Entity Resolution at Scale: How Graphs Match Millions of Records Without Losing Precision

Entity resolution determines whether records from different systems represent the same person, organization, device, or other real-world entity, and becomes increasingly difficult at enterprise scale because potential comparisons grow rapidly while data is often incomplete, inconsistent, or intentionally manipulated. Its central challenge is balancing under-resolution, which leaves related identities and behaviors fragmented, against over-resolution, which incorrectly merges unrelated records and can distort fraud, compliance, customer, and healthcare decisions. Graph-based approaches supplement deterministic and probabilistic attribute matching by evaluating relationships among accounts, devices, transactions, addresses, organizations, and other connected entities, providing evidence that can strengthen ambiguous matches or reveal contradictions. Effective enterprise systems require efficient candidate generation, continuous updates, confidence thresholds, explainability, and scalable infrastructure so that identity decisions can be reviewed and adapted as new information arrives. TigerGraph positions its distributed graph platform as supporting real-time entity resolution for fraud detection, AML/KYC, Customer 360, and healthcare by maintaining connected relationship context across large datasets.

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