A Risk-Based AML Framework: Finding Associates Through Ultimate Beneficial Owners

The ever increasing regulatory requirements for Anti-Money Laundering (AML) compliance presents significant challenges for financial institutions and small businesses world-wide. Efficiently navigating these requirements is crucial not only for legal adherence but also for safeguarding the integrity of the global financial system. In response to this challenge, we develop a framework that uses advanced algorithms to improve identification and risk assessment processes within Know Your Customer (KYC) procedures. Using a technique to measure graph-based node similarities, our approach enhances the detection of Politically Exposed Persons (PEPs) and their known associates, facilitating a more nuanced and comprehensive analysis than traditional methods allow. We study the dataset of Ultimate Beneficial Owner (UBO) registry in Luxembourg and translate our findings into two risk indicators: involvement with underage shareholders, and number of companies at the address. We integrate these two indicators, as well as several other components of AML compliance, including country risk indices, beneficial ownership structures, and adverse media exposure, into a single coherent risk metric. The framework is designed to be both modular, supporting various degrees of regulatory scrutiny, and scalable, suitable for evolving regulatory landscapes. This risk metric can be used to determine whether Enhanced Due Diligence (EDD) is required by European AML directives. The end result is a more robust defense against financial crimes and improved AML processes within the EU and beyond.