How AI Automated Insurance Claims With 99% Accuracy

THE PROBLEM

Manual Underwriting and Claims Processes Slowed Decisions and Increased Costs

For this leading insurer, two areas created the biggest bottlenecks: 

• broker-underwriter communications
• split liability claims analysis 

Each relied heavily on human judgment and repetitive information handling, causing inefficiencies that rippled across the business.

Problem #1: Broker-Underwriter Interaction

Manual broker-underwriter communication slowed the insurer’s underwriting process. Each policy or coverage query required multiple emails and follow-ups, causing delays and inconsistent decisions.

The lack of automation increased costs, created bottlenecks, and frustrated brokers waiting for slow, repetitive responses.

Problem #2: Split Liability Analysis in Motor Insurance Claims

On the claims side, split liability cases presented another challenge. Adjudicators manually reviewed diverse evidence types—documents, images, videos, and transcripts—often stored in separate systems. 

This manual evidence review caused processing delays, inconsistent determinations, and higher operational costs.

Both teams faced a growing burden as case complexity increased and experienced staff retired, risking the loss of institutional knowledge that guided decision quality. Without automation, growing backlogs and compliance risks exposed the need for faster, more reliable evidence analysis.

THE SOLUTION 

Agentic AI Automates Broker-Underwriting and Auto Claims

The insurer partnered with us to implement an agentic AI solution that addressed two of their high-impact areas. The goal was to eliminate manual effort, accelerate decision-making, and ensure consistent, auditable outcomes across underwriting and claims operations.

Solution #1: Broker-Underwriter Interaction Agent

To eliminate manual back-and-forth communication between brokers and underwriters, the insurer deployed AgentFlow, a context-aware agentic AI platform purpose-built for finance and insurance workflows.

AgentFlow uses Conversational AI to let brokers ask questions in natural language about policies, coverage, exceptions, and more, delivering instant, accurate responses powered by contextual data from past communications and documents processed by Unstructured AI.

The platform’s confidence scoring routes complex queries to human underwriters, while all actions are logged for transparency. Through a hands-on partnership model, engineers and underwriters refined the system to capture expert knowledge and improve accuracy.

Solution #2: End-To-End Claims Analysis and Decision Platform

AgentFlow ingested and analyzed diverse evidence types like PDFs, images, audio transcripts, and video stills, extracting key data points automatically. It applied company rules, prior decisions, and regulatory standards to ensure consistent, auditable claim outcomes. 

Adjudicators can use the built-in chat to request clarifications or perform deeper reviews. AgentFlow generates a comprehensive report summarizing every decision step.

Document AI extracts and structures key information from diverse evidence sources, while Decision AI applies company rules, prior adjudications, and regulatory guidelines to deliver consistent, explainable outcomes.

Deployed securely within the insurer’s private infrastructure, the system maintains full control of data and models. Through a hands-on partnership model, engineers and claims specialists refined the decision logic and workflows. 

THE RESULTS

Measurable Impact on Efficiency, Accuracy, and Speed

The three-month pilot demonstrated AgentFlow’s ability to deliver measurable efficiency and accuracy gains across underwriting and claims operations. Conducted as a small-scale, controlled implementation on the customer’s VPC, the pilot tested the solution’s feasibility, performance, and value before committing to a full deployment.

For underwriting, the Broker-Underwriter Interaction Agent successfully automated over 90% of broker queries, delivering real-time responses with 99% accuracy that drastically reduced turnaround times and eliminated repetitive communication loops. This improvement translated into faster deal cycles and higher broker satisfaction.

In claims management, the AI solution achieved 99% accuracy in document and evidence analysis, processing photos, handwritten statements, videos, and transcripts with near-perfect precision.

AgentFlow’s API-first architecture provided full transparency and auditability through confidence scores, traceable logs, and explainable outputs, automatically routing decisions with confidence scores below 95 to human review.

By the end of the  pilot, the insurer achieved complete workflow automation, greater decision consistency, and measurable cost savings. 

About the Customer

A major insurer specializing in commercial and motor coverage aimed to improve operational efficiency and decision accuracy.

With AgentFlow adoption, the customer automated key workflows, standardized decisions, and safeguarded institutional knowledge through secure, compliant automation.