Machine Learning-Powered Entity Resolution: A Scalable Approach for Real-Time Global Customer Matching

Abstract

This article presents a comprehensive approach to entity resolution (ER) that addresses the fundamental challenge of accurately unifying customer identities across disparate global data sources in real-time environments. The article introduces a hybrid record linkage system that transcends the limitations of traditional rule-based approaches by combining deterministic blocking with advanced fuzzy matching algorithms and supervised machine learning techniques. The article leverages Apache Spark's distributed processing capabilities alongside VoltDB's in-memory database technology to achieve both the accuracy and performance required for enterprise-scale deployment. Our methodology incorporates TF-IDF vectorization, Jaro-Winkler distance metrics, and logistic regression ensembles to generate calibrated match likelihood scores that enable flexible decision thresholds for different business contexts. Beyond the technical implementation, the article presents a holistic framework addressing the operational challenges of deploying sophisticated matching systems in regulated environments, including data quality monitoring, stakeholder engagement, and governance models that balance algorithmic consistency with business flexibility. Performance optimizations significantly reduced processing times while maintaining high match quality, enabling both efficient batch reconciliation and real-time matching during customer interactions. The system's self-monitoring and continuous learning capabilities have created a platform that evolves with changing data patterns rather than degrading over time. This article serves as both a technical blueprint and a strategic guide for organizations seeking to implement scalable, explainable, and high-performance entity resolution systems in complex, global environments.