Insurance Claims Automation: $500K Annual Savings, 85% Touchless Claims

The Challenge

A regional auto insurance carrier with 50,000 policies and $100M in annual premiums was drowning in manual claims work. Their claims team of 35 spent most of the day on repetitive tasks: rekeying information from claim forms, manually reviewing photos, and flagging suspicious patterns by eye. Settlement averaged 14 days. Customer satisfaction hovered at 3.2/5. And fraud—estimated at 5–10% of claims—was costing millions annually.

The business problem was clear: manual processes couldn't scale. Every new policy added more claim volume without proportional staff. Competitors were offering same-day or next-day payouts. The carrier needed to automate the routine, accelerate the flow, and catch fraud earlier—without sacrificing accuracy or compliance.

• •Claims adjusters spending 60%+ of time on data entry and document review
• •Average settlement cycle of 14 days vs. industry benchmark of 5–7 days
• •5–10% fraud rate with no systematic detection beyond manual red flags
• •Customer satisfaction at 3.2/5 driven by slow payouts and lack of visibility

They chose LTK Soft for our track record in insurance technology, AI/ML expertise, and proven integration with policy admin systems. We had built similar claims automation for other carriers and understood the compliance and audit requirements from day one.

Our Solution

We designed an end-to-end claims automation platform that handles document ingestion, damage assessment, and fraud scoring—automating the majority of claims while routing complex cases to adjusters with full context. The approach was phased: prove the pipeline, tune the models, then scale.

Technologies implemented: Python, TensorFlow, AWS Rekognition, .NET Core, PostgreSQL, React.

Timeline: 16 weeks from kickoff to production, with a 2-week pilot on 500 claims before full rollout.

Implementation Highlights

• Custom damage assessment model trained on the carrier's historical data—outperformed generic solutions by 12% on validation set.
• Explainable fraud scores so adjusters could understand why a claim was flagged and act quickly.
• Graceful fallback: when confidence was low, claims routed to humans with pre-populated data, cutting review time by 70%.
• Integrated with legacy policy admin via APIs—no rip-and-replace. Deployed on AWS with SOC 2 controls.

Results & Impact

Business impact: $500K annual savings from reduced claims staff time and faster processing. Fraud losses cut significantly. Customer retention improved. The carrier now competes on speed without sacrificing accuracy.

"The system paid for itself in under a year. Our adjusters focus on the complex cases that need human judgment—everything else runs automatically." — VP of Claims Operations (client name confidential)

What They Got

• Automated document ingestion and OCR pipeline for claim forms, police reports, and photos
• Computer vision damage assessment model with repair cost estimation
• ML fraud detection engine with explainable scores and audit trail
• Unified claims workflow with routing rules and approval automation
• Customer-facing claim status portal with real-time updates
• Admin dashboards for metrics, exception handling, and compliance reporting
• Full integration with policy admin, document store, and payment systems

Technologies Used

Full tech stack: Python (TensorFlow, scikit-learn), AWS Rekognition (supplemental CV), .NET Core (API layer), PostgreSQL (claims data), React (portals, dashboards), AWS (EC2, RDS, S3).

Python and TensorFlow were chosen for ML flexibility and model iteration speed. AWS Rekognition provided baseline image analysis while our custom model handled domain-specific damage assessment. .NET Core integrated with the carrier's existing ecosystem. PostgreSQL offered reliability and auditability for claims data.

Lessons Learned

• Domain data matters. A model trained on the carrier's own historical claims outperformed off-the-shelf solutions by a meaningful margin.
• Automate the easy path first. Focusing on routine, low-complexity claims gave immediate ROI and built trust before tackling edge cases.
• Explainability builds adoption. Adjusters trusted the fraud model because they could see why claims were flagged—transparency was critical for workflow adoption.