Distributed Data Engineering Pipelines for Real-Time Insurance Claim Processing
DOI:
https://doi.org/10.15662/IJEETR.2022.0406013Keywords:
Real-Time Data Processing, Data Engineering Pipelines, Insurance Claims Analytics, Event-Driven Architecture, Stream Processing, Multi-Source Data Integration, Real-Time Scoring, Cloud Microservices, Data Lakes and Warehouses, Operational Intelligence, Data Quality and Compliance, Observability and MonitoringAbstract
In today’s data-driven world, organizations seek to rapidly and accurately convert multi-source data into proactive intelligence to drive smart business decisions. Allowing real-time or near-real-time ingestion, evaluation, and scoring of operational data can improve the speed and efficiency of existing business processes, enabling organizations to uncover risks and opportunities faster than traditional batch-oriented approaches. This paper discusses the ground-up architectural design considerations required to allow real-time and end-to-end processing of multi-source claim data at an insurance firm.
The key architecture design principles focus on ingesting and pretreating operational data, stream processing alternatives with integrated data quality and compliance capabilities, and defining and operationalizing real-time scoring and evaluation pipelines with observability and monitoring capabilities. Such a design is instrumental in developing distributed data engineering pipelines that efficiently link cloud-based microservices with inevitable data lakes and warehouses. While the work focuses specifically on claim processing, the principles are applicable to other industry verticals where event-driven implementations of core business processes are required.
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