How Can AI Help Triage Delegated Claims for Human Review?

Delegated claims can develop quickly. A routine notification can become a large loss, a coverage dispute or a sensitive customer case as new information arrives.

Managing agents and delegated claims administrators therefore need a reliable way to identify which claims can remain within the delegated workflow and which require urgent or specialist attention.

The challenge is that material information may sit across financial fields, status changes, free-text narratives and separate correspondence. Triage must interpret that combined picture without delaying the prompt and fair handling of the claim.

Why delegated claims are difficult to prioritise consistently

A claims bordereau provides a periodic view of the portfolio, but a single row rarely tells the whole story. Reserve movements, litigation, coverage concerns, fraud indicators, complaints and claimant circumstances may emerge at different times and in different formats.

Authority also varies by contract. Monetary limits, classes of business, territories, claim types and specific circumstances can all trigger referral. The same reserve movement may be routine under one delegated claims agreement and require immediate attention under another.

Claims teams must combine these rules with professional judgement. Lloyd's expects delegated claims handling to be delivered consistently and effectively, while FCA rules require applicable claims to be handled promptly and fairly. A triage process that merely clears queues faster is incomplete if it overlooks customer harm or material claim development.

Existing referral and claims oversight controls

Traditional triage relies on contract referral rules, claims-handler checklists, diary alerts and review queues. Experienced handlers identify cases outside authority or requiring expertise, and managing-agent teams review bordereaux, referrals, audits and performance information.

These controls remain essential. Deterministic rules are effective for clear conditions such as a financial threshold or an excluded class. Handler judgement is vital where facts are incomplete or a narrative changes the significance of a structured field.

The weakness is consistency at volume. A material phrase can be missed in a long narrative, and a significant pattern may only become visible after several updates. Periodic bordereaux can also create a gap between a claim changing and an oversight team seeing it.

How AI can support claims triage

AI can read structured and narrative information together and prepare a routing suggestion. A controlled workflow might:

• Apply fixed authority and referral rules first.
• Summarise new information since the last review.
• Highlight reserve movements, repeated reopenings or changing claim status.
• Identify language associated with litigation, vulnerability, complaints or coverage uncertainty.
• Route low-confidence and conflicting cases to a conservative review queue.

This helps claims professionals focus attention where it is most needed. It can also make the reason for referral clearer by presenting the relevant source information together.

AI should not accept or deny a claim, set a reserve or agree settlement through the triage step. Those activities require appropriate authority, full evidence and accountable claims judgement.

Safeguards for fair and accountable triage

Authority rules should operate as mandatory controls. An AI score or classification must never override a contractual referral threshold.

Testing should examine false negatives as closely as false positives. A process that creates extra referrals may be inefficient, but a process that misses a vulnerable customer, authority breach or rapidly developing loss can cause much greater harm.

Teams should test the workflow retrospectively against varied claims and then monitor live outcomes. Useful measures include missed referrals, unnecessary referrals, time to appropriate review, overturned suggestions, complaints and evidence of customer harm.

Every routing suggestion should retain the source fields, narrative passages, model or rule version and reviewer action. Claims leaders can then understand why a case was prioritised and improve the workflow without losing accountability.

Example

A hypothetical delegated claims administrator submits a monthly claims bordereau for a household binder. One claim remains below the contractual financial referral threshold, but its reserve has increased twice and the latest narrative mentions disputed coverage, temporary accommodation and a potentially vulnerable claimant.

A fixed rule does not force a monetary referral. The AI-supported triage process brings the changes together, flags the customer and coverage indicators, and sends the claim to a senior review queue with links to the source entries.

A claims professional reviews the file promptly, contacts the DCA for the missing information and decides the appropriate handling and oversight response.

FAQs

• Should AI set claim reserves during triage?

  No. Triage can flag a reserve movement or suggest that specialist review is needed. Setting or changing a reserve is a separate controlled activity requiring suitable data, authority and claims judgement.

• What information can be used to triage delegated claims?

  Relevant inputs may include claim status, paid and reserved amounts, dates, cause and location of loss, authority limits, narrative updates, complaints and referral history.

• How should claims teams test an AI triage process?

  Test it retrospectively on representative and difficult cases, review missed and unnecessary referrals, validate source traceability and monitor customer and claims outcomes after controlled introduction.