Equifax Identity Matching vs Splink

Equifax Identity Matching links customer information held in disconnected sources, including records where names or addresses differ. It assigns persistent consumer identifiers and can create consumer, household, and address keys, helping organizations form a single customer view. Equifax says it compares multiple datasets and data-element combinations, and can combine company records with consumer credit and specialty finance data. Matching and updates are described as cloud-based and near real time, with incremental key changes available through delta services. Delivery options include batch services, an online API, and cloud-native Keying as a Service in Snowflake or Google Cloud Platform BigQuery environments. Confidential Keying keeps raw personal information in a customer cloud environment. Data is encrypted and tokenized, usage is regulated by GLBA, and processes are regularly validated. OAuth 2.0 client credentials secure APIs; production access requires credentials and whitelisted IP addresses. Pricing is on request.

Splink is a free Python package for probabilistic record linkage, used to deduplicate records and connect them when unique identifiers are absent. Its linkage method follows the Fellegi-Sunter model and can be trained without labeled examples. Matching can include term-frequency adjustments and user-defined fuzzy logic. Splink creates SQL for a selected backend, with documented support for DuckDB, Spark, SQLite, and PostgreSQL. DuckDB is recommended for most users; Spark is recommended for very large linkages or when a Spark cluster is more accessible. The maker says it can run on DuckDB or big-data backends such as Spark for linkages of 100+ million records. It works best with standardized data across several columns that are not highly correlated, rather than a single bag-of-words column. Interactive visualizations help examine and diagnose models, predictions, and clusters. Install with pip or conda, with optional backend-specific installation for Spark and PostgreSQL. It runs on Linux, macOS, and Windows, as well as self-hosted environments.