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Every day, financial institutions process huge volumes of payments across cards, bank transfers, digital wallets, mobile applications and increasingly interconnected platforms. Payments are faster, more digital and more frequently cross-border. Customers expect services to be instant and always available, while criminals exploit the same speed, scale and convenience.
Many financial institutions have already invested heavily in transaction monitoring capabilities over recent years.
Fraud schemes increasingly combine social engineering, account takeover, mule accounts, synthetic identities and automated attacks. In many cases, the transaction itself is correctly authenticated, making the wider context essential.
The challenge is no longer visibility. Financial institutions already have access to vast amounts of data. Instead, the focus is on identifying the signals that indicate fraud or financial crime early enough to stop the movement of funds before the opportunity to intervene is lost.
Static rules and fixed thresholds are no longer sufficient on their own. Criminals test controls, adapt their behaviour and structure activity to remain below known limits. Effective transaction monitoring must therefore move beyond a back-office compliance function. It should operate as a strategic capability that protects customers, reduces losses, supports regulatory compliance and enables secure digital growth.
What we see in practice
Many financial institutions are not struggling to generate alerts. They are struggling to determine which alerts matter most.
In our experience, the challenge often lies in fragmented data, disconnected fraud and AML processes, and growing investigation workloads. As fraud schemes become more sophisticated, institutions need greater context rather than simply more alerts.
Why context changes the risk
Imagine a customer who has historically made only domestic payments. One morning, the account sends several transfers to newly added beneficiaries in different countries. Each payment may look plausible in isolation. Viewed together and combined with a recent password reset, a login from an unfamiliar device and an unusual location - the pattern tells a very different story.
This is the central challenge in modern monitoring: risk rarely sits in one data point. It emerges from connections between the transaction, the customer, the device, the channel, the counterparty and the sequence of events. A single payment may be legitimate. A sequence of unusual actions may indicate that the customer has been manipulated or the account compromised.
A modern framework should bring together four mutually reinforcing capabilities:
Suspicious transaction detection
to identify unusual values, destinations, frequencies and flows.
Customer behaviour analysis
to distinguish normal activity from meaningful deviations.
Event monitoring
to capture risk signals before and around the payment.
Intelligent alert management
to prioritise the cases most likely to require action.
Their real value appears when they work together. A transaction amount, device change or new beneficiary may each be harmless. Combined, they can expose a higher-risk customer journey that would remain invisible to a transaction-only control.
From transaction detection to behavioural understanding
One notable finding is how slowly biometric technologies are being adopted.
Even more concerning, 30% and 43% respectively report no plans to adopt these capabilities.1
Suspicious transaction detection remains the foundation. Monitoring solutions assess indicators such as sharp increases in transaction frequency, unexpected international transfers, high-risk counterparties, rapid movement of funds or activity inconsistent with the customer profile. Advanced analytics can uncover patterns across large datasets and rank which anomalies are most likely to represent genuine risk.
Unusual does not always mean suspicious. A large international payment may be expected for a corporate customer and highly unusual for a retail customer with a stable domestic pattern. Behavioural analysis and machine learning provide the missing context by establishing what “normal” looks like for each customer and identifying meaningful deviations over time. Dynamic risk scoring can further help identify customers, transactions and behaviours that are more likely to be associated with fraud or financial crime.
By combining customer behaviour, transaction history, devices, locations and interaction patterns, institutions can assess risk more accurately, reduce false positives and focus investigations on genuinely suspicious activity.
Event monitoring can reveal fraud earlier
As fraud schemes become more sophisticated, relying solely on traditional transaction data is no longer enough. Connected devices can provide valuable behavioural signals that help organisations identify suspicious activity and improve fraud detection outcomes. Yet only 26% of organisations use data from connected devices in their anti-fraud analytics2. This suggests that many institutions may still be missing valuable signals that could help identify fraud earlier.
An unusual login, a new device, a password reset, modified contact details or a newly added or modified beneficiary may all be legitimate events in isolation. However, when combined with transaction activity, they can provide critical context and reveal elevated risk much earlier than transaction data alone.
For example, a payment that appears consistent with a customer’s typical behaviour may require additional scrutiny if it is preceded by a password reset, access from an unfamiliar device and the addition of a new beneficiary. Looking at the full customer journey enables intervention before the loss occurs rather than investigation afterwards.
Better detection is only the beginning
Detection is only useful when it leads to timely action. Poorly calibrated systems can generate large volumes of false positives, creating backlogs and increasing the risk that genuinely suspicious activity is missed.
Investigators may spend valuable time collecting information from multiple systems instead of analysing the case.
