AI Agent

Claims Adjudication AI Agent

Adjudicate claims in 2 hours instead of 2 days

Manual claims decisions frustrate customers with 48-hour delays. AI adjudicates 80% of claims in hours with consistent outcomes.

Benefits

Pay Claims Faster Without Increasing Leakage

Manual claims adjudication takes 2+ days per claim causing customer dissatisfaction. Inconsistent decisions create leakage and complaints. Adjuster capacity limits growth. This agent adjudicates 80% of claims in hours with consistent, accurate decisions.

80%

Same-day claim decisions

90%

Faster adjudication

100%

Decision consistency

Agentic Flows

Complete Claims Adjudication Workflow

Automated workflow: claim data extraction and validation, coverage verification against policy terms, liability assessment with comparative analysis, damage valuation using market data, fraud detection scoring, settlement determination with reserve calculation, payment processing or exception routing.

How it works

AI-Powered Claims Decision Making

The agent reviews claim details against policy coverage and exclusions automatically, assesses liability and damage valuation using historical data and ML models, applies settlement logic with reserve recommendations consistently, detects fraud indicators and routes suspicious claims for investigation.

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Integrations

Works with Claims Management Systems

Direct integration with Guidewire ClaimCenter, Duck Creek Claims, Snapsheet, and Mitchell for claims data. Connects to policy admin for coverage verification. Integrates with payment systems for settlement processing.

Implementation

Adjudicating Claims in 3 Weeks

Connect claims and policy systems. Configure adjudication rules and train valuation models. Most carriers achieve 80% same-day adjudication within 3 weeks of deployment.

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Key Features

Claims Adjudication Features

Coverage Analysis

Analyzes policy terms and applies to claim facts automatically. Identifies coverage issues early.

Valuation Models

ML models estimate repair costs and loss values. Compares to market data for accuracy always.

Fraud Detection

Scores claims for fraud indicators. Routes high-risk claims to SIU with evidence and recommendations.

Demo

See Claims Adjudication in Action

Watch the agent adjudicate 100 auto claims: verify coverage, assess liability, value damages, detect 5 fraud flags, approve 80 payments, route 15 for review—in 2 hours.