Contents
False positives: an underestimated phenomenon
Why so many false positives?
The difficult task of filtering names and context
Consequences that should not be underestimated!
Explainable AI: essential for combating false positives
Concrete benefits
Prioritizing transparent solutions
The technologies used in the fight against money laundering and terrorist financing have become considerably more effective. Automation and analysis algorithms, now at the heart of modern systems, enable more detailed and faster processing of transactions. This increase in power clearly reduces risks, but it comes with a major side effect: an increase in false positives, which still weighs heavily on compliance teams and the operational quality of controls.
False positives: an underestimated phenomenon
False positives are generated when a customer profile or legitimate transaction is interpreted as suspicious by compliance software, triggering an alert. While the generation of thesefalse positivesis a sign that the algorithm is working, they should not reach significant proportions. This phenomenon generates additional management costs, reduces the effectiveness of AML-CFT measures AML-CFT creates reputational risk. Unfortunately, there are no reliable sources on the proportion of false positives associated with AML-CFT measures. However, estimates suggest that they account for more than 40% of alerts, and even more than 90% for US banks, according to consulting firm McKinsey.
Why are there so many false positives?
Several factors explain the high prevalence of false positives:
Are traditional systems too rigid?
Traditional AML-CFT systems AML-CFT on algorithms based on pre-established rules. If these rules are too general, sensitive, or insufficiently developed (because they were developed too quickly and poorly tested, etc.), they may flag perfectly legitimate transactions that meet their criteria for potential fraud. For example, a customer making several cash withdrawals on the same day may trigger an alert because these transactions are considered unusual, even though they are perfectly legitimate. This is especially true since fixed rules can be easily circumvented, as criminals adapt their tactics to evade known rules.
A data quality issue
False positives are often generated due to inaccurate, incomplete, or outdated data. There are several reasons for this:
- Unstructured and voluminous data: AML-CFT the analysis of huge volumes of unstructured data from external sources (media, public/private documents). This confusion increases the risk of not distinguishing between false positives and genuine signals that need to be addressed.
- Redundant data: Redundant or duplicated data is a common cause of false alerts, often generating random name matches.
- Outdated data: The inability to continuously monitor and update data creates the risk of using outdated information, triggering false positives.
The difficult task of filtering names and context
Customer screening, particularly for sanctions lists or Politically Exposed Persons (PEP), is a major source of false positives due to challenges related to names. Regulations require detection using fuzzy logic, with tolerances for spelling or phonetic approximations. However, if these algorithms are too sensitive, they generate false positives due to common names, similar spellings, aliases, or naming conventions around the world (e.g., non-Latin scripts or similar Arabic names). In addition, systems or analysts may lack the context necessary to accurately assess the transaction. The lack of relevant contextual information (date of birth, nationality, gender) forces them to set lower matching thresholds, which increases the number of false positives.
Consequences that should not be underestimated!
Additional costs in AML-CFT management
The process of reviewing each case to meet compliance obligations is costly and time-consuming, as it requires human resources to verify the nature of transactions and identify whether they are truly likely to present a risk. Furthermore, since most of the qualification work is done by employees who may be under pressure due to the enormous volumes of data to be processed, this creates delays in the processing of alerts and can generate a large backlog of unprocessed alerts.
A decline in the effectiveness of control processes
False positives have repercussions that go far beyond the direct costs of the resources that need to be mobilized. They slow down the responsiveness that is essential in the fight against money laundering, and therefore the efficiency of processes and technological solutions. And the greater the volume of data to be processed, the more significant this deterioration becomes!
A change in the customer experience
The proliferation of false positives damages reputation and customer experience. When a legitimate transaction or customer is incorrectly flagged, it can result in the transaction being blocked, causing frustration for the customers involved, who become less loyal.
Finally, false positives undermine regulatory compliance, as they reveal relatively ineffective filtering processes.
