Do you have any AML (Anti-Money Laundering) data that is also your challenge or only data here?

AML (Anti-Money Laundering) data refers to transaction logs, customer KYC records, and other financial data that regulators require to be monitored for suspicious activity. In a data engineering context, AML data is often ingested from banking systems into a data lake, then transformed and enriched with risk scores. Because AML data is highly regulated, you must enforce strict data governance: encryption at rest and in transit, role‑based access control, and audit trails. You also need to handle data quality issues such as missing fields or inconsistent formats. Once the data is in a secure data warehouse, analysts can run AML rules or machine‑learning models to flag suspicious transactions. The key challenges are ensuring compliance with regulations like GDPR or the USA PATRIOT Act, maintaining data lineage, and providing timely access to analysts while protecting sensitive information.