Intelligent alert management addresses this by combining risk indicators, scoring alerts and routing the highest-risk cases first. Integrated case management can present relevant transactions, customer information, device events and prior activity in one view. Clear workflows, escalation criteria and quality controls then help investigators make faster, more consistent and more defensible decisions.
The objective is not to eliminate human judgement. It is to focus that judgement where it adds the most value. Fewer unnecessary alerts mean more time for complex cases, faster responses and better use of specialist capacity.
However, effective detection, technology and prioritization may not be sufficient to guarantee successful outcomes. Many institutions continue to face capacity constraints within investigation teams, particularly during periods of elevated fraud activity, regulatory reviews or large-scale remediation programmes. Even when monitoring systems effectively identify high-risk activity, delays in investigation can reduce the ability to intervene before losses occur.
Effective monitoring requires not only strong detection capabilities, but also sufficient investigative capacity and flexible operating models.
Increasingly, institutions are exploring specialist investigation teams, managed service models and hybrid approaches that help ensure critical alerts are reviewed quickly and consistently. These models can provide access to experienced investigators and specialised expertise without the delays typically associated with recruiting, onboarding and training new resources internally.
This enables organisations to scale investigative capacity more rapidly, respond more effectively to fluctuations in alert volumes and ensure that critical cases receive timely attention.
The business value extends beyond compliance
A stronger monitoring framework delivers value across several dimensions
The most immediate is lower fraud loss. Earlier detection and richer context improve the chance of stopping a suspicious payment, contacting the customer or restricting an account before further funds are moved.
The second is operational efficiency. Better calibration, data enrichment and prioritization reduce low-value reviews. Even relatively low time savings per alert can translate into substantial additional capacity when an institution processes thousands of alerts. That capacity can be redirected to emerging typologies, complex investigations and preventive analysis.
The third is customer trust. Strong protection should not require every customer to face additional checks on every payment. More accurate detection allows institutions to apply friction selectively, respond more quickly when customers are at risk and minimise disruptions to legitimate activity. This supports both security and a smoother digital experience.
Finally, effective monitoring strengthens regulatory readiness. Expectations are moving toward risk-based, real-time and data-driven fraud prevention. In Europe, the evolving PSD3 and PSR framework reinforces the importance of transaction monitoring, customer protection, fraud detection and information sharing. Institutions should therefore be able to show not only that controls exist, but that they are tuned, governed and effective.
What does a future-ready approach look like?
The future will not be defined by replacing every rule with artificial intelligence. Rules remain valuable for known scenarios, clear thresholds and regulatory requirements. The opportunity is to combine them with machine learning, anomaly detection, network analysis and behavioural intelligence, then govern those models throughout their lifecycle.
Beyond transaction detection, AI-powered solutions may increasingly support the continuous improvementof monitoring controls themselves. By analysing alert outcomes, investigator feedback and emerging fraud typologies, AI can help identify rules that are no longer effective, recommend threshold adjustments and highlight areas where new analytical models may be required.
Over time, this could transform transaction monitoring from a periodically reviewed control into a continuously learning framework that adapts to changing risks, customer behaviours and fraud patterns.
Institutions also need to connect functions that have historically operated in silos. Fraud prevention, transaction monitoring, AML controls, sanctions screening, customer risk management and case handling often rely on separate systems and teams. A connected model can improve information flow, expose relationships between accounts and counterparties, and support consistent decisions across the financial crime lifecycle.
Technology alone will not create that capability. Institutions also need high-quality data, clear ownership, model validation, documented thresholds, effective feedback loops and experienced investigators. Monitoring should be reviewed continuously against confirmed fraud, missed activity, changing customer behaviour and emerging typologies. A control that was effective last year may not remain effective today.
From reactive control to proactive intelligence
Financial crime is becoming faster, more connected and harder to detect through isolated rules. Financial institutions do not need more alerts. They need better intelligence. They need monitoring that brings together transactions, behaviour, account events and investigative intelligence. When those signals are connected, organisations can identify risks earlier, prioritise responses more effectively and protect customers with less unnecessary friction.
Transaction monitoring should no longer be treated as a standalone compliance control. Done well, it becomes a business capability that reduces losses, improves efficiency, strengthens resilience and supports trust. As financial crime becomes more complex and interconnected, transaction monitoring must evolve beyond compliance. The institutions that integrate data, intelligence and monitoring capabilities most effectively will gain a significant advantage in protecting customers, managing risk and supporting long-term growth.
Source: 1,2 ACFE and SAS, 2026 Anti-Fraud Technology Benchmarking Report
Financial Crime Services
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