Self-Healing Microservices for Insurance Platforms: A Fault-Tolerant Architecture Using AWS and PostgreSQL
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P113Keywords:
Self-healing microservices, fault tolerance, insurance platforms, AWS, PostgreSQL, resilience, cloud-native architectureAbstract
Insurance: The global insurance industry is in the process of a digital transformation driven by microservices, cloud-native computing, and distributed data platforms. Traditional monolithic insurance platforms are becoming problematic as they scale, become unmaintainable and unreliable, and adversely impact the customer experience and the continuity of operations. This paper describes a self-healing microservices-based architecture using Amazon Web Services (AWS) and PostgreSQL to build a fault-tolerant, resilient insurance system. The architecture that we propose combines auto-scaling, monitoring, service mesh orchestration and automated failover for database availability. Unlike traditional fault-tolerance methods, the architecture uses self-healing concepts based on proactive error-monitoring and reactive recovery mechanisms incorporated into microservices themselves. The key contributions in this paper are: (1) systematic framework for fault detection and automated healing in insurance microservices; (2) the integration of PostgreSQL replication and failover with AWS-native services (CloudWatch, EKS, RDS Multi-AZ); and (3) proof of decreased downtime and increased resiliency metrics vs. legacy architectures. Undergoing synthetic workloads that included claim processing, underwriting, and policy management, the system was able to maintain 99.95% uptime, 40% reduction in mean time to recovery (MTTR), and an improvement of 32% throughput during failure scenarios. The proposed architecture can be of much benefit to insurance platforms by ensuring business continuity, regulatory compliance and enhanced customer satisfaction. This paper describes the theoretical foundations, design methodology in practice, experimental assessment, and implications for future large-scale deployments
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