identitydistinct

Identitydistinct is a metric used in identity resolution and entity linking to quantify the degree to which two identity records are expected to refer to distinct individuals. It is often contrasted with identitymatch, which assesses the likelihood that two records refer to the same person. Identitydistinct can be expressed as a probability or score between 0 and 1, where higher values indicate a stronger belief that the records describe different individuals.

Definitions and computation

Identitydistinct is typically derived from a combination of attribute-level signals and, in some systems, behavioral or contextual data. Common inputs include name variants, birth date proximity, address history, contact details, device or channel fingerprints, and transactional or behavioral patterns. Computation may be rule-based, using weighted dissimilarity across fields, or probabilistic and machine-learned, such as a classifier that outputs P(Different | features). In practice, identitydistinct may be used alongside identitymatch to produce a final linkage decision, often through a decision rule or an ensemble score.

Applications

The concept is used in data deduplication, customer data platforms, fraud detection, and privacy-preserving linking, where organizations must determine whether two records represent the same person or two distinct individuals. It supports risk assessment, access control, and compliance workflows by helping to reduce false matches and missed distinctions.

Limitations and governance

Scores depend on data quality, feature availability, and model biases. Incomplete or biased data can distort identitydistinct, leading to incorrect linkage. Responsible use involves data minimization, transparency about scoring, and adherence to privacy regulations.

See also

Identity resolution, entity matching, record linkage, data deduplication.