Frequently asked questions

In order to set up a matching process with low error rates (both false positives and false negatives), it may be helpful to reflect on what input data you can provide in order to allow precise entity comparison.

Consider the following options:

• Entity type: Do you know if a record in your screening set refers to a person or an organization? Setting the schema in your matching query to Person and Organization will increase precision.

• Multiple name aliases: Can you provide multiple name aliases (in the name or alias fields)? For persons, are you able to include the name parts separately (in the firstName, middleName, lastName properties)?

• Additional qualifiers: The following can be useful qualifiers to include in your query in order to reduce false positives from name-only matches:

  • Date of birth: Can you provide a birth datefor individuals (birthDate)? This is a fantastic way to reduce false positives, even if you only have the year, or year/month.
  • Identifiers: For companies, do you know any registration numbers (registrationNumber) or tax identifiers (taxNumber)?
  • Nationality, jurisdiction or country: Do you know the nationality of a person, or the country in which a company was registered (country)?

• Filter by topics: When you query the /match/default endpoint, it will return any relevant entities from sanctions lists, government watchlists and PEP databases. If you're only interested in a limited subset of these risk categories, try using the ?topics= filter to select the entity risk tags that you are interested in.

• Filter by scope: You can also narrow down your query by selecting a more narrow scope of entities via the endpoint. For example, calling /match/sanctions will only return entities from the core sanctions datasets, and /match/peps only entities included in a PEP database.