> 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/personalization/what-screening-parameters-can-i-configure-to-meet-my-risk-appetite.md). # What screening parameters can I configure to meet my risk appetite? On top of name similarity scoring, our screening approach allows you to automatically leverage **secondary attributes** (i.e., dates of birth, places of birth, nationalities, addresses, places of registry, flags, BICs, LEIs) for comprehensive named-entity matching, also known as [secondary attributes-matching](/resources/faq/name-matching/how-does-secondary-attributes-matching-work.md). The accuracy of the matching algorithms applied to secondary attributes can be adjusted based on the risk appetite of the firms as well as the completeness and quality of the data available in the lists. Typically, geo-based algorithms can be configured to match locations within the same region or subregion, while date-matching algorithms can detect whether two dates are within the same month, year, quadrennium or decade. Screening results systematically include all attributes and information needed to understand why a match is returned. Screena also provides **custom libraries** to rate – and alert on – risky locations (e.g., countries, cities or regions) when screening customers and transactions, in either structured or unstructured format. Results are returned in accordance with the risk rating library configured by the customers. --- # 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/personalization/what-screening-parameters-can-i-configure-to-meet-my-risk-appetite.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.