Scalable Data Lineage And Auditability Models For AML Compliance In Snowflake Environments

Abstract

In today’s modern world, Anti-Money Laundering (AML) is very crucial and important. It depends primarily on the integrity, traceability, and explain ability of financial data as the data journey goes through the complex native architectures. Regulators are continuously updating the guidelines of AML and making the process stricter. Regulators mandate financial institutions like bank to demonstrate not only the accurate suspicious activity monitoring systems but also the complete data lifecycle that influences risk score calculations, alerts, sanctions, investigations and regulatory filings. In traditional approach, it is a struggle to find the data chain of custody due to limited visibility of data multiple sources, data paths and inconsistent audit controls across various analytical platforms. This paper presents a scalable data lineage and auditability framework designed for AML compliance on Snowflake based data platforms. The framework introduces multi-layer lineage architecture which gives end to end data visibility across AML pipelines , it includes ingestion traceability, business rule transparency and regulatory evidences. To achieve the framework objective, this architecture leverages snowflake services, object dependencies, access history, task execution and governance records. This paper proposes an architecture to integrate data quality controls, metadata governance, data lifecycle and compliance dashboards which can improve data transparency, reduces fraud and suspicious activities investigation effort, audit reviews and strengthen regulatory readiness and enhances trust in AML monitoring systems.