AI Agent

Underwriting Support AI Agent

Make instant underwriting decisions with 70% straight-through processing

Manual underwriting creates multi-day delays. AI underwrites 70% of submissions instantly with consistent risk assessment automatically.

Benefits

Underwrite More Business Without Adding Staff

Manual underwriting can't keep pace with submission volume. Inconsistent risk assessment causes loss ratio problems. Multi-day turnaround times lose business to competitors. This agent underwrites 70% of submissions instantly with consistent decisions.

70%

Straight-through underwriting

24 hrs

Average decision time

100%

Decision consistency

Agentic Flows

End-to-End Underwriting Automation

Automated workflow: submission data extraction and validation, risk scoring with ML models, guidelines application with automatic decisions, pricing calculation with competitive analysis, exception identification and routing, document generation and policy issuance.

How it works

AI-Powered Risk Assessment

The agent analyzes submission data against underwriting guidelines automatically, scores risk using ML models trained on historical loss data, applies pricing and coverage recommendations with consistent logic, routes exceptions to underwriters with risk analysis and recommendation.

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 Insurance Underwriting Systems

Direct integration with Guidewire PolicyCenter, Duck Creek, Vertafore, and Applied Epic for submissions. Connects to third-party data providers for MVR, credit, claims history. Integrates with rating and policy admin systems.

Implementation

Underwriting Faster in 4 Weeks

Integrate with submission and policy systems. Configure underwriting rules and train ML models on historical data. Most carriers achieve 70% straight-through processing within 4 weeks.

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

Underwriting Automation Features

ML Risk Scoring

ML models predict loss probability from submission data. Scores improve as more policies are written.

Consistent Decisions

Applies guidelines uniformly across all submissions. Eliminates underwriter bias and inconsistency.

Smart Exceptions

Routes only complex cases to underwriters with risk analysis. Provides recommended decision and reasoning.

Demo

See Underwriting Automation in Action

Watch the agent underwrite 200 auto insurance submissions: approve 140 automatically, decline 20 with reasons, route 40 exceptions to underwriters—all in 30 minutes.