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Insurance

Multi-Cloud Insurance Claim Processing System

18 months45 professionals$11.5M budgetCompleted September 2024

Executive Summary

A global insurance provider struggled with fragmented claim processing across multiple systems, causing delays and poor customer experience. We implemented a data mesh architecture spanning AWS, Azure, and GCP, achieving 20% faster processing, 99.9% data accuracy, and 50% improvement in fraud detection while enabling seamless scalability across all cloud platforms.

The Challenge

Fragmented claim processing systems unable to handle scale and complexity of modern insurance operations

Key Issues

  • Claims data scattered across multiple legacy systems
  • Processing delays averaging 5-7 days per claim
  • Limited fraud detection capabilities missing 30% of fraudulent claims
  • No real-time visibility into claim status
  • Inability to leverage IoT and telematics data
  • Compliance challenges across different jurisdictions

Business Impact: Poor customer satisfaction and significant revenue loss from fraudulent claims

The Solution

Multi-cloud data mesh architecture with real-time processing and advanced fraud detection

Phase 1: Architecture Design

Duration: 3 months

  • Designed data mesh with domain-oriented ownership
  • Established cross-cloud networking strategy
  • Created data product specifications
  • Defined security and governance framework

Phase 2: Platform Development

Duration: 6 months

  • Implemented data ingestion from multiple sources
  • Built real-time processing with Apache Flink
  • Deployed Kubernetes clusters across clouds
  • Created unified API management layer

Phase 3: Analytics & ML

Duration: 5 months

  • Developed fraud detection ML models
  • Built predictive claim analytics
  • Implemented real-time claim scoring
  • Created performance monitoring dashboards

Phase 4: Integration & Rollout

Duration: 4 months

  • Integrated with policy management systems
  • Connected customer service platforms
  • Migrated historical claim data
  • Conducted phased regional rollout

Technologies Used

Apache KafkaDatabricksKubernetesApache FlinkMLflowAWS S3Azure Data LakeGoogle BigQueryTerraformIstio

Results & Impact

20% Faster
Processing Speed
End-to-end claim processing
99.9%
Data Accuracy
Validated across all systems
50% Better
Fraud Detection
Improvement in fraud identification
10x
Scalability
Volume handling capability
30%
Cost Reduction
Operational cost savings
<200ms
API Response
Average API latency

Business Impact

  • Reduced average claim processing time from 7 to 3 days
  • Saved $8M annually through improved fraud detection
  • Increased customer satisfaction scores by 35%
  • Enabled launch of usage-based insurance products
  • Achieved compliance across all operating regions
The multi-cloud claim processing system has transformed our operations. We now process claims faster and more accurately than ever before, while the fraud detection capabilities have saved us millions. The flexibility of the data mesh architecture positions us for future growth.
Chief Technology Officer
Global Insurance Provider

Key Lessons Learned

1

Data mesh requires strong governance and domain ownership

2

Cross-cloud networking complexity often underestimated

3

Containerization essential for multi-cloud portability

4

Performance optimization differs significantly across clouds

5

Change management crucial for domain team adoption

Next Steps

Following the success of this transformation, the roadmap includes:

  • Integration with IoT devices for real-time risk assessment
  • Implementation of blockchain for claim verification
  • Expansion to parametric insurance products
  • Development of customer self-service claim platform