Artificial intelligence has become the default answer to many financial crime compliance challenges. Alert volumes are too high, rules are too rigid, and compliance teams are drowning in false positives. The quick response is often the same: “We’ll fix it with AI.”
But AI is not a magic solution. If screening configurations are noisy or misaligned with risk, layering AI on top only adds complexity.
Napier AI, a next generation intelligent compliance platform, recently explored how firms can improve screening with AI.
Many firms introduce AI late in the process, after alerts are generated, using it to decide which alerts matter. While this reduces some noise, it fails to address the underlying issue—why those alerts are being created at all, it said.
The problem stems from one-size-fits-all screening logic. Everyone is screened against the same lists, unchanged data is repeatedly rescreened, and blunt thresholds drive irrelevant results. This generates huge volumes of alerts, overwhelming analysts and forcing AI into a reactive role.
A better approach starts with multiconfiguration, Napier AI stated. By tailoring screening to specific risks, firms can cut false positives at the source. That means selecting appropriate watchlists for each customer group, refining lists to remove weak aliases, applying delta screening only when data changes, and setting thresholds by risk profile. Filters can also exclude irrelevant matches, aligning alerts more closely with policy.
This risk-based strategy can reduce false positives by up to 90% before AI is introduced. Once the noise is minimised, AI can deliver its true value—optimising fuzzy matching, prioritising alerts, supporting analysts with validation, and uncovering hidden typologies.
The lesson is clear: AML transformation begins with configuration. Smarter rules and cleaner data provide the solid foundation AI needs to enhance insight and efficiency, rather than simply patching inefficiencies.
For more, read the full story here.
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