The AML screening determines the strength of your AML-CFT compliance. When a system generates too many irrelevant alerts, analysts spend less time on truly sensitive cases. The audit trail then becomes more difficult to justify during an audit, an internal review, or a discussion with the regulator.
At AP Solutions IO, we have developed AP-Scan to address this operational pressure. The solution accurately filters customers, Beneficial Owners, counterparties and transactions, while reducing 98% of false positives. It is based on Glass Box Augmented Intelligence—explainable, traceable, and auditable—designed for compliance departments, MLROs, RCCI/RSCI and teams KYC.
This content builds on our analysis of false positives. It also follows on from the page dedicated to AP-Scan, our AML filtering integrated into the RegTech AP Solutions IO.
What is AML screening: customers, transactions, PEP sanctions
The AML screening includes checks designed to compare a person, a company, a transaction, or a counterparty against various watchlists and risk lists. It takes place at the start of the relationship and throughout the customer relationship lifecycle and as part of transaction monitoring.
In a system AML-CFT system, screening goes beyond a simple name search. It combines several complementary checks: name screening, transaction screening, PEP screening, sanctions screening, and KYB screening.
The name screening screens customers, prospects, Beneficial Owners, executives, and counterparties. The matches are checked against sanctions lists, PEP, adverse information from open sources and internal databases.
The transaction screening analyzes transactions and payment messages. It takes into account countries, intermediary banks, descriptions, beneficiaries, and atypical patterns.
The screening of PEP, often referred to as PEP screening in international settings, identifies politically exposed persons. It also covers their close associates, known associates, and entities that may be linked to them.
The screening of sanctions compares the data being checked against international sanctions lists, asset freeze measures, embargoes, and applicable sector-specific restrictions.
The KYB screening analyzes legal entities, their ownership structures, their Beneficial Owners the relationships between entities.
The goal is not merely to detect a match. Teams must assess the level of risk, document their decision, and explain why an alert was accepted, rejected, or escalated to a higher level of analysis. This traceability is an essential element during inspections by theACPRand in communications with Tracfin , and internalinternal audit.
The problem of false positives: cost, team overload, and operational risk
An anti-money laundering filter that is too broad quickly generates a volume of alerts that is difficult to manage. Spelling variations, transliterations, homonyms, duplicates, compound names, or incomplete data trigger matches that tie up analysts, even when the actual risk remains limited.
The first impact directly affects the cost of processing alerts. Every unnecessary alert requires reviewing documents, verifying the context, making a reasoned decision, and providing a documented justification. On a large scale, this volume reduces the system’s ability to handle sensitive cases with the expected level of analysis.
The second impact concerns the cognitive load on analysts. When they have to deal with a constant stream of low-priority alerts, the quality of their analysis suffers. This makes it harder to manage risk: a critical alert can get lost in the midst of excessive operational noise.
The third challenge lies in demonstrating proficiency with the system. In light of theACPR or other regulators, an effective system must explain its rules, parameters, thresholds, trade-offs, and decisions. A high volume of poorly classified alerts does not demonstrate the system’s vigilance. On the contrary, it may reveal insufficient control over the system’s configuration.
That is why AP-Scan prioritizes signal quality over the accumulation of alerts. We favor more accurate, contextualized, and explainable detection, so that human effort can be focused on cases that require further analysis.
Fuzzy matching, phonetic matching, and contextual scoring: useful approaches
An effective AML relies on several complementary techniques. Their effectiveness depends as much on the quality of the data as on the parameters used and the ability to interpret each result.
The fuzzy matching identifies matches despite spelling differences. It detects letter transpositions, missing accents, abbreviations, or transliteration variations. This technique is particularly useful when customer databases span multiple languages.
The phonetic matching analyzes phonetic similarities. It helps detect names that are spelled differently but pronounced similarly. This capability is particularly useful in cross-border payments, international environments, and customer databases that use multiple alphabets.
The contextual scoring provides a more nuanced assessment of risk. Instead of relying solely on the similarity of a name, it cross-references available information: date of birth, nationality, country of residence, role within the company, type of transaction, amount, frequency, channel, beneficiary, and relationship history.
At AP Solutions IO, we integrate these methods into a SaaS architecture that is configurable and interoperable via API. AP-Scan is based on more than 90 criteria to refine filtering, prioritize alerts, and improve the quality of decisions.
IA Glass Box: Explainability as a Compliance Requirement
AIAI applied to AML screening can improve the accuracy of controls. However, this accuracy is only valuable if the reasoning remains understandable, documented, and verifiable.
