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ACTICO Payment Screening

Detect sanctioned payments with precision. Stop them within milliseconds. Machine learning enables fuzzy matching, improves match quality and reduces false positives.

Real-time payment screening: Precise, transparent, full audit trail

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As a proven software solution, ACTICO Payment Screening automates the entire screening process – from payment data analysis and sanctions list screening to case management. **ISO 20022 compliant.**

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ACTICO Payment Screening can be tailored to customer-specific requirements. Additional checks, such as internal screening lists, individual configurations and machine learning, help optimise screening results.

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Business rules and AI provide the decision logic behind ACTICO Payment Screening. Adjustments can be made at any time, for example when regulatory requirements change.

Automated sanctions list checks in payment transactions

ACTICO Payment Screening is an integral part of the ACTICO Compliance Solutions. It checks incoming and outgoing payments before they are posted, comparing payment data against relevant sanctions lists. Suspicious transactions can be stopped and reviewed in time.

Machine learning enhances fuzzy matching, helping to identify less obvious matches while balancing screening accuracy, risk and operational effort.

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Identify risky payments and make transparent, traceable decisions.

Ex-Ante screening

Payment data is automatically screened against sanctions lists before execution. Payments with no matches are released automatically. Suspicious payments are held for review and either released or stopped after clarification.

Fuzzy matching

Typos, scanning errors and alternative spellings can cause names and terms to differ from sanctions list entries. Fuzzy matching helps identify these variations, while machine learning reduces false positives and keeps the need for manual review to a minimum.

Fast clarifications

Clear visual comparisons help users assess suspicious payments at a glance and resolve alerts quickly. Configurable workflows also support efficient review of high-risk transactions using the four-eyes principle.

Instant Payment decisioning

ACTICO Payment Screening checks payment data against sanctions lists, countries and keywords. For instant payments, screening results are available within 500 milliseconds to one second. If a match is detected, the payment is stopped and clarification takes place outside the instant payment process.

White List / Exceptions

Exceptions can be defined for selected cases where no match should be generated. This helps prevent unnecessary alerts for recurring or known payments.

External sanctions lists

ACTICO Payment Screening supports widely used sanctions and watchlist data from sources including the EU, OFAC, LSEG (World-Check), Dow Jones, LexisNexis and Info4C.

Internal sanctions lists

Company-specific lists of names, keywords, countries and bank data can be managed flexibly and included in automated payment screening.

Screening history

The screening history provides a complete record of all checked transactions and any exceptions identified during the process.

Instant Payments sanctions screening

With instant payments, every second counts. Transactions must be executed within 10 seconds, which is why ACTICO Payment Screening completes sanctions screening in under one second.

Under the Instant Payments Regulation (IP), PSPs must regularly screen their customer base against applicable sanctions lists instead of screening each individual instant payment against EU sanctions lists. However, AML and counter-terrorist financing (CTF) requirements still apply to instant payments.

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Make transaction screening more efficient

Its business rules approach provides a simple and flexible way to automate compliance decisions. Match generation can be configured to reflect different screening requirements and risk scenarios.

Supervisory authorities increasingly expect banks to use fuzzy matching rather than exact searches in payment screening. The aim is to detect potential matches even when names in payment data are misspelled, altered or deliberately disguised.

However, switching from exact to fuzzy matching often increases the number of false positives. This can place a significant burden on compliance teams and make manual review inefficient. Machine learning can help reduce this workload by improving match quality and prioritising relevant alerts.

Machine learning helps banks strike the right balance between screening effectiveness and operational efficiency. The goal is to detect as many relevant risks as possible, reduce false positives and ease the workload for compliance teams.

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Machine learning improves the matching of payment data against sanctions lists, helping to identify more true matches and reduce the risk of false negatives.

At the same time, it can reduce false positives by filtering out less relevant matches. This helps banks and financial institutions use compliance resources more efficiently and focus their attention on genuinely higher-risk cases.

Increasing regulatory pressure led VP Bank Group to modernise its payment screening processes. A system review showed that the existing solution no longer met current fuzzy matching requirements.

To identify potential risks more accurately without increasing workload and costs, the Liechtenstein-based bank implemented ACTICO Payment Screening. The integration of machine learning significantly improved fuzzy matching quality and reduced the workload for compliance and payments teams by 50%.
Read the success story:

For us, it was important that fuzzy matching would increase the quality of hits for incoming and outgoing payments without unnecessarily increasing the number of transactions that had to be checked. We have achieved this goal without overburdening our payments and compliance teams.

Fabian Wälte

Head of Payments and Transaction Services

Recognised throughout the industry.

For many years, ACTICO has been recognised by customers and analysts as a leading provider of Advanced Decision Automation Technology and solutions for Compliance and Credit Risk Management.