What explains money laundering risk? Empirical evidence from a risk-based approach in the Brazilian financial system

Resumo

This study investigates the determinants of money laundering (ML) risk in the Brazilian financial system, addressing the lack of empirical evidence supporting risk-based regulatory models. The results provide partial empirical validation of Brazil’s AML framework, showing that cash transactions and major metropolitan areas are the only robust and persistent determinants of reported ML risk. Using a mixed-methods approach, the research combines a systematic literature review with panel data econometric analysis (2010-2022). The scarcity of empirical studies testing ML risk determinants limits the refinement and comparability of internal risk assessment (AIR) models. Given legal restrictions on access to customer-level data, the study relies on publicly available information to evaluate whether regulatory risk factors effectively explain suspicious transaction reporting. The findings indicate that gross domestic product, border location, mining activity, and informal settlements have limited or unstable explanatory power. The findings contribute by demonstrating that major metropolitan areas should be treated as structural risk-weighting factors in AIR models, supporting more efficient allocation of compliance resources and evidence-based regulatory improvements. The study combines a systematic literature review with panel data econometric analysis of Brazilian federative units from 2010 to 2022. Multiple regression models and Chow structural break tests were employed to assess the stability of risk determinants before and after major regulatory changes.