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“We’ll fix it with AI.”
Sound familiar?
In just a few years, AI has gone from an interesting tool to the go-to answer for nearly every challenge in financial crime compliance. Alert volumes too high? Screening logic too rigid? Teams overwhelmed with false positives? AI is often seen as the silver bullet.
But here’s the catch: if your screening setup is not working in the first place, AI will not magically fix it. In fact, layering AI on top of a noisy, misconfigured system often just adds complexity without delivering the results you actually need.
So instead of asking how to apply AI, maybe we should ask when.
Most teams apply AI too late in the process
What we see time and again is that AI is being used after alerts are generated. The alerts come in, the false positives pile up, and AI is introduced as a smart discounting layer that decides which alerts matter.
This is a reactive fix. A way to triage the outcome of a flawed process. And while it might reduce some of the noise, it does not address the root cause.
Why generate all those alerts in the first place?
One-size-fits-all logic creates unnecessary noise
Too many organisations are still running screening configurations that are overly broad and not aligned to actual risk. Everyone is screened against everything. Unchanged data is rescreened repeatedly. Matching thresholds are blunt, and filtering is minimal.
The result? Huge volumes of irrelevant alerts and pressure on compliance analysts and AI to clean them up.
The smarter approach is to stop the noise before it starts.
Reduce alerts at the source with multiconfiguration
Multiconfiguration is not just about tuning thresholds, it’s about rethinking how screening is applied in the first place. Most systems today still rely on a one-size-fits-all model: everyone gets screened the same way, against the same lists, using the same rules. This leads to too many false positives, not enough precision, and overworked teams trying to keep up.
A smarter approach starts with data management and targeted configuration. That means:
- Choosing which watchlists to screen against for each population (e.g. sanctions vs PEPs)
- Cleaning and refining those lists – like stripping out weak aliases that drive noise
- Screening only when data changes (delta screening) or when changes are material to risk
- Applying tailored thresholds and scoring for different segments (e.g. high-risk geographies, corporate clients, retail clients)
- Using filters to include or exclude associates based on your risk appetite
Taken together, this layered approach allows organisations to reduce false positives by 80–90% before AI even gets involved. It’s a way to align alerts with actual policy and risk, not just generate them because the system is set too broadly.
Once you’ve quieted the noise, that’s when AI can truly shine.
AI still matters, but only when built on solid foundations
None of this is to say that AI does not have a role to play. In fact, when used correctly, it can be incredibly powerful. But its true value emerges when it is working with a cleaner, more manageable alert set.
This approach also supports a compliance-first posture. When screening decisions are explainable, audit-ready, and grounded in tailored risk logic, it becomes easier to meet regulatory expectations and demonstrate control over your financial crime defences.
AI can help by:
- Optimising fuzzy matching sensitivity per risk segment
- Prioritising alerts based on scoring models
- Supporting analyst decisions with validation and second-opinion logic
- Uncovering typologies and patterns that rules might miss
- Learning from historical decisions to improve future outcomes
But if your system is generating noise, AI will not save you.
It’s time to rethink your screening foundations
If your team is spending more time reviewing alerts than refining configurations, it is worth taking a step back. Are you optimising your screening engine, or just adding more layers on top of it?
Real transformation in AML does not start with AI. It starts with configuration.
Smarter rules. More precise filtering. Cleaner data.
That is what allows AI to shine. Not as a fix for inefficiency, but as an enhancer of insight and speed.
Explore how our NextGen screening solution helps you stop alerts before they start.
Photo by Agence Olloweb on Unsplash
