The below listed best practices are based on our experience with customers who use our smart name-matching technology (API version 2.0+).
Best Practice for Sanctions Screening
There is no ‘one fits all’ balance between under-defining (many false positives) and over-defining (possible false negatives) the screening as this largely depends on the data quality of the business partner data as well of the specific Sanctions List and also the 'risk appetite', exposure to high-risk customers/countries, and the compliance resources any organization has.
Our Name-Matching Scoring Model
Our smart name-matching technology helps to reduce false positives without the trade-off of a higher risk of false negatives. The API returns for every search result a Confidence Score between 0 and 1, 1 meaning it's a 100% exact match. The confidence score takes several factors into consideration which are explained below and which also can be customized based on your specific use case.
The confidence score is a weighted matching score calculated based on individual field scores for all parameters used in the search request.
For fields with the _scoring_weight suffix, you can change the relative weight of that field compared to the others for the confidence_score. We usually recommend using the default values as those are based on best practices but depending on your specific situation it can make sense to change them.
If a field does not have a _scoring_weight suffix it means that only exact matches are returned for searches using that specific field.
The score_if_null parameter allows changing the percentage of the respective field weight which is applied when there is no data in the database record for that field.
For example, if you are defining your search with name and year_of_birth - the API will score all records with a match in both fields and will then also apply a value defined in score_if_null for records that don’t have data for those fields. The data quality in Sanctions lists varies a lot: In some lists only 50% of all records have a year_of_birth field while in other lists (for example OFAC's SDN list) 100% of all records include that data point.
The min_score parameter filters all results with a lower score.
Screening for Individuals
For Individuals, a good approach for example is to start with the Full Name and the Date of Birth (DOB) or Year of Birth (YOB for a ‘wider’ approach) in your search request. Usually, these two data points are sufficient and lead already to good results with our smart matching technology. Including additional data points like Country of Birth, Passport IDs and/or Addresses in your search request can help reduce further the number of false positives. (If you use any of the country fields please make sure to use the country information in ISO 3166-1 alpha-2 format).
Screening for Entities
A recommendable approach for Entity screening is to use the Name of the Organization along with the Country of Residency field ( ISO 3166-1 alpha-2 format) and Entity Type = "Entity". This will usually lead to good results, and with our name-matching algorithm you don't need to remove legal forms like LLC, INC etc.
Screening for Vessels, Aircraft, and Crypto Wallet Addresses
Our database also contains vessel/aircraft names and IDs as well as Crypto Wallet Addresses which can be searched for in the respective fields.
Screening against Sanctioned or High-Risk Jurisdictions
sanctions.io also provides the ability to screen your customers/business partners also against jurisdictions that are sanctioned, either comprehensively (embargo) or sectoral, or defined by FATF as high-risk jurisdictions.
To include this feature in your screening setup, the following simple steps are necessary:
- Include the country of residence of your business partner in your search request (country_residence field)
- Make sure the required data sources are included in the data_source field. (For example OFAC-COMPREHENSIVE, FATF) See our comprehensive list of all Targeted Sanctions Lists and High-Risk Jurisdictions Lists in Sanctions & Watchlists.
Best Practice for PEP Screening
Our PEP Screening endpoint allows searching for the Full Name in connection with a Fuzzy value. We recommend starting with a fuzziness value of around 10 to 30 (default 15).
The above-discussed screening setups should be a good starting point for most organizations. The performance of this setup should be continuously analyzed and fine-tuned over time.