What is transaction monitoring in AML?
Transaction monitoring in anti-money laundering (AML) is the process of tracking and analysing financial transactions to detect suspicious activities.
Banks and other financial institutions typically use automated transaction monitoring systems to identify unusual behaviour patterns and flag transactions that may be related to money laundering, fraud, or terrorist financing. These transactions are then further reviewed by a trained anti-financial crime specialist or fraud analyst. If they conclude the transaction is high-risk, the institution files a Suspicious Activity Report (SAR) with the relevant authorities.
Why the AML transaction monitoring process matters
AML transaction monitoring is becoming more crucial thanks to the rise of digital payments and the increasing rates of financial crimes like money laundering, tax evasion, and terrorist financing.
Transaction monitoring is a mandatory process for banks and other organisations that fall within the scope of money laundering regulations such as the Anti-Money Laundering Directive (AMLD) in the EU. Without effective transaction monitoring, there’s a greater likelihood of illicit funds flowing undetected through the financial system.
For financial institutions, failing to implement proper transaction monitoring mechanisms means they could be subject to regulatory penalties and increased reputational risk.
Key components of effective transaction monitoring systems
While regulated organisations are obligated to perform transaction monitoring, they have a degree of freedom regarding how they carry out and manage the process.
To monitor customer activities such as deposits, money transfers, and withdrawals, banks and financial institutions usually rely on various measures, including:
A set of risk-based rules and thresholds
Customer profiling and behaviour analysis
Automated alerts for suspicious activities
Real-time and retrospective transaction analysis
Machine learning and AI-driven detection of anomalies
Rules-based vs. AI-driven monitoring approaches
Rules-based monitoring relies on predefined thresholds and scenarios to flag transactions (e.g., cash deposits above €10,000 or structuring attempts with multiple €9,999 transactions).
AI-driven monitoring leverages machine learning and behavioural analysis to identify anomalies and evolving financial crime patterns. It improves the efficiency of rules-based mechanisms by reducing false positives (i.e., cases in which a legitimate transaction is incorrectly flagged as suspicious).
Common challenges and red flags in AML transaction monitoring
AML transaction monitoring systems look for pre-defined red flags in customers’ behaviour or risk profile to uncover potential threats. However, both external and internal factors can impact the efficiency of the applied AML transaction monitoring procedures.
Some of the major challenges in transaction monitoring include:
1. Identifying the patterns behind suspicious transactions
Financial institutions rely on automated transaction monitoring systems to detect suspicious patterns. The systems analyse large volumes of historical and real-time financial data and apply predefined rules, machine learning models, and behavioural analytics to evaluate the risks associated with individual transactions.
Common red flags include large cash deposits or withdrawals that are inconsistent with a customer’s profile or behaviour. AML transaction monitoring systems also look for structured transactions in which large amounts are split into several transfers to circumvent reporting thresholds (structuring/smurfing) or unusual activity in dormant accounts, such as sudden large transactions. Frequent high-value transactions or rapid wire transfers involving offshore accounts or high-risk jurisdictions may also be flagged as suspicious.
Flagged cases can trigger an Enhanced Due Diligence (EDD) review, during which compliance officers perform in-depth investigations. If necessary, they can file a Suspicious Activity Report (SAR) with the relevant authorities.
2. Filtering out false positives
False positives occur when legitimate transactions are incorrectly flagged as suspicious. Usually this happens if rules (e.g., flagging transactions exceeding €10,000) aren’t accompanied by additional information gained from customer segmentation and behavioural profiling.
Filtering false positives out is essential to:
Improve detection accuracy by focusing on genuinely suspicious activities and allowing legitimate transactions to proceed.
Streamline transaction monitoring processes in times of ever-increasing volumes of online fund transfers.
Avoid unnecessary investigations and operational inefficiencies.
Reduce compliance costs by decreasing labour costs, which are usually the biggest expense in financial crime compliance spending.
3. Cross-border transaction risks
Cross-border transactions pose significant risks in AML compliance due to the complexity of international financial networks, varying regulatory standards, and increasing transaction volumes.
Criminals can try to exploit differences in AML regulations across jurisdictions and obscure the source of illicit funds by using offshore accounts and shell companies, for example.
