> 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/what-methods-does-screena-use-to-reduce-false-positives.md).

# What methods does Screena use to reduce false positives?

Besides traditional technics such as whitelisting, false positives are reduced with three combined methods:

1. [**Secondary attributes-matching**](/resources/faq/name-matching/how-does-secondary-attributes-matching-work.md) to automatically discard hits where names match but other entity attributes are incompatible. These attributes include entity types, sexes, BICs, LEIs, dates of birth, dates of registry, date of build, places of birth, places of registry, places of build, addresses, nationalities, flags, etc.
2. [**Machine learning prediction scoring**](/resources/faq/name-matching/does-screena-have-any-machine-learning-or-ai-capabilities.md) based on 100+ name matching features to go way beyond the natural limitations of traditional fuzzy algorithms and increase scoring precision by analyzing numerous name characteristics altogether.
3. [**Delta screening**](https://developer.screena.ai/#periodic-screening) to avoid periodically regenerating the same results. When using delta screening, once all source records have been screened against all watchlist records, only deltas are considered (i.e., any new record or any update on at least one of the fields used for matching is deemed a delta).


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