AI has quickly become the default response to almost every challenge in financial crime compliance.
Whether teams are facing overwhelming volumes of alerts, rigid screening rules, or analysts exhausted by false positives, the solution is often framed in the same way: “We’ll fix it with AI.” But relying on artificial intelligence without addressing the fundamentals of screening design risks compounding complexity rather than solving the real issue.
Napier AI, a next generation intelligent compliance platform, recently explored how firms can improve screening with AI.
The key question for many compliance teams should not be how to apply AI, but when. Too often, AI is deployed at the wrong stage in the process, it said. Many organisations introduce AI only after alerts have already been generated, using it as a discounting layer to separate signal from noise. While this approach can ease some of the burden on analysts, it does not fix the flawed process that created the noise in the first place.
A major source of this problem is the reliance on one-size-fits-all screening logic. Many systems still rescreen unchanged data repeatedly, apply broad thresholds, and screen everyone against the same lists without differentiation. This produces a flood of irrelevant alerts, leaving both compliance analysts and AI tools struggling to keep up. The smarter question is why those unnecessary alerts are being created at all.
One solution lies in adopting multiconfiguration approaches. Instead of treating every customer, geography, or transaction type identically, organisations can tailor screening based on actual risk. This involves choosing appropriate watchlists for specific populations, cleaning and refining those lists to remove weak aliases, and using delta screening so data is only checked when it changes in a material way. Risk-based thresholds and filters further refine the process, ensuring different segments such as corporate clients or high-risk regions are assessed appropriately.
When implemented effectively, this layered configuration approach can cut false positives by 80–90% before AI is even introduced. By removing unnecessary noise at the source, organisations create a cleaner dataset that enables AI to operate more effectively and deliver genuine value.
That does not mean AI lacks a role. On the contrary, when built on a foundation of precise rules and clean data, AI becomes a powerful enhancer. It can fine-tune fuzzy matching sensitivity by risk type, prioritise alerts with scoring models, support analysts with validation, and even uncover new typologies that static rules may miss. But AI alone cannot rescue a poorly configured system, Napier AI concluded.
For more, read the full story here.
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