Know your red flags: Geographic risks in (suspicious) transaction monitoring

Obliged entities and law enforcement agencies face similar challenges in efficiently assessing and mitigating money laundering risks related to clients, transactions and suspicious transaction reports (STRs). We present a novel geographic risk assessment framework designed for use in both administrative and commercial practice. The framework builds on the notion that financial secrecy creates a criminogenic environment, enabling illicit financial flows (IFFs) to hide and move more easily. Since IFFs are sensitive to jurisdictional levels and types of financial secrecy, incorporating measures of secrecy is crucial for effective risk assessments in large-scale financial datasets. We combine Financial Secrecy Index scores as proxies for geographic markers of financial secrecy with transaction values to compute an IFF risk score for each transaction. The framework requires only minimal input data and produces a clear, dynamic risk score that avoids subjective, discriminatory, or politically biased judgments. We exemplify the approach by applying it to the FinCEN files – leaked STRs published by the ICIJ. We show how a modest harmonisation of STR formats coupled with effective risk assessment can transform an underutilized mass of reports into a systematised treasure trove of hierarchised red flags to counter IFFs.