How to Build a Claims Triage Agent (Playbook)

The first hours of a claim determine much of its cost and the customer's experience. Triage decides whether a claim goes to fast-track, a standard desk, a complex-loss specialist, or investigation, and how quickly. A claims triage agent makes that decision consistently, explainably, and fast, while adjusters keep coverage and payment decisions. This playbook covers the build, following FISTA's AI agents practice and the controls in the AI controls for financial services whitepaper. Context is in ai claims automation. This is general guidance, not legal advice.

What does the agent do?

Stage	Function	Method
Intake	FNOL from calls, web, app, email, brokers	Connectors; transcription
Extraction	Loss facts, parties, dates, damages, injuries, documents	Structured extraction with confidence
Policy check	Policy status, coverages, limits, deductibles	System lookup
Scoring	Severity, complexity, fraud indicators	Explainable models and rules
Routing	Handling path and adjuster assignment	Deterministic rules
Communication	Acknowledgement and next steps to claimant	Templates; approval where required
Audit	Every extraction, score, and routing decision	Immutable log

Step 1: Specify with claims operations

Define lines and claim types in scope, handling paths and their criteria, severity and complexity definitions, fraud referral thresholds, routing rules including adjuster skills and workload, communication rules, prohibited behavior (no coverage determinations, no denials, no payments), and quality thresholds. See how to write an ai spec.

Step 2: Build intake and extraction

Consolidate channels; transcribe calls with consent; extract structured facts from narratives, forms, photos, and documents with confidence and source positions; validate against policy data and consistency rules; route low-confidence fields to intake staff. Patterns are in how to build an ai data extraction pipeline and how to build a speech-to-text pipeline.

Step 3: Score severity, complexity, and fraud indicators

Severity models estimate expected loss range from facts and history; complexity models estimate handling effort from parties, coverage questions, and injury involvement; fraud indicators combine rules and models on inconsistencies, history, and network links. All scores carry explanations. Models are validated under model risk management and tested for fairness. Modeling practice is in how to build a predictive model and how to build a fraud detection system.

Step 4: Route with deterministic rules

Apply routing rules in code: path by severity and complexity bands, investigation referral by indicator thresholds, regulatory and litigation triggers, adjuster assignment by skills, license, jurisdiction, and workload, and escalation for catastrophes or high-value losses. Claims operations own the rules; changes are versioned and tested. See rules engine vs llm.

Step 5: Communicate with claimants appropriately

Send acknowledgements with claim number, next steps, and adjuster contact from approved templates; collect missing information through guided requests. No coverage statements or settlement figures. Templates and triggers are reviewed by compliance. See ai transparency notices.

Step 6: Preserve adjuster decision rights

Adjusters receive routed claims with extracted facts, scores, explanations, and source documents, and they make coverage and payment decisions in their systems. Fast-track paths automate steps only under explicit rules with sampling; anything above thresholds requires adjuster action. Design is in what is a human approval gate.

Step 7: Evaluate

Build a labeled set of historical claims with adjuster-validated facts, correct paths, and outcomes. Measure extraction accuracy, scoring calibration and explanation quality, routing accuracy, investigation referral precision and recall, and fairness across groups. Wire the suite into CI. Method is in the AI evaluation and testing whitepaper.

Step 8: Launch and monitor

Run in shadow mode against current triage, review disagreements with claims leadership, then go live with a human triage queue for low confidence. Monitor reassignment rates, cycle times, fast-track leakage, referral outcomes, complaints, and fairness metrics. Observability design is in the AI observability whitepaper.

Worked example: auto physical damage

A personal-lines carrier deploys the agent on auto physical damage claims. FNOL arrives by app and phone; transcription and extraction capture loss facts, vehicle, drivability, injuries, and photos. Policy lookup confirms coverage status. Severity scoring uses damage descriptions, photos, and vehicle data; complexity flags multi-vehicle losses and injuries; fraud indicators check history and inconsistencies between narrative and photos. Rules route drivable single-vehicle losses under a threshold to fast-track with a repair network offer, injury claims to casualty adjusters, and indicator-flagged claims to investigation. Adjusters make all coverage and payment decisions. Shadow mode reveals the model under-scores severity for a vehicle class, corrected before go-live, and post-launch metrics show time to assignment and reassignment rates improving with fairness review finding no disparities across groups.

What governance applies?

Model risk management, fairness testing, regulatory and litigation routing rules, complaint handling, audit trails, access control over claimant data, and change control. See ai model risk management and ai in insurance.

What does it cost to run?

Cost scales with claim volume and document and call processing; value is measured in cycle time, reassignment reduction, leakage reduction, investigation precision, and customer satisfaction. Drivers are in the AI total cost of ownership whitepaper.

What are the common mistakes?

• Letting scores route directly instead of through owned rules.
• Fraud indicators that deny rather than refer.
• Extraction from narratives without validation against policy data.
• Fast-track paths without leakage monitoring.
• No fairness testing until a regulator asks.
• Claimant communications that imply coverage.

What team does the agent need?

Claims operations leaders own the handling paths and routing rules, experienced adjusters label historical claims and review shadow-mode disagreements, compliance owns fairness and regulatory routing, and engineering delivers extraction, scoring, and integration. The adjusters' involvement in labeling is what makes the routing accuracy measurement credible.

How FISTA Solutions builds claims triage agents

FISTA Solutions builds claims triage agents to this playbook: claims-operations-owned specifications and rules, confidence-scored extraction from every channel, explainable severity, complexity, and fraud scoring under model risk governance, deterministic routing, preserved adjuster decision rights, compliant communications, and shadow-mode launch with fairness monitoring. The AI agents practice delivers the agent, AI enablement the document, speech, and integration platform, and forward deployed engineers embed with your claims team. The record behind the work is 150+ projects with 99.9% uptime.

This playbook is general guidance, not legal advice. To scope a claims triage agent, message FISTA on WhatsApp, or read how to build an ai underwriting assistant for the front-end counterpart.