The primary functionality of AML AI is to support risk typologies related to transaction monitoring. These typologies include the following:
- Funneling (placement)
- Pass-through funds
- Structuring
- High-risk jurisdictions
- Cross-border activities
- Shell companies
- Professional enablers
- Money mules
To optimize coverage, you should not only provide MANDATORY fields but also RECOMMENDED fields because some of them enable additional features that are critical risk indicators for the less common typologies.
Data fields categorized as RECOMMENDED can improve risk typology coverage in two ways:
- By supporting less common 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 | Tables | Performance impact? | Typology coverage impact? | Other uses |
|---|---|---|---|---|
nationalities | Party | Yes | Yes - money muling
| N/A |
residencies | Party | Yes | ||
birth_date | Party | No | No | Field can be used for your own fairness analysis. Field can be used in feature generation, depending on engine version. |
establishment_date | Party | No | No | Field can be used in feature generation, depending on engine version. |
gender | Party | No | No | Field can be used for your own fairness analysis. |
is_entity_deleted | No | No | 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). | |
source_system | No | No | Field helps you to manage dataset quality. |