Counterparty Anti-Money Laundering Risk: A Three-Factor Matrix Model for Assessment and Control

This study develops and applies a three-factor matrix model for assessing counterparty risk within Anti-Money Laundering (AML) measures. The research problem arises from the need to transform qualitative features related to ownership, effective control, economic logic of operations, geographical links, documents and review behavior into a comparable and documented risk score. The model uses three factors: counterparty exposure, transaction impact and control vulnerability. Each factor is assessed on a five-point ordered scale, and the overall risk score is obtained by multiplying the three values. The methodology includes scoring rules, rating interpretation, an application algorithm and a link between the profile, due diligence, ongoing monitoring and updating. The model was applied in 2026 to 25 anonymized counterparty profiles selected for methodological demonstration across different risk configurations. Identifying data, personal information and confidential business information were removed, while the relevant risk characteristics were preserved. The results demonstrate how the model distinguishes between low, moderate, increased, high and critical risk and how it creates a traceable link between established facts, numerical assessment and follow-up control actions. The study does not claim predictive accuracy, detection of money laundering or empirical validation against external AML outcomes. Its contribution is methodological and applied: it provides a transparent and explainable framework for structured, documented and proportionate management of counterparty AML risk, including potential use in AI-assisted monitoring under human oversight.