Transaction monitoring is the screening of financial payments to detect suspicious activity. It prevents fraud by acting as a filter, giving firms the power to instantly pause activity when red flags are identified, before wide-scale damage can occur. It’s also a critical aspect of UK anti-money laundering (AML) frameworks.
Trustpair supports transaction monitoring by validating supplier bank account ownership in real-time. By automatically flagging vendor data anomalies before payments are released, the platform controls risk and secures enterprise organizations.
Transaction Monitoring: Key Takeaways:
- Transaction monitoring involves watching the income and spending patterns of an account holder to detect unexpected activity
- Transaction monitoring works by comparing actual financial activity against ‘normal’ or ‘expected’ patterns
- Transaction monitoring is a core control for anti-money laundering (AML), helping regulated UK organisations detect, investigate, and report suspicious financial activity in line with compliance requirements
- Monitoring should detect red flags like: smurfing, layering, micropayments, data mismatches, change requests, and associations with suspicious partners
- Firms should prioritize automated monitoring to avoid errors, inconsistencies, backlogs and false positives
- For compliant transaction monitoring, consider AI technologies and building a culture of compliance from the top
What is transaction monitoring?
Transaction monitoring is the process of actively watching a customer, partner or business’ spending in order to spot suspicious activity. It’s typically required of banks, payment service providers, and other regulated firms in the UK. And it’s done to spot signs of account takeover fraud or money laundering within online banking activity. As such, it’s a critical part of UK Anti-Money Laundering (AML) regulation.
The transaction monitoring process is a crucial part of wider risk management, protecting firms from the initial fallout of financial crime and the reputational damage that ensues. It relies on companies having enough transaction history or account-holder data to determine ‘typical’ spending patterns, and then being able to react when atypical spending behaviour happens.
The other popular use case for transaction monitoring fits into loan assessments – and speeding them up. Lenders therefore use transaction monitoring to analyse an applicant’s financial behaviour automatically. Because the process is based on real account data, the lending decision-making of approvals, referrals or denials, becomes easier than ever.
How does transaction monitoring work?
The technical side of transaction monitoring works as so:
- A person or entity opens a new financial account with a financial institution.
- When income is deposited or a purchase is made, it’s categorized and enriched accordingly. The insights derived in this part of the process varies by provider, but could entail:
- classification into categories- with up to three levels of detail
- merchant identification – naming the retailer where the purchase was made (outgoing) or source of income (incoming)
- geolocation services- figuring out the address of the retailer where the purchase was made
- visual enrichment – such as adding the merchant logo, website address and integrated map of the store location
- The transaction is then added as a data point to all of the others from that account. It contributes to figuring out the account patterns and building a baseline of typical spending behaviour for that customer.
- Financial institutions with good segmentation practices may also look at patterns among specific groups, such as: first time buyers, owners of particular financial products like credit cards, or demographics.
- When a transaction occurs outside of typical patterns, it is automatically flagged. For example:
- Purchases with merchants outside of the typical physical location (either by country or region), especially if these locations are on watchlists
- Unusual categories, such as for personal products in a business account
- Spending at atypical times of day, like when the customer is expected to be sleeping
- Unusual amounts, either much smaller or larger than normal
- When a red flag transaction is detected, it should trigger automated responses to immediately protect the account, and a manual review by a human at the bank for further investigation.
How does transaction monitoring help AML regulatory compliance?
Transaction monitoring plays a central role in AML compliance by enabling organizations to detect, assess, and report suspicious financial activity. For financial institutions and other regulated entities, it is a core control within broader anti-money laundering (AML) frameworks designed to combat financial crime (money laundering, terrorist financing, etc).
AML transaction monitoring involves continuously analysing transactions across the customer lifecycle to identify behaviours that deviate from expected patterns. These behaviours may indicate attempts to disguise the origin, movement, or destination of illicit funds. By screening each transaction against predefined rules, risk indicators, and behavioural models, organisations can determine whether activity warrants further investigation or reporting.
The AML transaction monitoring process supports compliance obligations by generating alerts when potential risks are detected. These alerts are reviewed to determine whether they must be escalated and reported via a Suspicious Activity Report (SAR) to the UK National Crime Agency (NCA), in line with expectations set by the Financial Conduct Authority (FCA).
One of the main challenges facing AML transaction monitoring systems is managing false positive alerts. Poorly calibrated rules or incomplete data can result in high false positive rates, overwhelming compliance teams and diverting attention away from real financial crime risks. As a result, modern anti money laundering AML programs increasingly rely on advanced analytics and contextual data to improve accuracy, reduce noise, and focus resources on high-risk activity.
Ultimately, effective transaction monitoring enables organisations to demonstrate robust AML regulatory compliance by showing that they can identify suspicious behaviour, respond in a timely manner, and maintain clear audit trails. When implemented correctly, it strengthens trust with regulators while helping institutions protect themselves from regulatory penalties, reputational damage, and financial loss.
What red flags should monitoring detect?
When fraudsters take over an account, or users commit first-person fraud by money-laundering or committing other financial crimes, there are some telltale signs.
