Why Real-Time AML Compliance Is Now Essential for Instant Payments

The global adoption of real-time payments has transformed financial services. From the UK’s Faster Payments system to the EU’s Instant Payments Regulation, money now moves in seconds, around the clock.

But financial crime does too.

Legacy anti-money laundering (AML) systems weren’t built to operate at this speed. Static thresholds, delayed alerts, and batch monitoring cannot protect a real-time ecosystem. To manage financial crime risk effectively, compliance infrastructure must become just as fast, intelligent, and integrated as the payments themselves.

The Scale of the Instant Payments Shift

According to the 2024 Prime Time for Real-Time report by ACI Worldwide, over 266 billion real-time transactions were processed globally in 2023. That’s a 63% increase from the previous year.

Europe’s upcoming Instant Payments Regulation will require euro transactions to settle within 10 seconds, available 24/7. This is not just a technology upgrade. It demands a fundamental rethinking of compliance monitoring. The European Central Bank is not only leading regulatory change, but also investing in infrastructure that ensures instant payments are fast, secure, and compliant. 

Why Traditional AML Systems Fall Short

Most AML tools in production today were built for a different era. The UK’s Money Laundering Regulations require firms to assess and manage risk continuously, yet many legacy systems still operate on outdated batch logic.

They rely on:

• Periodic batch processing

• Static transaction thresholds

• Delayed alerting

• Siloed customer data

• Manual resolution pipelines

The UK’s Future of Payments strategy acknowledges the increasing operational demands placed on financial institutions, especially as real-time systems require faster, more resilient compliance frameworks.

The FCA’s financial crime guidance further stresses that risk detection must now operate in near-real time to meet regulatory and operational expectations.

Criminals Are Exploiting the Speed Gap

Bad actors already understand the speed advantage. The UK Finance 2024 Annual Fraud Report reported that £460 million was lost to authorised push payment (APP) fraud in 2023. Many of these scams took advantage of real-time payment flows, allowing funds to vanish before compliance teams could intervene.

Techniques like account takeovers, APP fraud, and transaction splitting often succeed because risk systems lack contextual speed. For instance, criminals regularly manipulate sanctions screening by exploiting list mismatches and stale data. Improving sanctions list accuracy through better data management plays a crucial role in reducing false positives and catching true risk signals in motion.

Recent research in graph learning and transaction anomaly detection confirms this gap. A study on illicit accounts in e-payment networks found that machine learning models can accurately flag hidden laundering behaviour within milliseconds, well before traditional systems raise alerts.

The Case for Real-Time, Data-Centric Compliance

To meet this moment, AML systems must be rearchitected around real-time data ingestion and decisioning. This includes:

1. Contextual Behavioural Monitoring

Instead of fixed thresholds, firms should assess each transaction in the context of customer profile, history, location, device, and behavioural signals.

2. Integrated Risk Pipelines

Screening, monitoring, KYC, fraud, and sanction checks should operate through a shared decision engine, not fragmented systems.

3. Automated Screening Aligned to Payment Speed

Real-time alerting only works if the screening engine itself is real-time. Traditional systems introduce latency or friction during sanction checks. Real-time sanctions screening removes this lag by integrating sanction detection directly into the payment flow, ensuring compliance doesn’t hold up legitimate transactions or let suspicious ones slip through.

4. Cloud-Native Infrastructure

Elastic computing and real-time orchestration are essential for scaling with transaction volumes and maintaining system resilience.

5. Compliance as a Continuous Process

Move away from periodic checks and point-in-time reviews. Compliance should be always-on, embedded into payment flows, and responsive in milliseconds.

5. Graph & Deep Learning Models

Academic research shows how deep learning frameworks and graph-based anomaly detection can flag suspicious activity across high-volume digital networks, critical for environments where decision speed equals risk exposure.

Global and Regulatory Support for Real-Time AML

Governments and regulators are aligning around the need for faster, smarter compliance. The FCA’s financial crime guidance continues to stress that financial institutions must be able to detect, delay, or block high-risk transactions in real time.

At the same time, the UK’s Money Laundering Framework require firms to assess and manage risk on an ongoing basis, particularly for high-speed payment flows. Across Europe, the European Central Bank is leading infrastructure reform to ensure instant payments are backed by strong compliance controls.

Internationally, the Financial Action Task Force (FATF) has repeatedly emphasised the need for AML and CFT frameworks to evolve in response to real-time transaction environments, calling for data-driven monitoring and technology-led transformation.

Conclusion

Real-time payments demand real-time compliance. Static monitoring, delayed alerts, and outdated risk models will not protect institutions, or their customers, in an always-on, instant-settlement environment.

To stay ahead of fraud, meet regulatory expectations, and build trust in the modern financial ecosystem, firms must evolve their AML infrastructure. That means making compliance intelligent, data-integrated, and real-time by design, not applied as a reactive layer.