casematching

Casematching, sometimes written as case matching, is a process in information management and analysis used to determine whether separate records, events, or documents pertain to the same underlying case or scenario. It combines attribute comparison, metadata alignment, and contextual reasoning to establish equivalence or linkage across data sources.

Approaches typically involve data normalization, feature extraction, and similarity assessment. Techniques include exact matching on identifiers, fuzzy string matching, token-based similarity, and machine learning models trained to predict case equivalence. In practice, casematching often relies on rule-based thresholds and probabilistic scores to decide whether two items belong to the same case.

Common applications include fraud detection and insurance claims, where multiple transactions may represent a single fraudulent case; law enforcement and investigations for linking related incidents; healthcare and clinical decision support for identifying similar patient cases; and customer support for consolidating tickets that describe the same issue.

Challenges include data quality issues, inconsistent naming, duplicates, privacy and security constraints, and scalability across large datasets. Heterogeneous data sources—structured databases, documents, and free text—make normalization difficult, and evolving cases require continual model updates.

Related concepts include case-based reasoning, entity resolution, and record linkage. An example is linking two payment records and a device identifier to a single fraud case when they share multiple corroborating attributes.