An opaque decision, resulting from a model Black Boxmodel, becomes difficult to defend when a controller asks why an alert was generated, deprioritized, or dismissed. When it comes to AML-CFT AML-CFT, technical effectiveness must always be explainable.
Our position is clear: AI used for compliance purposes should enhance human judgment, not replace it. Our Glass Box Augmented Intelligence addresses this challenge precisely. It makes visible the criteria used, the weightings, the comparisons, the thresholds, and the factors that led to the decision.
This approach is in line with the growing expectations of supervisory authorities. TheACPR has identified specific use cases for AI in the area of AML-CFT, particularly regarding KYC and transaction monitoring. The European regulation on artificial intelligence, often referred to asAI Act, also strengthens transparency requirements applicable to certain AI systems. Its requirements are being phased in, notably starting on August 2, 2026.
For your teams, the value is very tangible: analysts understand why an alert was generated or prioritized. The compliance officers can document the system with greater precision. Auditors have clear information to understand the reasoning behind decisions and assess the system’s effectiveness. Your organization thus gains greater traceability, control, and credibility.
Reduce false positives by 98% with AP-Scan
AP-Scan was designed for institutions seeking to strengthen their AML-CFT compliance without increasing the analytical workload. The solution covers AML filtering, KYC, KYB, KYT, PEP, sanctions, adverse information from open sources, and counterparty monitoring.
What sets us apart comes down to specific choices: more than 15 years of AML expertise, Glass Box, and an architecture that is interoperable via API and hosting in France. This hosting meets requirements for sovereignty, security, and compliance with the GDPR.
Our solutions are updated every four months to keep pace with changes in risks, lists, and regulatory requirements.
AP-Scan integrates with your information system via API, with no-code and multilingual support. You can use it from the moment you establish a customer relationship, during KYC remediation, in periodic follow-ups, or for transaction monitoring. The solution integrates with AP-Scoring, AP-Monitoring and AP-Filter to integrate filtering, risk scoring, transaction monitoring, and continuous control.
For your teams, the operational benefit is immediate: fewer false alerts, more time spent on high-value-added analysis, and more robust documentation of decisions. Compliance becomes easier to verify during audits, without compromising the rigor of risk assessment.
To evaluate AP-Scan in your environment, you can request a demo focused on your volumes, the lists used, internal rules, and priorities for reducing false positives.
FAQ — AML Screening and AML-CFT Screening
What is the difference between AML screening and transaction monitoring?
The AML screening identifies and classifies matches with risk lists : sanctions, PEP, adverse information, Beneficial Owners counterparties.
The transaction monitoring analyzes transaction patterns: frequency, amount, country, channel, beneficiary, and deviations from normal activity. The two systems complement each other and must share the same traceability requirements.
Why are false positives so high in AML screening?
The false positives are often the result of overly broad search parameters, incomplete customer data, namesakes, transliterations, heterogeneous lists, or a lack of context.
AP-Scan reduces this noise through intelligent matching, configurable criteria, and contextual scoring. The goal is to maintain vigilance while focusing human analysis on alerts that are truly significant.
Can AI be used for anti-money laundering screening?
Yes, provided that its operation remains transparent, well-documented, and subject to oversight by compliance teams.
At AP Solutions IO, we reject opaque models that are difficult to justify during an audit or inspection. Our Glass Box AI provides clear insights to understand, trace, and defend the decisions made in the context of anti-money laundering screening.
Is AP-Scan suitable for large enterprises and ME
Yes. Our RegTech was designed for large enterprises, ME and regulated organizations that handle large volumes of clients, counterparties, or transactions.
Architecture SaaS, France, regular updates, and over 90 configurable criteria allow you to tailor the filtering to your risks, processes, and control requirements.
Filter better, make more confident decisions
The AML screening creates value when it genuinely improves the quality of detection, the prioritization of alerts, and the justification of decisions. Massive, opaque, or poorly configured filtering burdens compliance processes without strengthening risk management.
On the contrary, explainable, contextualized, and traceable screening strengthens your system AML-CFTsystem. It allows you to better classify alerts, document decision-making processes, and demonstrate the consistency of decisions to regulators.
At AP Solutions IO, we have developed AP-Scan to meet this requirement. The solution reduces false positives, speeds up alert qualification, and makes decisions easier to document, explain, and defend to regulators.
Our Approach Glass Box offers a solid alternative to legacy solutions, which are often inflexible, and to newer tools that are still difficult to justify during an audit.
Request a demo AP-Scan to evaluate the reduction in false positives in your own AML screening.
