Data Quality Engineering Frameworks for Regulatory-Grade AML Transaction Monitoring Systems

Abstract

Anti-Money Laundering (AML) systems function within tightly enforced compliance architecture defined by global regulators. The increasing scale and sophistication of financial fraud and money laundering have made robust AML controls critically important. Breakdowns in AML controls can expose institutions to reputational damage, regulatory penalties, and systemic financial risk. These challenges necessitate the adoption of systematic, data-driven approaches for effective risk detection and mitigation. Contemporary AML platforms integrate rule engines with machine learning models to evaluate transactional risk patterns, various risk scoring models demonstrating data integrity checks across multiple stages of data lifecycle. Despite these advancements, financial crime incidents continue to rise, indicating gaps in current system effectiveness. Still a lot needs to be explored in data engineering to help in data analysis and resolution. This paper proposes data quality engineering (DQE) model for regulatory grade AML environments. DQE framework focusses on 5 data quality dimensions - completeness, accuracy, consistency, timeliness, and lineage integrity. This architecture uses Microsoft Azure, Azure Databricks and data will be stored in Delta Lake. Technical validations performed using above model also aligns with the regulatory compliances and obligations under the Bank Secrecy Act (BSA), Financial Action Task Force (FATF) recommendations, and Office of Foreign 
Assets Control (OFAC). This study hypothesizes that DQE implementation can reduce false negatives and can improve financial crime detection.