Identity Resolution: a Critical Technology in Anti-Money Laundering

July 03, 2018 | Identity Resolution, Intelligence Analysis, Name Matching, Record Management, Risk Management

Money laundering is a main concern for financial institutions like banks, money transmitters, and investment companies. It typically involves “cleaning” money for terrorists, organized crime, or sanctioned governments or individuals. Financial institutions are required to complete due-diligence procedures to ensure they are not aiding in money laundering activities. They must ensure compliance with a number of applicable Know Your Customer (KYC) and anti-money laundering (AML) rules and regulations related to potential criminal activities and terrorism financing.  While not illegal in most cases, these same institutions need to adhere to additional regulations and processes when dealing with politically exposed persons (PEPs). Failure to comply with those rules and regulations often results in hefty penalties, reputational loss, and even government seizure.

An important part of the due diligence that financial institutions must perform is to screen prospective and current customers against sanction lists like those by the European Union, the UN Security Council, or the US Treasury Department’s Office of Foreign Assets Control (OFAC). Individuals and companies on those lists are typically associated with target countries and regimes, terrorists, international drug traffickers, or are engaged in the proliferation of weapons of mass destruction.

NetOwl’s Identity Resolution provides an effective, robust, scalable, and high-accuracy solution to screen customer records against sanctions lists. It provides accurate, fast, and scalable identity resolution based not only on fuzzy matching of the entity names but also other key entity attributes such as date of birth, place of birth, address, and nationality. It does so by utilizing its unique proprietary search and indexing engine that allows for combinations of evidence from multiple matching attributes. Additionally, it allows for application-specific business rules to determine what combination of record attributes should be matched and how important each attribute is to the overall matching process.

NetOwl’s Identity Resolution leverages NetOwl’s award-winning machine, learning-based, multicultural, multi-lingual name matching product to enable sophisticated name matching of various entity types across different languages. Accuracy of name matching of various entity types – such as people, organizations, places, and addresses – is crucial in identity resolution, since the values of many important record attributes are names of such entity types. For example, spouse (person), child (person), employer (organization), education (organization), place of birth (place), and home address (address).

NetOwl’s Identity Resolution supports multiple languages and handles matching of a name in one language against names in other languages (Latin-based and Cyrillic alphabets, Chinese, Arabic, and Persian scripts).

As an example of a customer’s use of NetOwl’s Identity Resolution, IDT Payment Services, Inc., a licensed money transmitter and provider of an international money transfer service via its flagship Boss Revolution payments platform, uses NetOwl’s Identity Resolution to ensure compliance with applicable KYC and AML regulations. NetOwl’s Identity Resolution allows IDT to perform entity record matching with state-of-the-art accuracy, thus reducing compliance risks.

Last but not least, NetOwl’s Identity Resolution can be used to understand who knows whom through Social Network Analysis. For instance, NetOwl can detect people or organizations with similar addresses or people with a kinship relationship. Detecting relationships between people and/or organizations allows financial organizations to proactively identify complex money laundering schemes and other financial crimes.

NetOwl’s Identity Resolution provides financial institutions with an effective product to detect sanctioned individuals and organizations and uncover suspicious hidden relationships and thus help comply with AML rules and regulations.