Getting ready for AI in AML transaction monitoring
Everyone wants AI in AML transaction monitoring. Fewer people ask whether the data feeding the monitoring engine is complete and correct in the first place.
AML monitoring is one of the largest cost centers in banking compliance, and AI looks like an obvious accelerator. The first idea usually sounds like this: let's use AI to tune and back-test scenario thresholds or deploy AI agents to speed up case handling. Both are good ideas. But they may be premature if you skip one step backwards: verifying the quality of the data feed and the actual system configuration.
Why data quality is the real foundation
Most banks run third-party AML monitoring engines connected externally to the core. That raises a question we find is rarely answered with evidence: who ensures that the complete set of transactional data is captured, correctly transformed and delivered to the external monitoring engine? The room for error is substantial. Confidence should come from real proof through data reconciliation, rather than from simply trusting the implementation.
In FSQUARE RegTech practice, we build a purpose-built dataset (a staging area) with historical data load and run multi-layer reconciliation against what the engine actually consumed. It is no great surprise to find payment types historically missing from the feed, data points populated from the wrong source fields, or risk flags that never reached the engine. Such issues can stay invisible for a long time and silently erode the quality of, and the trust in, the entire AML control framework.
System configuration checks
The second control answers a different question: does the AML monitoring engine actually behave the way the rules documented by your Compliance function say it should? To gain sufficient confidence, FSQUARE rebuilds the monitoring ruleset in our staging area and runs it against historical transactions.
The principle is simple: if the data is complete and the configuration matches the documentation, both environments should produce the same alerts. Where they differ, we trace the root cause.
Even when issues are discovered and need correcting, a documented, self-identified remediation plan is a fundamentally stronger position than a finding raised by an auditor or a supervisory authority.
Where AI fits in
At FSQUARE we currently prioritize expert competence in data mapping and configuration verification, complemented by our first AI-assisted techniques. As AI practices mature, we will embed more AI-enabled controls in our engagements with future clients.
If you would like to discuss and enhance your AML monitoring practices, please contact FSQUARE Partner Darius Jasinskis.