International compliance legislation forces financial institutions such as banks and insurance companies to check whether they are doing business with people or organisations that occur in a variety of sanction lists (or blacklists). Financial companies need to manage these risks in order to avoid consequences such as operational damage or legal penalties and to eventually to keep the trust of their customers. As these blacklist checks are carried out, banks and insurance companies need to meticulously report on the process and the outcome of these checks.




As customer data is being checked against a variety of sanction lists (e.g. OFAC, UNSANCTIONS, EDB, etc.), you will need a somewhat more sophisticated matching process. The matching methods must be able to deal with incomplete data, nicknames and aliases.

Here are a couple of examples of sanction list data:

A date of birth is represented a month or a day: e.g. “1970”. The name field does not consist of a given name and a surname, but only of a surname. Also, nicknames and aliases must be considered: “Frank Haroun” and “Tarek Sharaabi” are names, which refer to a single person. Finally, spelling and phonetic variations such as “Usama Bin Ladin” and “Osama bin Laden” are taken into account.



Human Inference offers an out-of-the-box sanction list matching solution, in which a high degree of quality leads to less manual rework and a higher degree of certainty in the actual check. As financial institutions have to comply with various kinds of legislation, we will help these companies to prevent operational and judicial damage and to achieve next level risk management.