Understanding the Fuzziness Score in the Veriff API’s PEP and Sanctions Matching
The concept of fuzziness in search queries allows the Veriff API to identify potential matches in PEP (Politically Exposed Person) or sanctions lists even when there are slight differences or variations in the provided data. This feature is particularly useful for accommodating typographical errors, naming variations due to international differences, or other minor discrepancies.
What Is the Fuzziness Score?
The fuzziness score, reflected in the API response under pepSanctionMatches -> hits -> score, quantifies the allowed variation during a comparison between the search term and existing records. It helps users identify possible matches even when the input isn't an exact match. For example:
A score of 0.7 represents 7% variation, indicating the degree of difference tolerated between the search term and matching records.
The score does not guarantee the person in the response is definitively listed on a PEP or sanctions database. Users must follow up by reviewing the match details, typically accessible via URLs in the response, for confirmation.
How Fuzziness Works Across Names and Dates
Name Matching
In name comparisons:
The score accounts for misspellings and international naming variations (e.g., "Smith" versus "Smythe").
Fuzziness tolerates matching differences based on character edits. For instance, names such as "Grant" and "Grint" are considered a match due to minor one-character differences.
Date Matching
When incorporating dates (such as date of birth):
A fuzziness score of 0% requires exact matches, meaning names and dates must align perfectly for a match to occur.
For fuzziness settings ranging from 10% to 100%, the algorithm can tolerate a ±1-year difference in date of birth to account for inconsistencies in supplied data.
Ensuring Match Accuracy
While the fuzziness score helps identify potential matches, users must verify results manually to ensure accuracy. A score alone cannot confirm that an individual is featured on the PEP or sanctions list.
Example: Why a 0% Fuzziness Match Can Still Appear?
At 0% fuzziness, matches require complete alignment across all data fields (such as names and dates). However, the system might still flag entries with exact matching to specific fields while overlooking optional fields such as middle names. These discrepancies should be reviewed with further investigation through link-provided records.
Conclusion
The fuzziness score provides a flexible mechanism for handling variations in search input data within the Veriff API’s PEP and sanctions matching process. However, users must not mistake a high fuzziness score as a definitive indicator of a match. Always verify the match systematically to ensure compliance and accurate identification.