The Hidden Risk in AML Programs: Poor Data Governance

We all know the saying as it relates to model data: 🚮 garbage in, garbage out 🚮 . 

🚨 But when it comes to AML models, the stakes are obviously much higher...

Many institutions invest millions in advanced AML systems, yet the models are only as effective as the data feeding them.

🛑 Inaccurate, inconsistent, or incomplete data can inflate false positives, obscure suspicious activity, and drain compliance resources. And even worse, it can leave real financial crime undetected.

That’s why data reconciliation is so critical. Aligning inputs from core banking systems, customer records, transaction logs, and third-party feeds is critical and expected. 

🗽 NYDFS 504 mandates rigorous data validation for AML systems for New York-regulated institutions, but this standard is increasingly being viewed as best practice across the industry.

💡 Equally important is clear data ownership. Without it, even strong compliance teams risk working with fragmented, stale, or unverifiable information. Compliance can’t just default to rely on IT. They need to actively own, validate, and challenge the data driving their models. 

Strong governance, traceability, and validation processes are essential parts of an effective AML lifecycle.

This isn’t just about checking a box. It’s about building AML programs that are resilient, auditable, and actually work.

🙏 How is your organization fostering accountability for data quality in AML efforts? I’d love to hear what’s working and where challenges remain.