# 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.


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