Explainable AI: a must to avoid false positives
Given the relative ineffectiveness of traditional systems and the burden (human, financial, organizational) of false positives, AI and machine learning are viable solutions for improving AML-CFT. However, their adoption requires one essential condition: explainability.
Accordingto the CNIL(Commission Nationale de l’Informatique et des Libertés), explainability is defined as "the ability to connect and make understandable the elements taken into account by the AI system to produce a result. These may include, for example, input variables and their impact on the prediction of a score, and thus on the decision."
Thelack of explainabilityof machine learning algorithms can be a major obstacle to their adoption. In France, in 2025, the APCR began discussions on this subject, particularly on AI auditing, within the framework of the European AI Act, which will come into force in2026.
Explainable AI addresses issues of trust and legitimacy. The opacity (black box effect) of traditional AI systems is hardly tolerable in highly regulated areas where heavy penalties apply in the event of non-compliance. Companies have an obligation to ensure the reliability and transparency of AI systems; they must be able to explain the decisions and actions taken by the AML-CFT solutions AML-CFT .
The main advantage of explainable AI is that it allows consistency to be verified and ultimate responsibility for a decision to be assumed. It also gives regulators the means to understand why an alert has been classified in a certain way.
Tangible benefits
In practical terms, integrating explainable AI helps financial institutions achieve their compliance objectives and optimize operational efficiency by reducing the burden of false alerts.
More detection, fewer false positives
Explainable AI streamlines alert processing by focusing on true signals while reducing false positives. It improves accuracy through contextual and semantic analysis, while ensuring complete transparency in decision-making. Every step is traceable and justifiable, which builds trust among compliance teams and regulators.
Rather than relying on opaque models, the explainable approach favors intelligent rules and auditable algorithms. These systems identify inconsistencies, eliminate duplicates, and prioritize alerts according to clear criteria validated by internal governance. This "glass box" logic ensures that every decision can be explained, documented, and controlled.
In practice, this means less false positives, better risk prioritization, and enhanced compliance, without compromising transparency or regulatory accountability.
AI, recommended for productivity and operational efficiency
Reducing the volume of false alerts frees up compliance team resources and speeds up business processes and workflow efficiency related to transaction monitoring. Similarly, AI accelerates onboarding processes (KYC) by automating identity verification and risk assessment, while ensuring continuous customer monitoring.
Other benefits of AI include assistance with incident reporting, reporting accuracy, and data structuring, especially for unstructured data. According to McKinsey, AI can help banks improve their identification of suspicious activity by up to 40% while significantly reducing the number of false positives.
Enhanced compliance and governance
Explainable AI is essential for the governance of AML-CFT systems AML-CFT for ensuring that the companies concerned meet ever-changing regulatory requirements. Systems based on explainable AI generate audit trails that demonstrate how AML decisions were made. For explainable solutions, the automatic elimination of a suspicion can be demonstrated to regulators in "glass box" mode (transparency). Similarly, AI enables continuous monitoring of information sources (sanctions lists, PEP, adverse media) to ensure that the data used is always up to date, reducing false positives caused by outdated information.
However, it is important to note that implementing AI requires rigorous governance, including verification and recording of parameters, continuous auditing of explanations to avoid "illusions of transparency," and training analysts in decision-making in a semi-automated environment.
When combined with explainability, artificial intelligence therefore enables financial institutions to break free from the traditional dilemma that forced them to choose between strict compliance (and costly false positives) and operational efficiency.
Prioritize transparent solutions
To meet these needs for transparency and trust, AP Solutions IO relies on augmented intelligence for its AML-CFT tools, thanks totraceable and explainable detection. Based on powerful algorithms, these solutions, available in SaaS/API modes, enable the detection of sensitive transactions or individuals and the automatic elimination of up to 98% of false positives, while enhancing the relevance of reports to regulators. Explainable AI tools are thus becoming the cornerstone of a model in which technology supports human actions (still essential in AML-CFT) to focus efforts where the threat is real. It shifts transaction monitoring from an essentially administrative exercise to a strategic lever in the fight against financial crime.
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