A screening system that generates a very large number of alerts is not necessarily more prudent. When teams spend most of their time filtering out irrelevant matches, the quality of the analysis can decline and processing times can increase.
Reducing false positives in screening is therefore both an operational and a methodological challenge. The goal is not simply to generate fewer alerts; rather, it is to improve their relevance without increasing the risk of missing a genuine match.
Several approaches can be combined: improving the quality of input data, adjusting matching thresholds, using multi-criteria fuzzy logic, segmenting populations, and carefully leveraginghistorical decision data.
It is precisely this balance that we are working on with AP Solutions IO. Our approach focuses on improving the quality of detection and configuration rather than mechanically seeking to reduce the number of alerts.
Why can too many alerts undermine a screening system?
An alert must be analyzed, documented, and then, depending on the outcome, resolved or investigated further. As the volume increases, the workload on compliance teams grows.
The problem arises when the volume of data generated by the engine is no longer consistent with the resources actually available to process it.
The volume of alerts has a direct impact on the operational workload
Each match requires checking various details: name, alias, date of birth, country, available identifiers, or other relevant data, depending on the type of verification.
Since many alerts are simply the result of namesakes or overly broad matches, analysts spend a significant portion of their time filtering out the noise.
The consequences extend beyond the compliance team alone: delays inestablishing client relationships, longer processing times for files, or slowdowns in certain workflows.
For AP Solutions IO, reducing false positives must therefore be approached as a detection quality issue. Our analysis focusing on the causes of false positives and ways to reduce them discusses this issue in greater detail.
Reducing the volume isn't enough
An engine that triggers fewer alerts isn't necessarily better. A setting that's too restrictive can also result in more false negatives—that is, it may miss a match that should have been detected.
The right metric, therefore, is not just the number of alerts generated, but the system’s ability to maintain an appropriate level of detection while minimizing irrelevant matches.
Where do false positives come from in a screening test?
They may stem from the engine itself, its settings, or the data transmitted to it.
Common causes include homonyms, name variants, transliterations, incomplete data, or the application of the same parameters to very different populations.
Homonyms, Aliases, and Transliterations
Transliteration involves transcribing a name from one writing system to another. As a result, the same Arabic, Chinese, or Cyrillic name can appear in several different Latin spellings.
The engine must be flexible enough to detect these variations without treating every similarity as a relevant match.
The opposite phenomenon occurs with very common names. A search based solely on a last name can yield so many results that it becomes difficult to distinguish between them.
Insufficiently discriminating input data
The quality of the input data is one of the key factors in reducing false positives.
If the search engine only has a name, it has very little information to distinguish between two people. A date of birth, a country, a full first name, or an additional identifier, on the other hand, can greatly refine the search results.
The problem, then, does not necessarily lie with the screening software: it may lie with the upstream information system or with the quality of the information collected during the KYC process.
The same settings for all populations
An individual, a legal entity, and a counterparty listed in a payment message do not have the same data or the same reconciliation requirements.
Applying the same rules to all populations can therefore yield results that are not very relevant.
With AP Scan, our screening solution for individuals in sensitive positions, AP Solutions IO allows you to configure checks and scopes according to your organization’s needs. The engine is based on fuzzy logic combined with more than 90 configuration criteria, ensuring that the decision is not reduced to a simple exact match between two names.
How can you reduce false positives in a screening engine?
There is no one-size-fits-all approach that works for all organizations. Noise reduction generally relies on several complementary strategies.
| Approach | Principle | Target Profit | Point to Watch For |
| Improve the input data | Add dates, countries, identifiers, and other criteria | Better Distinguishing Between Homonyms | It depends on the quality of the information system and the available data |
| Adjust the thresholds | Change the thresholds that trigger an alert | Exclude certain matches that are too weak | Test the effect on detection capability |
| Use multi-criteria logic | Combining and Weighting Multiple Pieces of Information | Improve the accuracy of the match | Configuration to be documented |
| Segmenting Populations | Adapt the rules to the type of data or risk | Avoid a uniform setting | Segmentation to be justified |
| Leveraging Historical Data | Take into account previously documented decisions | Limit certain repetitive analyses | Reevaluate if the data or reference standards change |
First, enrich the input data
Before adjusting the thresholds, it is helpful to check whether the engine has enough information to distinguish between matches.
A date of birth, nationality, legal status, or identifier can turn a hard-to-classify alert into a much more precise match.
Improving the data is often preferable to artificially making the engine less sensitive.
Adjusting Thresholds Methodically
Raising a match threshold can immediately reduce the number of alerts, but this action should not be taken in isolation.
