

Leaders want faster, more consistent handling without weakening fraud controls or fair treatment of customers.

What the organization wants to achieve

• Capture claim details once, in a consistent structure.
• Flag missing information early, while the customer is still engaged.
• Route claims by complexity so adjusters focus where judgment is needed.
• Keep a clear record of how each claim was handled.

Approach

ARCHR would typically run this through its four phases: Discovery and Strategy, Pilot and Validation, Build and Scale, then Optimize and Evolve. In this scenario that could look like:

• Discovery: map the claims journey for one line of business, review how claims are triaged today and agree what adjusters must always decide themselves.
• Pilot: extract key fields from documents and descriptions using document intelligence and language models, suggest a routing for each claim, and compare suggestions with adjuster choices without changing live handling.
• Pilot: test for errors and uneven results across claim types and customer groups, as part of fair-treatment testing.
• Build: connect to the claims system through APIs and change data capture, keep master records for policies and parties, and add a review screen showing the source of every extracted field.
• Build: route low-confidence cases to people by design, and keep an evaluation set so each release is tested before it reaches adjusters.
• Optimize: monitor accuracy and routing quality, retrain on adjuster feedback and retire rules that no longer help.

Technology and governance considerations

Document and language services on AWS, Microsoft Azure or Google Cloud, shaped around the insurer's existing claims platform, could handle extraction and classification. Suggestions would carry the reasons behind them so adjusters can accept, correct or reject them, and corrections would feed back into evaluation.

Claims files hold personal and sometimes health information, so access, retention and cross-border handling would be set under the privacy laws that apply, such as Canada's Personal Information Protection and Electronic Documents Act (PIPEDA), the European Union's General Data Protection Regulation (GDPR) or state laws. Insurers also work within expectations from insurance and prudential regulators (for example the Office of the Superintendent of Financial Institutions (OSFI) and provincial regulators in Canada, or state departments in the United States) on model risk, conduct and fair treatment, so the model would be inventoried, documented and reviewed. This design is intended to support alignment with those expectations, not to assure compliance.

What success could look like

A pilot would agree measures and a baseline before any work starts. Qualitative signals the team could track include:

• Adjusters spend less time re-keying and chasing missing details.
• Straightforward claims move through the process more smoothly.
• Extracted fields and routing suggestions match adjuster judgment closely enough to trust.
• Reviewers can explain any handling decision from the record.

Actual results depend on an organization's data, systems and objectives. This page is not legal, financial or investment advice.