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AI-Powered Insurance Claims Engine
AI & Data10 MonthsEnterprise Insurance Group

AI-Powered Insurance Claims Engine

Development of an AI-enhanced claims processing platform that automates the intake, classification, and initial assessment of enterprise insurance claims.

Overview

The story
behind it

The processing of claims was a highly manual, error-prone effort involving thousands of physical and digital documents daily. Keyhole Software implemented an AI-driven pipeline that uses natural language processing (NLP) and a custom rules engine to automate 80% of routine claims, freeing adjusters to focus on complex cases.

80%
Automation Rate
3.5x
Claims Speed
95%
Accuracy Rate
35%
Cost Reduction
The Challenge

What stood in the way

The primary challenge was the variety and low quality of incoming documents. The system needed to extract data from hand-written forms, poor-quality faxes, and varied digital formats with extremely high accuracy to ensure fair settlements and prevent fraud.

Our Solution

How we made it happen

We developed a pipeline utilizing Python and Azure Machine Learning to extract and normalize data. This was coupled with a sophisticated Drools-based rules engine that analyzes claims against policy metadata to determine initial settlement recommendations.

Key Results

Impact that speaks

80%

Claims Automation

The majority of routine claims are now processed from intake to settlement without manual intervention.

5x

Resolution Velocity

Claim resolution time dropped from an average of 10 days down to less than 48 hours for automated cases.

45%

Operational Savings

Reduced the per-claim processing cost by nearly half through intelligent task automation.

Gallery

A closer look

AI-Powered Insurance Claims Engine gallery 1
AI-Powered Insurance Claims Engine gallery 2
Features

What we
delivered

Every feature was built with purpose, performance, and user experience at its core.

Intelligent Document Extraction
NLP-Based Claim Classification
Automated Rules Engine (Drools)
Fraud Detection Scoring Model
Claims Adjuster Collaboration UI
Policy Integration Middleware
Real-Time Processing Analytics
Secure HIPAA/PII Handling
Tech Stack

Tools & technologies

PythonAzure MLDroolsNode.jsReactDatabricksDocker
Timeline

From concept to launch

Phase 1

Data Science Discovery

Analyzing historical claims data to train and fine-tune extraction and classification models.

Phase 2

Pipeline Development

Building the scalable intake pipeline and integrating the AI/ML inference service.

Phase 3

Rules Engine Build

Translating complex policy documents into executable business rules.

Phase 4

Deployment & Feedback

Rolling out the system and implementing human-in-the-loop validation for continuous model improvement.

FAQ

Common questions

Ready to start?

Let's build something remarkable together

We'd love to discuss how we can help bring your vision to life. Our team has extensive experience across AI, web, mobile, and cloud technologies.

Start a conversation