Optimizing Qlik Replicate and Change Data Capture for Real-Time Risk Evaluation in Life Insurance

Authors: Pavan Kumar Veerapally

DOI: https://doi.org/10.37082/IJIRMPS.v13.i5.233147

Short DOI: https://doi.org/

Country: United States

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Abstract: The efficacy of real-time risk quantification in life insurance is fundamentally compromised by the systemic latency and computational overhead inherent in traditional batch-oriented data integration. This research provides a technical evaluation of Qlik Replicate and log-based Change Data Capture (CDC) as a high-throughput, low-latency framework for continuous risk evaluation. An optimized pipeline architecture is proposed, leveraging asynchronous transaction log mining to bypass the performance bottlenecks of SQL-based polling. By implementing a non-intrusive, zero-footprint capture mechanism, the study demonstrates a methodology for synchronizing high-velocity transactional data, encompassing policyholder behavior and claims metadata, into analytical environments with sub-second propagation delays. Central to the analysis is the optimization of CDC commit-log parsing and watermark-based synchronization to ensure transactional atomicity and consistency across heterogeneous distributed systems. Experimental results indicate that the optimized CDC configuration yields a 99% reduction in data staleness, facilitating a transition from static actuarial models to dynamic, event-driven risk scoring. This research describes an architecture for utilizing real-time stream processing as part of a larger framework that enables low-latency fraud detection and accurate risk evaluation in the insurance industry.

Keywords: Change Data Capture (CDC), Qlik Replicate, Real-Time Analytics, Insurance Technology, Risk Evaluation, Data Engineering, Low-Latency Pipelines, Predictive Underwriting.

Paper Id: 233147

Published On: 2025-10-10

Published In: Volume 13, Issue 5, September-October 2025

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