> For the complete documentation index, see [llms.txt](https://help.screena.ai/resources/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.screena.ai/resources/faq/name-matching/does-screena-provide-rules-based-and-or-fuzzy-matching-capabilities.md).

# Does Screena provide rules-based and/or fuzzy matching capabilities?

Screena name-matching approach is twofold with a combination of **deterministic rules** and **predictive scoring** methods to minimize false negatives (i.e., achieving high recall) and false positives (i.e., achieving high precision).

Rules-based and fuzzy matching is the core approach to ensure **high recall** before reducing false positives through our machine learning models.

We first employ traditional **edit distance algorithms** such as [Jaro](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) to measure string similarity when initiating searches on lists. We also apply **rules-based algorithms** and use proprietary name libraries to detect specific name patterns that can not be addressed through string distance alone. These include, but are not limited to:

* Name order variations and missing name components,
* Misspellings and errors (inverted letters, missing letters, substituted letters),
* Truncated names,
* Name concatenations,
* Acronyms and initials,
* Nicknames, synonyms and common aliases,
* Titles, honorifics and company legal forms,
* Phonetic resemblances,
* Detection of stopwords, linking words and weighting of common words,
* Detection of locations (cities, towns, regions, ports),
* Numbers variation,
* Domain names.


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