What is transaction screening | Napier AI

Transaction screening is the process of analysing individual transactions for suspicious or prohibited activity before they are approved. This article explains how transaction screening works, its role in anti-money laundering (AML) and counter-terrorist financing (CFT), common red flags, how it differs from transaction monitoring, the steps involved, challenges firms face, and how to optimise it using advanced technologies like AI.

Transaction screening is a key component of AML/CFT controls. It involves checking transaction details in real time to detect illicit or high-risk activity suggesting potential financial crime. According to the Wolfsberg guidance on sanctions screening, ‘Transaction screening should be performed at a point in time where a transaction can be stopped and before a potential violation occurs’.  

The primary goal of transaction screening is to prevent prohibited transactions, such as those involving sanctioned individuals or entities, PEPs (politically exposed persons), or embargoed goods.  

Transaction screening is critical to:

• Ensuring compliance with international sanctions and regulatory obligations

• Supporting a risk-based AML/CFT approach

• Preventing the movement of funds linked to financial crime  

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Some key red flags that may trigger alerts during transaction screening include:

• Unusually large amounts inconsistent with a customer’s typical behaviour

• Transactions involving sanctioned or high-risk jurisdictions

• Payments to or from individuals/entities on sanctions, watchlists or PEP lists

• Inconsistent or incomplete transaction data

• Immediate high-value transactions following account opening

• Suspicious third-party intermediaries or shell entities

While not always indicative of criminal activity, these red flags warrant further scrutiny and may lead to the transaction being halted or escalated for review by AML analysts.

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While both transaction screening and transaction monitoring are vital components of a robust AML framework, they serve different purposes and take place at different stages of the transaction lifecycle.  

Transaction screening is a pre-transaction control. It analyses the details of individual transactions such as the names of the sender and recipient, the amount, and the countries involved, before the transaction is approved. Its primary purpose is to prevent payments from being processed if they are associated with prohibited or high-risk activity, such as sending funds to a sanctioned entity or purchasing restricted goods. This allows financial institutions to stop a transaction in its tracks, even if the customer’s past behaviour appears legitimate.

Transaction monitoring, by contrast, takes place after transactions have been completed. It analyses patterns of activity over time to detect behaviours that might indicate money laundering or other financial crimes. While an individual transaction may not appear suspicious in isolation, when combined with other activities (such as rapid movement of funds through multiple accounts or structuring deposits to avoid detection) it may point to a broader, coordinated attempt to launder money.

Transaction monitoring can operate in real time or retroactively, and it plays a critical role in identifying emerging risks that screening alone may not catch. Together, these tools provide a layered, risk-based approach to AML/CFT compliance.

Learn more about transaction monitoring.

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An effective transaction screening process involves several real-time and automated steps:

1. Transaction initiation: A customer initiates a payment or transfer.

2. Data capture: Key information is extracted — sender and beneficiary names, amounts, dates, locations, goods, etc.

3. List matching: This data is matched against sanctions lists, PEP databases, internal blacklists, and other relevant watchlists.

4. Risk evaluation: The screening engine analyses whether any risk indicators are present.

5. Decision making:

• If no matches are found, the transaction proceeds.

• If potential matches arise, the system either blocks the transaction or sends it for review.

6. Investigation and review: Compliance analysts investigate alerts to determine whether they are false positives or legitimate risks.

7. Audit trail and reporting: All screening activity is logged for audit and compliance purposes.

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Changing regulatory requirements

The regulatory environment varies across different jurisdictions and is constantly changing, especially around sanctions. Failing to stay current with updated watchlists or emerging risks can lead to non-compliance and reputational damage.

Poor AML systems or databases

Effective transaction screening relies heavily on high-quality systems and accurate data. Poorly built or outdated systems may struggle to aggregate information from multiple sources, leading to missed matches or unreliable screening results. Incomplete or inconsistent databases can create blind spots, undermining compliance and increasing the risk of financial crime slipping through.

False positives

High rates of false positives can overwhelm compliance teams, delaying legitimate transactions and reducing operational efficiency. Many legacy systems lack the intelligence to differentiate between benign matches and real threats.

Ongoing maintenance

Financial criminals continuously adapt their methods to bypass controls. AML systems need regular updates to data feeds and rule configurations to stay effective against evolving threats.

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To conduct effective transaction screening, organisations must take a proactive, risk-based approach supported by intelligent technology. This begins with integrating screening directly into payment workflows so that transactions are checked in real time before approval. Up-to-date sanctions, PEP, and internal watchlists are essential, and firms must ensure these lists are refreshed automatically to remain compliant with rapidly changing regulations.  

Traditional rule-based systems often struggle with volume, complexity, and nuance, leading to high false positive rates. Artificial Intelligence helps overcome these limitations by learning from historical data, identifying complex patterns, and making intelligent decisions about which transactions warrant investigation. For example, AI can distinguish between a true match and a false positive by analysing the context of a name, location, or transaction behaviour.

It can also continuously adapt to new risks, improving performance over time and reducing manual intervention. When combined with automation, AI enables faster decision-making and more accurate risk detection, thus freeing compliance teams to focus on genuinely suspicious activity.  

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