Overview
A leading US health insurance provider with over 5 million members was struggling with their claims processing system. Manual review processes were creating bottlenecks, leading to delayed reimbursements, increased operational costs, and declining customer satisfaction scores.
The insurer approached Koyal Tech Solutions to design and implement an AI-powered claims processing system that could automate routine claims while intelligently flagging complex cases for human review.
The Challenge
The client faced several interconnected challenges that were impacting their business:
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High Processing Volume
Over 50,000 claims processed daily with 40% requiring manual intervention, creating a persistent backlog of 10,000+ claims.
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Error-Prone Manual Reviews
Human reviewers made errors in approximately 8% of processed claims, leading to costly corrections and member complaints.
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Inconsistent Decision Making
Different reviewers applied varying interpretations of policies, resulting in inconsistent claim outcomes and audit findings.
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Scaling Limitations
Hiring and training new claims processors was expensive and time-consuming, limiting the ability to handle volume spikes during open enrollment periods.
Our Solution
We designed a comprehensive AI-powered claims processing platform that combined multiple technologies to address each challenge:
Intelligent Document Processing
We implemented advanced OCR and NLP models to automatically extract information from claim forms, medical records, and supporting documents. The system could handle handwritten notes, faxed documents, and various electronic formats with 98.5% extraction accuracy.
ML-Based Claims Adjudication
A custom machine learning model was trained on 3 years of historical claims data to automatically adjudicate routine claims. The model learned to identify covered services, verify eligibility, calculate benefits, and detect potential fraud patterns.
Smart Routing Engine
Claims that required human judgment were intelligently routed to specialized reviewers based on claim type, complexity, and reviewer expertise. This reduced average handling time by ensuring the right person reviewed each claim the first time.
Real-Time Analytics Dashboard
A comprehensive dashboard provided managers with real-time visibility into processing metrics, bottlenecks, and individual reviewer performance, enabling data-driven workforce optimization.
Implementation Approach
We followed our proven four-phase implementation methodology:
Discovery & Data Analysis (6 weeks)
Deep dive into existing processes, analysis of 500,000+ historical claims, and identification of automation opportunities. Defined success metrics and ROI targets.
Model Development & Training (10 weeks)
Built and trained ML models using historical data, with continuous refinement based on feedback from claims experts. Achieved 95% accuracy in test environment.
Pilot Deployment (8 weeks)
Deployed the system for a subset of claim types, running in parallel with existing processes. Fine-tuned models and routing rules based on real-world performance.
Full Rollout & Optimization (8 weeks)
Expanded to all claim types, decommissioned legacy systems, and established ongoing monitoring and improvement processes.
Results & Impact
The AI-powered claims processing system delivered transformative results across all key metrics:
70% Faster Processing
Average claim processing time reduced from 4.2 days to 1.3 days, with 60% of claims now processed in under 4 hours.
99.2% Accuracy
Error rate dropped from 8% to 0.8%, virtually eliminating costly rework and member complaints related to processing errors.
$2.4M Annual Savings
Reduced operational costs through automation, decreased rework, and optimized staffing levels during peak periods.
32% NPS Improvement
Member satisfaction scores increased significantly due to faster reimbursements and more consistent claim decisions.
"Koyal Tech Solutions didn't just deliver technology—they transformed how we think about claims processing. The AI system has become a competitive advantage, allowing us to process claims faster and more accurately than anyone in our market."
