Intelligent Data Analytics Platform for Insurance Domain Test Data Management and Privacy Preservation

Abstract

Test data management in insurance applications presents unique challenges due to stringent privacy regulations, complex data relationships, and the need for realistic test scenarios that preserve data utility while ensuring privacy compliance. This paper introduces an intelligent data analytics platform that leverages advanced machine learning and differential privacy techniques for comprehensive test data management in insurance domains. The proposed system combines generative adversarial networks (GANs), variational autoencoders (VAEs), and statistical disclosure control methods to create synthetic test datasets that maintain statistical properties and business logic relationships of production data while ensuring complete privacy preservation. Our methodology incorporates domain-specific knowledge graphs to preserve complex insurance business rules, policy relationships, and claim processing workflows in synthetic data generation. The platform utilizes federated learning approaches to train data generation models across multiple data sources without compromising sensitive information. Implementation results from Guidewire PolicyCenter, ClaimCenter, and BillingCenter applications demonstrate 91% statistical similarity to production data while achieving zero privacy risk scores according to established privacy metrics. The system incorporates advanced data profiling algorithms to automatically discover data patterns, dependencies, and constraints essential for realistic test scenario creation. Machine learning models analyze historical test execution patterns to optimize data generation strategies and ensure comprehensive test coverage. The research addresses critical challenges including maintaining referential integrity across complex insurance data models, preserving temporal relationships in policy and claim lifecycles, and ensuring regulatory compliance with GDPR, CCPA, and industry-specific privacy requirements. Performance evaluation shows 63% reduction in test data preparation time, 78% improvement in test scenario realism, and 100% compliance with privacy audit requirements. The proposed solution integrates seamlessly with existing CI/CD pipelines and test automation frameworks, providing scalable test data provisioning capabilities. Comparative analysis with traditional data masking and anonymization techniques demonstrates superior privacy protection while maintaining higher data utility for comprehensive testing scenarios.

Topics & Concepts