The ever increasing regulatory requirements for Anti-Money Laundering (AML) compliance presents significant challenges for financial institutions and small businesses globally. Efficiently navigating these requirements is not only crucial for legal adherence but also for safeguarding the integrity of the global financial system. In response to this challenge, we develop a framework that leverages advanced algorithms to improve the identification and risk assessment processes within Know Your Customer (KYC) procedures. By employing a technique for measuring 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 singular, 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 an enhancement of the overall AML/CFT efforts within the EU and beyond.