{"id":5931,"date":"2026-06-17T09:11:52","date_gmt":"2026-06-17T16:11:52","guid":{"rendered":"https:\/\/quienesquien.mx\/?p=5931"},"modified":"2026-06-24T11:48:55","modified_gmt":"2026-06-24T18:48:55","slug":"como-reducir-falsos-positivos-en-monitoreo-de-pld-y-conoce-a-tu-cliente-reglas-procesos-y-metricas","status":"publish","type":"post","link":"https:\/\/quienesquien.mx\/en\/como-reducir-falsos-positivos-en-monitoreo-de-pld-y-conoce-a-tu-cliente-reglas-procesos-y-metricas\/","title":{"rendered":"How to reduce false positives in AML monitoring and Know Your Customer: rules, processes and metrics."},"content":{"rendered":"
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False positives have a very particular way of wearing down teams: they don't fail spectacularly, fail <\/strong><\/span>in volume<\/span><\/strong>. A coincidence that wasn't a coincidence; an alert that repeats with every update; a common name that triggers ten cases in a single shift. Over time, the cost isn't just operational (hours, backlog, rework): it's also a matter of consistency. Two analysts can resolve the same case differently if the criteria aren't well-defined.<\/span><\/p>

Reduce false positives It does not mean loosening controls<\/strong>. Means <\/span>improve data quality<\/span>, <\/span>improve matching<\/span> y <\/span>standardize the process<\/span> so that the decision is defensible. This approach is consistent with the international principle of proportionality to risk (Risk-Based Approach) promoted by the standard of <\/span>FATF<\/a><\/strong>.<\/span><\/p>

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Quick summary<\/h3>

A false positive is an alert that appears to be a coincidence, but isn't. It almost always stems from incomplete (or poorly normalized) data and overly "blind" matching rules. You reduce it with three levers: data (better information), rules (better matching logic), and process (better resolution). And you manage it with metrics: if you don't measure noise and time, the backlog returns.<\/p><\/div>

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It starts where it hurts the most: the data (not the engine)<\/span><\/strong><\/h2>

When the volume of alerts increases, it's tempting to blame the tool being used. But in most cases, the problem lies in the incoming data: <\/span>Incomplete capture, different formats, and fields that do not help to differentiate people<\/span>. In individuals, classic triggers include common names, compound surnames, aliases, changes in order (paternal\/maternal), missing birth dates, and blank nationality or country fields. In legal entities, friction usually arises from abbreviated company names., acronym<\/strong>, \u201cSA de CV\u201d in a thousand variations, or corporate groups where several companies share part of the name.<\/span><\/p>

Here's an idea that will save you a lot of frustration: screening rarely fails because no information was found; it fails because <\/span>he could not rule it out<\/span>. Therefore, the most profitable operational question is not "how do I lower alerts?", but "what data am I missing to safely rule it out?". <\/span>OFAC<\/a><\/strong> He expresses this practically when he talks about false positives: <\/span>Don't get stuck on the name<\/span>, compares against descriptors (date of birth, nationality, identifiers, etc.) to confirm or rule out.<\/span><\/p>

In the financial sector, this typically translates into two quick improvements: defining a minimum KYC capture standard to reduce ambiguity and, secondly, reinforcing data quality at the first point of contact (when corrections are still inexpensive). The same applies, without using those acronyms, if you're validating a critical supplier in Purchasing or a sensitive candidate in HR: <\/span>If the data doesn't distinguish, neither will the engine.<\/span><\/strong>.<\/span><\/p>

Rules that do reduce false positives without lowering the standard<\/span><\/strong><\/h2>

The goal is not to make it less sensitive. It's to make it sensitive. <\/span>smarter<\/span>The engine should be demanding where it needs to be and flexible where the data reality demands it. Best practice guides on sanctions screening also recognize this tension: you need effective controls, but with processes and calibration that make the operation viable.<\/span><\/p>

If you want to keep three practical rules (without turning this into a manual), they are these:<\/span><\/p>