Select data for best performance and risk typology coverage
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AML AI has MANDATORY fields which are essential for the
detection of money laundering, such as transaction value and time. The product
also has RECOMMENDED fields which are used to improve risk coverage, for
fairness analysis, and to help manage data lineage.
To optimize coverage, you should provide RECOMMENDED fields because some of them
enable additional features that are critical risk indicators for some
typologies.
Data fields categorized as RECOMMENDED can improve risk typology coverage in two
ways:
By supporting typologies that don't have any supporting features
calculated from the MANDATORY data fields (for example, money laundering
through high-risk jurisdictions)
By strengthening the coverage for an already supported typology with new
features that yield additional results (for example, money muling)
The following table summarizes the purpose of all RECOMMENDED fields in the AML
AI schema.
Field can be necessary to correctly model how entities change over time, depending on how you manage your data internally (see Understanding how data changes over time).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[[["\u003cp\u003eAML AI requires mandatory fields like transaction value and time for money laundering detection.\u003c/p\u003e\n"],["\u003cp\u003eRecommended fields are used to enhance risk coverage, perform fairness analysis, and manage data lineage.\u003c/p\u003e\n"],["\u003cp\u003eProviding recommended data fields improves risk typology coverage by enabling new typologies or strengthening existing ones.\u003c/p\u003e\n"],["\u003cp\u003eFields such as \u003ccode\u003enationalities\u003c/code\u003e, \u003ccode\u003eresidencies\u003c/code\u003e, \u003ccode\u003ebirth_date\u003c/code\u003e, \u003ccode\u003eestablishment_date\u003c/code\u003e, and \u003ccode\u003egender\u003c/code\u003e all have a positive impact on typology coverage.\u003c/p\u003e\n"],["\u003cp\u003eFields such as \u003ccode\u003eis_entity_deleted\u003c/code\u003e, \u003ccode\u003esource_system\u003c/code\u003e and \u003ccode\u003erisk_typology_measurements\u003c/code\u003e have no impact on typology coverage, however they can be used for other purposes such as data quality management and recall measurement.\u003c/p\u003e\n"]]],[],null,["# Select data for best performance and risk typology coverage\n\nAML AI has MANDATORY fields which are essential for the\ndetection of money laundering, such as transaction value and time. The product\nalso has RECOMMENDED fields which are used to improve risk coverage, for\nfairness analysis, and to help manage data lineage.\n\nTo optimize coverage, you should provide RECOMMENDED fields because some of them\nenable additional features that are critical risk indicators for some\ntypologies.\n\nData fields categorized as RECOMMENDED can improve risk typology coverage in two\nways:\n\n- By supporting typologies that don't have any supporting features calculated from the MANDATORY data fields (for example, money laundering through high-risk jurisdictions)\n- By strengthening the coverage for an already supported typology with new features that yield additional results (for example, money muling)\n\nThe following table summarizes the purpose of all RECOMMENDED fields in the AML\nAI schema."]]