Transactions involving high-risk countries, tax havens, or politically unstable regions require enhanced scrutiny, as they often lack vigorous AML enforcement. Financial institutions must implement real-time transaction monitoring, sanctions and politically exposed persons (PEPs) watchlist screening, and risk-based due diligence to detect suspicious activities in cross-border payments.
4. Other emerging threats
Other emerging threats that require transaction monitoring processes to evolve continuously include:
Cryptocurrency transfers and decentralised finance (DeFi) platforms: Growing volumes of digital asset transactions have complicated cross-border AML compliance and surfaced risks related to customer identity and the origin of funds.
Evolving complexity of synthetic identity fraud schemes: Such schemes, which include creating fraudulent identities to help “clean” illicit funds, have become more sophisticated. Today's synthetic ID schemes may include various channels and tools, such as social media, digital payment apps, and employment scams.
Growing popularity of real-time payment services: While instant money transactions bring convenience, they also necessitate stricter risk assessments and more robust real-time transaction monitoring measures to capture potential money laundering efforts as they unfold.
AI-powered financial crimes: The latest technological developments have equipped financial criminals with advanced tools to commit fraud, including AI-generated deepfake identities and voice cloning.
AML transaction monitoring best practices
There isn’t a universal standard that spells out which components an AML transaction monitoring process should include. But regulatory requirements require financial institutions to adopt a risk-based approach that analyses customer profiles and transaction types while accounting for jurisdictional risk exposure.
To ensure effective AML transaction monitoring, banks and financial companies should consider the following best practices:
Initial risk assessments
Based on the business case, customer profiles, and jurisdiction-specific regulations, every organisation should define the rules that will determine when financial transactions and customer activities would be considered suspicious and subject to follow-up investigations (e.g., amounts transferred; number of transactions over a certain period; participating parties in the fund transfers, etc.).
Designing a risk-based AI-powered monitoring framework
The initial risk assessments inform the design of the triggers that would flag suspicious activities. These might include pre-defined rule-based thresholds, customer profile specifics, transaction patterns, jurisdictional risks, and more.
The rule- and risk-based system should be complemented by real-time AI-driven analytics to better detect anomalous transaction patterns, reduce false positives, and automate processes as much as possible.
Initial screening and continuous real-time monitoring
Critical procedures such as initial risk scoring and real-time transaction flagging should be applied proactively and reactively. This would allow compliance teams to promptly identify suspicious activities and ensure the needed time to perform follow-up investigations and file the cases as SARs, if necessary.
Third-party intelligence and risk screening tools
To enhance the efficiency of internal transaction monitoring systems, financial institutions should complement their capabilities with third-party intelligence. This includes data from adverse media screening, blockchain and forensics tools, sanctions lists, PEP databases, and other sources.
Compliance oversight, training, and keeping the AML transaction monitoring system up to date
Organisations should conduct regular regulatory stress tests, internal audits, and AML training programs to stay abreast of the evolving risk universe and ensure compliance. It’s also important to establish cross-border information-sharing agreements and foster collaboration with global financial intelligence units.
Transaction monitoring FAQs
What's the difference between transaction monitoring and transaction screening?
Transaction monitoring involves continuously analysing customer transactions in real-time or retrospectively to detect suspicious patterns related to money laundering, fraud, or terrorist financing.
Transaction screening, on the other hand, is a pre-transaction check that uses KYC and other information to account for the client risk and verify payments against sanctions lists, politically exposed persons (PEPs) lists, and watchlists before processing.
While monitoring is an ongoing process, AML screening is preventative, blocking suspicious transactions from occurring.
How often should I update my company’s transaction monitoring rules?
It depends on the specifics of your business, but it’s generally advisable for a company to update its transaction monitoring rules quarterly or semi-annually.
In cases of regulatory changes, new threats, or identified loopholes in the adopted AML mechanisms, the transaction monitoring rules should be updated immediately.
Do transaction monitoring systems detect all forms of money laundering?
Transaction monitoring systems, especially standard rule-based ones that lack adaptability, probably can’t detect all forms of money laundering with 100% accuracy. After all, criminals continuously evolve their tactics to bypass detection.
However, advanced AI-driven monitoring approaches significantly improve detection by identifying unusual transaction patterns, advanced layering techniques, and high-risk anomalies and behaviours. Paired with human oversight, they can strengthen AML compliance and protect against most money laundering risks.