The more restrictive a threshold is, the greater the risk that certain orthographic or phonetic variations will no longer trigger a check. The impact must therefore be tested on representative cases and then documented.
For AP Solutions IO, the effectiveness of the configuration cannot be assessed solely based on the volume of alerts generated. We seek to measure both the reduction in noise and the maintenance of detection capabilities simultaneously .
Weighing multiple criteria rather than a single score
A multi-criteria fuzzy logic approach makes it possible to examine several attributes simultaneously and adjust their weights.
The similarity between two names can, for example, be clarified by the date of birth, country, or other distinguishing information.
This level of granularity is at the heart of the engine developed by AP Solutions IO. More than 90 criteria can be used to tailor detection to the compliance policy and the characteristics of the data being processed.
For payment flows, AP Filter allows you to filter for international sanctions and embargoes, by taking into account various types of data such as names, countries, currencies, BICs, or IBANs.
Leverage historical data without locking in decisions
A piece of correspondence that has already been analyzed and set aside can be useful information when it resurfaces.
However, this should not lead to automatically ignoring all future alerts involving the same person. Reference data, customer information, or the context may change.
The history is therefore useful when it is combined with specific rules for reassessment and full traceability of previous decisions.
What Must Be Preserved When Reducing False Positives
An effective configuration must be documented, testable, and explainable.
The challenge is not only to determine which parameter works today, but also to be able to understand later why it was chosen and what effect it has.
Document changes to the settings
For each significant change, it is helpful to keep the following, in particular:
- the rule or the modified value;
- the date of the change;
- its reason;
- the identity or position of the person who approved the change;
- the results of the tests conducted.
This documentation helps maintain a proper audit trail and facilitates future reviews.
Track decisions made in response to alerts
The same logic applies to the alerts themselves.
A decision must be linked to the information reviewed and the rationale provided. Without this record, the organization risks repeating the same investigations or being unable to reconstruct the reasoning behind the decision.
AP Solutions IO's tools log the actions performed by users and the system to ensure this traceability. Our Glass Box approach is based precisely on the ability to understand and justify the rules that led to the result.
What role does the engine's explainability play?
Explainability makes it possible to move from an engine that simply delivers a result to a system whose operation can be analyzed.
This distinction becomes particularly important when a complex configuration is used to reduce false positives.
Understanding Why an Alert Appears
For a given match, the compliance team must be able to identify the data taken into account and the criteria that influenced the result.
This is the principle behind AP Solutions IO’s“Glass Box” Augmented Intelligence: the technology supports the analysis, but the rules and results remain traceable and explainable.
Our analysis of the explainability and auditability of AML systems delves deeper into this issue of governance in particular.
The goal is not to take decision-making away from compliance teams. On the contrary, we place people at the center of the system and use technology to support them, in line with our vision of Augmented Intelligence.
What metrics should be tracked to manage false positives?
The reduction in false positives must be measured before and after each significant change to the settings.
Several indicators can be used to assess the resulting effect:
- the number of alerts relative to the volume screened;
- the proportion of alerts that were ultimately dismissed;
- the average processing time;
- the recurrence of the same comparisons;
- the breakdown of alerts by population or data type.
It is best to establish a baseline before making any changes. Without a point of comparison, a decrease in the number of alerts may be observed, but it becomes much more difficult to pinpoint the exact cause.
The decline in the number of alerts should by no means be viewed, on its own, as evidence that the system has improved. It is also necessary to verify that detection capabilities remain consistent with the scope and risks covered.
How does AP Solutions IO reduce false positives in screening?
AP Solutions IO is a French RegTech company whose founders have more than fifteen years of experience in compliance, filtering, and screening tools.
Our suite combines AP Scan, AP Scoring, AP Monitoring, and AP Filter, with solutions available as SaaS and via API, and data hosted in France.
To reduce noise, our detection and reduction engine features more than 90 configuration criteria. Depending on the configuration and use case, this technology can reduce false positives by up to 98%.
This figure does not represent a universal configuration. The result depends on data quality, the reference frameworks used, the scope of the screening, and the rules specific to each organization. That is why a configuration must be tested on representative cases rather than simply applied from a standard environment.
Our Glass Box approach complements this work by ensuring that results are traceable and explainable. For compliance teams, the goal remains the same: to reduce the time spent on noise without losing the ability to understand, control, and justify detections.
To assess the impact of these settings on your own portfolio, talk to our team about your trading volumes and screening rules allows you to base your analysis on your actual use cases.
