Frequent false positives dilute the company's effectiveness in its fight AML-CFT.
Although it is not possible to eliminate them completely, the detection and reduction of false positives represents a major technical and organizational challenge for companies. Beyond compliance and regulatory constraints, an increase in the frequency of these anomalies has negative consequences both in terms of corporate criminal liability, with fines, convictions and sanctions in the event of non-compliance, and in terms of image and reputation, with customers, suppliers and other third parties.
An alert does not necessarily mean that a money-laundering operation is underway. The higher the volume of a company's data, the greater the probability of false positives. For companies, the challenge is clear but complex: to detect and reduce false positives without compromising the efficiency and accuracy of their efforts AML-CFT or altering their commercial relationships.
False positive definition
As part of the fight against money laundering and the financing of terrorism (AML-CFT), companies must monitor their customers' activities using automated systems for filtering people and transactions. These systems generate alerts concerning suspicious individuals or financial movements. Compliance officers within the company are responsible for validating or rejecting these alerts. Confirmed suspicious cases are grouped together in the form of "suspicious transaction reports", which must be forwarded to the Treasury Department or Tracfin, the financial intelligence unit. The regulations require fuzzy logic detection, i.e. detection with tolerances for spelling, graphical, phonetic approximations, etc. This approach leads to the generation of a large number of data sets. This approach leads to the generation of numerous "false positives". A false positive is therefore an alert wrongly considered as positive or confirmed by AML-CFT solutions.
What the law says
The law requires companies to assess their customer risks and deploy filtering and detection tools, solutions and measures to be alerted when their customers engage in operations, activities or transactions linked, associated or assimilated to money laundering.
These tools, increasingly based on AI, must be implemented in compliance with GDPR as the CNIL reminded us in 2017. This concern on the part of the regulator is all the greater given that the development of AI could threaten the confidentiality of customer data. Thus, the ACPR and Tracfin's AML-CFT intelligence service are working on future guidelines in this area, while the European Parliament's AI Act, due to come into force in 2026, classifies the different types of AI according to their degree of criticality: minimal, high, high risk and unacceptable risk. In the case of generative AI, this is a two-speed approach, with transparency and information rules applying to all companies, and stricter constraints for the most powerful systems or those identified as being at risk, such as the banking and insurance sectors.
Companies therefore have an obligation to ensure the reliability and transparency of AI systems: they must be able to explain the decisions and actions taken by their deployed solutions.
How can you reduce false positives?
Structuring data and volumes
The fight against financial crime requires the collection of large volumes of heterogeneous or unstructured data from a multitude of sources and origins. This confusion increases the risk of false positives remaining indistinguishable from genuine AML-CFT signals.
A company must therefore pay particular attention to its data collection, classification and structuring processes. It must organize or list them according to different criteria. For example, each customer can be organized according to surname, first name, function or profession, etc., just as a transaction model can be defined according to frequency, amount, reason, recipient...
This initial stage requires a high level of involvement from compliance teams to avoid hampering or complicating their efforts to identify, detect and monitor customers and transactions.
Specify data and their relevance
Data structuring is necessary, but insufficient, both to reduce false positives and to determine a customer's precise profile and risk score. This requires continuous updating of data, particularly when a customer's residence changes, or when a risky or sanctioned country enters the relationship, so that filters can isolate and identify unknowns and inconsistencies.
Continuous monitoring of data and solutions AML-CFT
Les solutions, outils et méthodes mis en place pour la LCB-FT doivent évoluer constamment ne serait-ce que pour rester efficientes par rapport à la criminalité et aux fraudes ou encore pour tenir compte des évolutions des règlementations nationales et internationales. Les entreprises doivent ainsi procéder à un examen continu de leurs mesures et outils de filtrage-détection LCB-FT pour s’assurer qu’ils ne deviennent pas obsolètes. Ceci passe par l’ajout ou la suppression de certains programmes ou briques afin de réduire de manière plus fiable et contrôlée le nombre de faux positifs, voire réduire et optimiser la charge de travail des services de conformité.
Reducing false positives improves operational efficiency
A high false positive rate creates a significant workload for compliance teams and departments, and reduces the effectiveness of measures AML-CFT. An automated RegTech solution can effectively validate the KYC / KYS customer vigilance and transaction monitoring measures imposed on companies, thus reducing the number of unfounded alerts and enabling analysts to focus on cases of real concern.
The contribution of latest-generation RegTech
AP Solutions IO s’appuie sur l’Intelligence augmentée pour développer des outils LCB-FT éprouvés grâce à une détection et à une réduction performante mais parfaitement traçable & explicable. Basées sur de puissants algorithmes, ces solutions SaaS/API permettent de détecter les transactions ou personnes sensibles et d’éliminer les « faux positifs » de manière automatique jusqu’à 98%, tout en renforçant la pertinence des déclarations avec les régulateurs en leur donnant les moyens de démontrer les raisons d’une élimination automatique d’une suspicion en mode glass box. Avec un abonnement full service, les barrières à l’entrée chutent (coûts d’installation et d’exploitation) grâce à une intégration à tous les fichiers clients et au système d’information CRM, ERP… des sociétés et de leurs filiales.
