Case Study: Automated Claims Processing & Fraud Detection

Insurance

Automated Claims Processing & Fraud Detection

The Challenge: Manual Processes Driving Costs and Risk

A large insurance company was grappling with significant inefficiencies in its claims handling department. The process relied heavily on manual assessment of submitted documents (claim forms, invoices, photos, reports), leading to long processing times from first notice of loss (FNOL) to settlement. This not only resulted in high operational costs due to the intensive labor involved but also negatively impacted customer satisfaction due to delays. Furthermore, the manual fraud detection methods, often based on simple rule checks and adjuster intuition, struggled to keep pace with increasingly sophisticated fraudulent activities, leading to substantial financial losses from unwarranted payouts. Adjusters were overburdened with repetitive tasks, limiting their ability to focus on complex claims requiring nuanced judgment.

Our Solution: AI-Powered Claims Intelligence on AWS

Lydatum engineered an end-to-end automated claims processing and fraud detection system built on AWS, leveraging powerful AI and data analytics services. The solution aimed to accelerate processing, improve accuracy, and proactively identify suspicious claims:

• Intelligent Document Processing with Amazon Bedrock: We utilized foundation models available through Amazon Bedrock (e.g., Anthropic Claude for text extraction/summarization, potentially others for image analysis) to automatically ingest and analyze various claims documents. The AI extracted key information, classified document types, summarized narratives, and validated data consistency across submitted materials. This automated the initial data capture and verification steps.
• AI-Driven Fraud Detection: The system incorporated multiple layers of fraud detection. Rules-based engines flagged obvious inconsistencies, while machine learning models (potentially trained using Amazon SageMaker, although Bedrock was the focus for document analysis) identified subtle patterns indicative of fraud based on historical data. Bedrock's models also helped analyze unstructured text for sentiment or language patterns associated with fraudulent claims. High-risk claims were automatically flagged for review by specialized fraud investigation teams.
• Claims Trend Analysis with Amazon Redshift: All processed claims data, including extracted information and fraud flags, was loaded into Amazon Redshift, a cloud data warehouse. This enabled powerful analytics and reporting. Lydatum developed dashboards for monitoring claims processing times, identifying bottlenecks, tracking fraud detection rates, and analyzing emerging fraud trends across different lines of business or geographical regions.
• Workflow Integration: The AI-powered components were integrated into the insurer's existing claims management system, providing adjusters with automatically populated data fields, fraud alerts, and analytical insights directly within their familiar workflow.

The Impact: Faster Claims, Reduced Fraud, and Empowered Adjusters

The implementation of the automated system delivered substantial improvements across the claims value chain:

Automating document analysis and initial assessment significantly reduced the end-to-end processing time, leading to faster settlements and improved customer satisfaction. The enhanced, multi-layered fraud detection capabilities resulted in a marked decrease in financial losses due to fraud. By automating routine tasks and providing AI-driven insights and fraud flags, the solution empowered claims adjusters to focus their expertise on complex claims negotiation, customer interaction, and investigating genuinely suspicious cases, leading to higher job satisfaction and overall productivity gains. The trend analysis in Redshift also provided valuable insights for refining underwriting rules and future fraud prevention strategies.

Technologies Used: Amazon Bedrock, Amazon Redshift, Amazon SageMaker (implied for ML fraud models), Amazon S3, AWS Lambda, Claims System Integration APIs