Building a Real-Time Identity Matching System Using Deep Learning and Graph Modeling

The challenge of real-time identity matching continues to grow in domains like healthcare, retail, and financial services due to increasing data fragmentation, mobility, and the demand for personalized experiences. Current deterministic and probabilistic matching systems struggle with data sparsity, inconsistencies, and lack of context. This paper proposes a novel, scalable framework that combines deep learning techniques with graph-based modeling to enhance identity resolution. We present a conceptual system architecture, examine limitations in existing solutions, and highlight how our proposed method leverages embeddings, attention mechanisms, and knowledge graphs to significantly improve match accuracy while supporting real-time applications. The framework addresses critical challenges including scalability, context awareness, and multi-modal data integration by utilizing Graph Neural Networks (GNNs) and transformer-based architecture.