Insurance Provider: Automated Claims Processing

Client Context

A regional property and casualty insurance provider serving 85,000 policyholders across auto, home, and commercial lines processed approximately 3,200 claims monthly. Their claims operation employed 28 adjusters and 12 support staff, using a legacy claims management system implemented in 2012.

The claims process was heavily manual: adjusters reviewed documentation, verified coverage, assessed damages from photos and estimates, coordinated with third parties (repair shops, medical providers, attorneys), and ultimately approved payouts. Despite digital document submission capabilities, most processing steps required human review and manual data entry across multiple systems.

The Problem

Claims processing was unacceptably slow, expensive, and inconsistent. Average time from claim submission to payment was 18-21 days for straightforward claims that should have taken 3-5 days. Customer satisfaction with claims experience was the lowest-rated aspect of the company's service, scoring 68% versus 89% for sales and policy management.

Specific operational challenges included:

• Every claim required adjuster review regardless of simplicity or claim amount, creating bottlenecks for routine low-value claims
• Document processing was manual: extracting data from repair estimates, medical bills, police reports, photos—adjusters spent 40% of their time on data entry
• Inconsistent decision-making: similar claims received different outcomes depending on which adjuster handled them, creating fairness concerns and regulatory risk
• Fraud detection was reactive and informal—no systematic analysis of claims patterns or red flags
• Customer communication was manual: phone tag, email exchanges, lack of self-service visibility into claim status
• Peak periods (severe weather events, holiday weekends) created massive backlogs, extending processing times to 30+ days

Financial analysis revealed that claims processing costs were 22% above industry benchmarks. Average cost per claim was $485 versus industry average of $375. The lengthy processing times were driving policyholder attrition: 15% of claimants switched insurers due to claims experience frustration. The Chief Operating Officer estimated $3.2M in annual excess costs plus meaningful revenue loss from customer churn.

Why Existing Tools Failed

The company's claims management system could track workflow and store documents but provided no intelligence or automation. Every decision required human judgment, even for routine scenarios that followed clear patterns.

They had explored modern claims platforms from Guidewire and Duck Creek, but these required complete system replacement, 12-18 month implementations, multi-million dollar investments, and substantial organizational change management. The platforms also came with rigid workflows that didn't match the company's specific underwriting rules and claims philosophy.

Simple rules-based automation (auto-approving claims under $500) was too rigid and created fraud risk. OCR tools could extract text from documents but couldn't understand context or make decisions. What the company needed was intelligent automation that could understand claim documents regardless of format, apply their specific underwriting and claims handling rules, detect fraud patterns, make approval decisions for straightforward claims, and route complex cases to appropriate adjusters with context and recommendations.

Discovery & Engagement with Autana

The COO learned about Autana through an insurance industry working group on operational efficiency. Initial consultation included comprehensive analysis of claims data, adjuster workflows, approval patterns, and regulatory requirements.

Autana's discovery process analyzed 18 months of claims history: 57,600 completed claims across all lines. They identified that 62% of claims were "routine"—straightforward scenarios with clear liability, documented damages, and claim amounts within predictable ranges. These routine claims shared specific characteristics: single-party incidents, no injury, prompt reporting, consistent documentation, and amounts under policy thresholds.

The team documented approval criteria for each claim type, fraud indicators to watch for, and escalation rules for exceptions. They also mapped the complete claims workflow from first notice of loss through investigation, evaluation, negotiation, approval, and payment, identifying automation opportunities at each stage.

The Autana Solution

Autana built an intelligent claims processing system with these capabilities:

• Intelligent Document Processing: AI extracts and understands data from any document type—repair estimates, medical bills, police reports, photos—regardless of format or source, with 99.2% accuracy
• Automated Damage Assessment: Computer vision analyzes photos of vehicle or property damage, estimates repair costs based on historical data, and flags discrepancies with submitted estimates
• Intelligent Claims Triage: AI instantly categorizes claims as routine, complex, or suspicious based on dozens of factors, routing each to appropriate workflow
• Fraud Detection: Machine learning identifies suspicious patterns—duplicate claims, inconsistent stories, unusual claimant behavior, inflated damages—flagging for investigation
• Automated Decision Engine: For routine claims, AI applies underwriting rules and claims handling policies to make approval decisions, calculate payouts, and initiate payments—no human review needed
• Intelligent Adjuster Assistance: For complex claims, AI provides recommendations, highlights relevant policy provisions, and surfaces similar historical claims to guide decision-making
• Proactive Customer Communication: Automated status updates, document request management, and estimated timeline notifications, with self-service portal for claim tracking
• Continuous Learning: System learns from adjuster decisions and outcomes to improve accuracy and expand autonomous processing capabilities

The system was trained on the company's historical claims data, learning their specific interpretation of policy language, risk tolerance, and settlement approaches.

Implementation Timeline

Results & Impact

Additional Business Impact: Customer satisfaction with claims experience improved from 68% to 91%, becoming a competitive differentiator. The Net Promoter Score for claimants increased 32 points. Policyholder retention improved measurably, reducing post-claim attrition from 15% to 6%.

Adjuster productivity increased 240%—the same team now handles 3× the claim volume while spending more time on high-value, complex cases requiring expertise. Fraud detection improved dramatically: the AI system flagged 340% more suspicious claims for investigation, with 78% confirmed as fraudulent upon review, preventing an estimated $4.8M in improper payments.

Decision consistency improved substantially. Automated processing eliminates adjuster bias and applies rules uniformly, reducing regulatory risk and improving fairness. The company's combined ratio (claims costs plus operating expenses as percentage of premiums) improved by 4.2 points, translating to millions in underwriting profit improvement.

Total annual impact exceeded $2.9M in direct cost savings plus meaningful revenue retention from improved customer experience. During a severe weather event that generated 800+ claims in 72 hours, the system handled the surge without creating backlogs, maintaining service levels that would have been impossible with manual processing.

Final Takeaway

This implementation showcases how AI can transform heavily regulated, judgment-intensive processes where many assume human review is always required. The insurance industry has long accepted slow claims processing as inevitable due to complexity and regulatory requirements. This case proves that intelligent automation can actually improve quality and compliance while dramatically accelerating throughput.

Autana's solution succeeded because it was purpose-built for this insurer's specific reality: understanding their policy language, applying their unique underwriting philosophy, integrating with legacy systems, and meeting regulatory requirements. The AI doesn't replace adjuster expertise—it handles routine scenarios that don't require judgment, enabling adjusters to focus on cases where human expertise creates value.

The ongoing partnership with Autana continues driving value as the system evolves: expanding autonomous processing to more claim types, refining fraud detection models based on investigation outcomes, incorporating new data sources and decision factors, and providing strategic analytics to inform underwriting and risk management. This is intelligent infrastructure that creates compounding operational and competitive advantage.