Transaction patterns
Consistent monitoring should be able to reveal income or spending behaviour that is unusual, such as:
- Smurfing: multiple transactions that are kept just below the regulatory reporting threshold over a short period of time. For example, the Bank Secrecy Act has a requirement to file Currency Transaction Reports (CTRs) for cash transactions exceeding $10,000 in a single business day.
- Layering: funds are rapidly moved by being deposited in and out immediately, to unrelated third parties without clear business rationale.
- Dormancy spikes: accounts that are inactive for months suddenly start receiving high-value transfers or deposits.
- High-volume micropayments: also known as card cracking, this is a technique used by payment fraudsters to test the validity of card details. By making lots of tiny purchases (typically under the value of $1) in a period of a few hours, the attackers test whether the payment details are valid while avoiding the attention (and push notifications) that larger purchases bring.
Data inconsistencies
By monitoring transactions consistently, they should be able to uncover:
- KYC mismatches: customers who have transaction volumes or values that are inconsistent with their stated occupation or wealth profiles, suggesting that more information is required.
- Unusual routing: funds are sent through intermediaries before landing at their final destination.
- Data changes and requests: account owners are changing their names, addresses, or business details, without validating them.
Financial associations
Firms that monitor transactions should also consider how they inspect the accounts that send or receive money with their own account holders. For example, third parties in blacklisted or greylisted locations are a red flag. Payments from these countries could indicate terrorism watchlists or high money laundering risks, as set out by the FATF.
What are the risks of manual monitoring?
While manual transaction monitoring, which relied on spreadsheets and human review, was once standard, it’s now considered high-risk. As transaction volumes and complexity increase, especially with the addition of cryptocurrency payments, manual monitoring brings risks:
| Risk | Why? |
| It’s highly prone to inconsistency | Different reviewers may interpret the same transaction differently, based on their own biases and experiences. |
| It’s subject to errors | Reviewing thousands of lines of data can lead to cognitive fatigue, and with lots of irrelevant noise in the picture, red flags may be missed. |
| It’s done in batches | Meaning that institutions can miss the opportunity to react in real-time and close down any threats before they grow worse. |
| It can cause backlogs and bottlenecks | When transaction volumes spike, manual teams find it harder to scale up, either relying on untrained temporary support, or taking on the costs of hiring, onboarding and training full-time staff. |
| It leaves inadequate legal trails | Accountability trails and immutable history are required for audits. But manual workflows, without access logs and indelible data, for example, are not fit for regulatory compliance. |
How does automation improve monitoring?
Automation transforms reactive monitoring into a proactive, data-driven way to protect businesses, and their account holders.
It can drastically reduce false positives, by replacing rules-based logic with smart decision-making, driven by machine-learning. With contextual intelligence, these systems can tell when transactions are genuine, and when they may need a second look. This enables humans to only be brought in on transactions that are questionable, conserving company resources for the high-risk activity.
It also enables organizations to respond in real-time. Without the retrospective monitoring aspect of batches, modern systems can automatically screen transactions before they are cleared.
Trustpair offers automated account validation, monitoring the given account details against verified external data, and helping businesses avoid the risk of payment fraud. If any discrepancies or suspicious activity exists, the outgoing payment is automatically paused before it’s left the account. This protects companies against paying out to fraudsters, and the reputational damage that comes with it.
How can companies keep monitoring compliant?
Keeping transaction monitoring compliant requires more than implementing basic rules or meeting minimum reporting thresholds. Regulators increasingly expect organisations to demonstrate that their monitoring controls are effective, risk-based, and consistently applied across all transactions and customer segments. This means moving away from static, one-size-fits-all rules towards monitoring approaches that reflect real financial crime risks and evolving behaviour.
Recent enforcement actions show how gaps in transaction monitoring quickly translate into compliance failures. In 2024, TD Bank was fined $1.3 billion by FinCEN after failing to adequately monitor and report suspicious transactions. The investigation highlighted not only weaknesses in monitoring technology, but also insufficient oversight, under-resourced AML teams, and a lack of visibility into how alerts were generated and escalated. As a result, high-risk activity went undetected for extended periods.
While rules-based controls such as the $10,000 reporting threshold remain a regulatory requirement, they are no longer sufficient on their own. Effective compliance depends on unifying transaction data, applying contextual analysis, and ensuring monitoring systems can explain why transactions are flagged. Advanced analytics and machine-learning models can help improve detection and reduce false positives, but only when they are transparent, auditable, and aligned with FCA expectations.
Ultimately, regulators assess transaction monitoring not just on detection capability, but on governance. Clear accountability, documented decision-making, and senior management oversight are critical to proving that monitoring controls are operating as intended and that AML obligations are being met in practice.
Implementing smart transaction monitoring to prevent financial crime
Transaction monitoring is the real-time, automated screening of financial activity to detect and block suspicious patterns. By replacing manual reviews with behavior-based analytics, companies can identify complex criminal networks. Trustpair supports firms by validating transaction data in real-time, automatically blocking payments to suspicious accounts.