Matching Methods

To ensure the highest level of assurance, we employ several methods to validate user data against authoritative sources. The method used often depends on the country, regulatory requirements, and the data sources available.

Below is an explanation of the core methods.

1x1 Matching

This method involves matching one input data point (typically a national ID or similar identifier) against our database. If a direct match is found, we confirm the identity. However, if the initial attempt fails, we may try alternate trusted data sources in a waterfall sequence. This means we continue validating through successive providers until we either achieve a match or exhaust all options. Importantly, a partial match does not stop the process—we only stop once we find a full and conclusive match or determine that no match exists.

The 1x1 Matching logic requires matching a user’s personal data against one source to receive a Full Match on their identity.*

*The applicable country sources are screened until a Full Match on the identity is made. If a Full Match is not found, the system then repeatedly screens those sources for a Partial Match on the user’s identity.

2x2 Matching

This method requires matching two input data points (e.g., name + date of birth, or national ID + phone number) against two corresponding fields in our database. Just like with 1x1, we follow a waterfall approach, querying multiple data sources sequentially. We persist through each step until we find a complete match across both data fields. Partial or single-field matches are not sufficient; the validation process continues until a definitive 2-field match is achieved or all sources have been checked.

The 2x2 Matching logic requires matching user’s personal data against two sources to receive a Full Match on their identity.*

*The applicable country sources are screened until a Full Match on the identity is made. If a Full Match is not found, the system then repeatedly screens those sources for a Partial Match on the user’s identity.

Data Category and Attribute Matching

The following are the scenarios that result in a Full Match for the Name, Date of Birth, and ID data categories. A Partial Match is only possible for the Name category.

Name Category Matching

A Full Match on the Name category is considered when any of the following data attribute matching scenarios is met:

• First Name Full Match + Last Name Full Match

A Partial Match on the Name category is considered when any of the following data attribute matching scenarios is met:

• First Name Full Match
• Last Name Full Match

Date of Birth Category Matching

A Full Match on the Date of Birth category is considered only when the following data attribute matching scenario is met:

• Year of Birth Full Match + Month of Birth Full Match + Day of Birth Full Match

A Partial Match on the Date of Birth category is not possible.

ID Category Matching

A Full Match on the ID category is considered only when the following data attribute matching scenario is met:

• Identification Number Full Match

A Partial Match on the ID category is not.

Fuzzy Matching for Data Attributes

These are the criteria for fuzzy matching on different data attributes against country sources.

• Name Data Attributes
  For a Name category data attribute to generate a Full Match, it should be within 70% of the Levenshtein character similarity in comparison to the records in the data sources. E.g., if the Name attribute input is ‘Christophel’ and the actual name is ‘Christopher’, a Full Match is returned based on the fuzzy matching logic. This system is applicable to the First Name and Last Name data attributes.

• Date of Birth and ID Data Attributes
  We do not employ any kind of fuzzy matching on data attributes within the Date of Birth or ID data categories. An exact match is required for these fields.

Examples

Below are examples of data attribute inputs that would satisfy the criteria of the fuzzy matching logic and generate a Full Match.