> 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-have-any-machine-learning-or-ai-capabilities.md). # Does Screena have any machine learning or AI capabilities? Machine learning prediction scoring is at the core of our matching engine to reduce false positives when simple deterministic rules are not accurate enough to retain or reject candidate hits immediately. We first identify the **typology** and **cultural context** of both customer and list names. Detecting the [typology](https://developer.screena.ai/#about-objects) and the [culture of a name](https://developer.screena.ai/#name-cultures) is instrumental in directing candidate hits to the most suitable machine learning models. We then have distinct **supervised machine learning models** specifically trained across hundred thousand names for either individual or organization names. For individuals, our models are segmented across distinct cultural groups. We train each model with specific name datasets. Our machine learning models encompass more than **100 name-matching features** to increase the accuracy of name similarity scoring and dramatically reduce the number of false positives. We are also developing new AI techniques to automatically detect geographical elements within unstructured fields in the context of transaction screening. Such technics can help to accurately parse named-entities and addresses, ensuring adequate matching of semantically similar elements against entity and embargo lists. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://help.screena.ai/resources/faq/name-matching/does-screena-have-any-machine-learning-or-ai-capabilities.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.