Agentic AI interface processing and adjudicating insurance claims

Claims Agent

Deploy an AI agent that independently manages insurance claims from intake to resolution. Claims Agent verifies user identity, assesses photo evidence against historical cases, determines eligibility, and books the nearest repair shop—all in a seamless chat interface. By integrating vision models, decision policies, and scheduling tools, it eliminates handoffs and dramatically reduces time to resolution.

Understanding & Automating Claims Management



Processing insurance claims demands speed, accuracy, and transparency. At Intellimark, our Claims Agent automates the entire process—from intake to resolution—reducing manual effort, accelerating decisions, and enhancing customer trust.

Automated Intake & Verification – Captures policyholder details, confirms identity, and validates claim submissions dynamically.

AI-Based Evidence Review – Analyzes images, documents, and contextual inputs to assess damage using precedent-trained models.

Smart Decision Adjudication – Determines claim eligibility by referencing fraud indicators, claim history, and underwriting rules.

Repair Partner Scheduling – Recommends vetted service providers and books appointments based on availability and proximity.

Complete Case Resolution – Tracks all touchpoints, manages escalations, and finalizes claims with full audit visibility.

Key Benefits of Claims Agent

99% accuracy improvement reported in AI-enabled claims systems.
99%
70% reduction in claims processing time through automation.
70%
30% reduction in operational costs tied to claims resolution.
30%

Impact


Claim Automation

Enables end-to-end claims handling with minimal human intervention—from report to repair.

Customer Experience

Delivers near-instant responses, clear status updates, and lower effort for the policyholder.

Operational Savings

Reduces manual work, cycle time, and fraud exposure, while improving claims throughput.

Metrics That Matter

Adjudication accuracy, time to resolve, claims throughput, auto-closure %, and escalation rate.

Execution Framework


Input Signals

Policy data, customer messages, uploaded photos, vehicle diagnostics, and prior claim records.

Agent Logic

Computer vision, eligibility scoring, fraud detection, LLM-powered Q&A, and repair provider matching.

Stakeholders

Claims managers, product owners, customer experience leads, AI governance, and legal.

Outputs

Case logs, claim decisions, booked appointments, audit trails, and performance dashboards.

Methodology


1. Define Claim Classes 2. Train on Historical Data 3. Integrate Document & Image Pipelines 4. Build Adjudication & Routing Logic 5. Pilot, Monitor & Refine Segment claims by type (e.g., auto collision, property damage, health) and determine which can be automated end-to-end. Use labeled historical claims—including documents, images, outcomes, and fraud flags— to fine-tune eligibility, payout, and escalation patterns. Set up secure intake for PDFs, receipts, and images (vehicle damage, medical bills), enabling NLP and vision models to extract structured data. Design the decision engine: verify coverage, assess damage vs. precedent, detect anomalies, and recommend actions like approval or referral. Run a pilot on a specific claim type (e.g. windshield repair), compare agent vs. human results, then retrain and scale to broader categories.