False positives account for more than 90% of anti-money laundering (AML) alerts. When each alert requires manual review, the cumulative burden drains resources, overextends teams, and increases the chances of overlooking legitimate threats. During periods of geopolitical instability or following regulatory changes, alerts can surge overnight — creating unmanageable backlogs and pushing already-stretched compliance teams to their limits.

Most traditional AML alert management systems were designed to detect potential risks. They were not built to guide analysts in interpretation, prioritisation, or supporting adjudication workflows. As a result, teams are left sorting through a flood of alerts without context or clear paths to resolution. 

The next phase of alert management goes beyond detection, using smarter tools to turn individual alerts into shared, usable intelligence — improving accuracy, speeding up reviews, and helping teams focus on higher-risk cases.

Why AML Alert Management Needs To Evolve

As compliance costs rise and false positives overwhelm alert queues, financial institutions are recognising that traditional, manual-heavy AML alert systems can't keep pace.  

The Cost of Manual AML Alert Review

In the UK alone, banks and fintechs now spend upwards of £38 billion annually to combat financial crime, a 33% jump since 2021. That rise is driven by increasingly stringent regulations and growing customer volumes, both of which add pressure to compliance operations.

But even with increased spending, many firms still struggle with underlying operational inefficiencies. Duplicative tasks, fragmented processes, and heavy reliance on manual remediation quietly chip away at budgets and slow resolution times. These inefficiencies also make it harder to scale operations or adapt to new regulatory requirements without adding headcount. 

Even under steady conditions, analysts may review hundreds of alerts per day. During high-volume periods such as those triggered by recent Russian sanctions, that pace can double.

This pressure takes a human toll. Alert fatigue, a state where analysts become desensitised to real risk, leads to burnout, lowers morale, and slows triage. As the pace of alerts accelerates, quality drops and true threats are more likely to slip through — exposing firms to regulatory penalties and reputational damage.

Why Regulators Are Prioritising Intelligence over Alert Volume

More alerts don’t necessarily lead to better outcomes or better intelligence. In its recent guidance, the Wolfsberg Group noted that despite a growing number of Suspicious Activity Reports (SARs),  there has been no “proportionate increase in highly useful information.” The Financial Action Task Force (FATF) has also observed that there has been no “material reduction in financial crime.”

To improve effectiveness, financial institutions are urged to adopt a more strategic, intelligence-led approach. This means moving beyond sheer volume and aligning more closely with the risk-based approach: understanding where risk lies and allocating resources accordingly.

In this context, the shift from volume to value becomes essential — prioritising the quality and relevance of alerts over quantity. That requires systems that not only flag risk but also provide accurate, contextualised insight to support faster triage and more focused investigations. 

What Intelligence-Led AML Compliance Looks Like

An intelligence-led approach focuses on using data effectively to highlight genuine risk, reduce noise, and support consistent decision-making. The goal is to empower analysts to act with greater speed and confidence, focusing on what truly matters. 

Turning AML Alert Data into Actionable Insight

Intelligence is the combination of data, context, and actionability. Rather than the amount of data available, intelligence centres on how that data is used to support consistent, well-informed decisions. The FATF explicitly emphasises the need for actionable information about threats to effectively counter money laundering and terrorist financing. 

However, many compliance teams are already overwhelmed with information. What’s missing is a layer of analysis that makes existing alerts more actionable. That means suppressing irrelevant signals and pinpointing high-risk activity —  so analysts can move quickly and confidently through adjudication with fewer distractions, applying their expertise to real instances of financial crime or suspicious activity. 

How to Prioritise Genuine Risk over False Positives

Intelligence in AML goes beyond what’s visible to knowing which risks are worth acting on. But for many teams, resources are drained by alert volume. Analysts spend their time clearing queues rather than evaluating actual threats.

The Wolfsberg Group encourages firms to focus on crystallised risk — threats that are observable and evidenced, not just theoretical. Their guidance highlights the importance of feeding insights  back into monitoring systems, such as incorporating case outcomes to improve risk detection. 

In alert management, that feedback loop is essential. For example, when an alert linked to a known low-risk entity is consistently adjudicated as a false positive, the system can learn to deprioritise similar cases in the future. When adjudication outcomes inform decision logic, systems become more precise over time. This creates a continuous learning cycle — one that frees analysts from repetitive false positive reviews and allows them to focus on genuine risk. It also marks a shift from reactive alert clearing toward true risk-based decision-making. 

What Smarter AML Alert Management Software Should Deliver

The next generation of AML software goes beyond flagging potential issues. It learns from outcomes, adapts to evolving threats, and helps teams handle alerts with greater speed and accuracy. By connecting context, risk scoring, and adjudication logic, smarter tools reduce repetitive work and improve overall effectiveness.

Adaptive Learning That Speeds Up Alert Decisions

A modern alert management tool should do more than generate alerts — it should learn from outcomes and improve over time. As financial crime becomes more complex, driven by artificial intelligence (AI) and cross-border networks, compliance teams need systems that can keep pace.

AI technologies can recognise familiar patterns, reference past decisions, and convert isolated judgment into repeatable institutional knowledge. This kind of shared intelligence connects time, teams, and alerts — reducing the need to re-investigate known scenarios. Analysts can build on what’s already been resolved, leading to faster, more confident and consistent decisions – especially when handling recurring alerts.

To support this, modern tools utilise machine learning models to adjudicate alerts with minimal human intervention, drastically reducing investigation time. Over time, this learning sharpens alert scoring, reduces false positives, and supports more effective prioritisation. As systems learn from past outcomes and reuse familiar decisions, teams can retain control by customising scoring logic to reflect their risk thresholds and compliance policies.

Transparency and Audit-Ready Compliance

Technologies like AI and machine learning are increasingly recognised by industry bodies for their ability to improve effectiveness and efficiency in anti-financial crime compliance. But to withstand regulatory scrutiny, these tools must be transparent, explainable, and secure, especially as explainable AI becomes a growing area of regulatory focus. 

If an alert system can’t explain how it reached its decision to deprioritise an alert, it undermines accountability and trust. Alert management tools should generate detailed audit trails that capture both system and user actions, along with clear, understandable rationale behind each decision. Role-based access controls are also essential to ensuring users only see what’s relevant — supporting transparency, data security, and audit readiness.

Together, these features make outcomes defensible in regulatory reviews and support consistent decision-making across teams and regions.

How Facctum Improves Alert Adjudication and Risk Triage

Facctum’s Alert Adjudication Software reduces redundancy, prioritises high-risk alerts, and accelerates case resolution. Dynamic alert scoring and decision reuse help teams work more efficiently at scale.

Built-In Compliance with FATF, OFAC, and Audit Standards

Workflows automatically align with FATF, OFAC, and other regulatory standards. Every action is tracked, and every decision is explainable, with full audit trails and role-based access controls to support oversight and streamline regulatory reviews. The platform integrates with your existing systems and scales with your operations — maintaining speed and accuracy as volumes grow.  

Ready to Modernise Your AML Alert Strategy?

Our auto-adjudication engine automates up to 70% of manual reviews, so analysts are free to focus on high-priority alerts and investigations.

Each adjudication refines scoring and lowers false positives, turning past decisions into a shared knowledge base that improves accuracy and long-term efficiency.

Move from alert overwhelm to faster, risk-based decisions — guided by shared intelligence.

Contact us to learn how Facctum can improve your alert management strategy.