When it comes to financial crime compliance, few topics spark more skepticism than real-time screening. The myth goes something like this: AI may be smart, but it’s too slow or too heavy to work in real-time, especially for high-speed, high-volume environments like instant payments.
Let’s bust that myth once and for all.
The speed misconception
As digital payment volumes explode—with global real-time transactions reaching 266 billion in 2023, and projected to surpass 575 billion by 2028, according to an ACI Worldwide report, institutions are rightly focused on one thing: speed. Payment corridors are accelerating, regulatory pressure is mounting, and customers expect frictionless transactions. The assumption is that AI, with its need for training data, models, and compute resources, can’t move fast enough to screen at wire-speed.
This view might have been true a decade ago. Today, it’s outdated.
Real-time AI screening isn’t a theory, it’s already in production
Let’s take Brazil-based Travelex as a case study. Operating under the tight scrutiny of the Banco Central do Brasil, Travelex needed a solution that could support real-time screening across tens of thousands of clients and transactions per minute, without breaking compliance or customer experience.
ThetaRay’s AI Screening engine processed 30,000 clients per minute during proof-of-concept testing, a staggering volume made possible by cloud-native infrastructure and AI models optimized for high-throughput screening. The false positive rate was slashed by 10x, reducing the alert load from thousands to just a few hundred high-quality hits.
The low false positive rate was the key here, because what’s the point of flagging a transaction in milliseconds if your team is stuck investigating alerts that lead nowhere?
Our AI flipped that script. Traditionally what’s slowed down things for transaction and customer screening: false positives. In traditional screening environments, up to 95% of alerts are false positives. These clog up compliance queues, delay payment flows, and erode the customer experience.
In the Travelex deployment, reducing false positives by 90% empowered compliance teams to focus on real threats, not noise.
Oh and integration? Done in just two weeks. According to Travelex’s AML Data Manager Edson dos Santos Almeida Jr., it was “the fastest implementation” he’d seen in a decade.
AI Can be Fast — if it’s built for screening
Not all AI is created equal. Legacy machine learning systems require batch processing, model retraining, and manual overrides that make them clunky in live environments. But next-gen systems like ThetaRay’s are purpose-built for near real-time risk detection, and they do it by:
- Screening at the transaction level — down to every field and narrative
- Prioritizing risk based on real-time scoring and dynamic risk factors
- Updating continuously to reflect regulatory changes and emerging threats (from new OFAC designations to geopolitical red flags)
This is more than traditional name matching. It’s dynamic, adaptive screening that fuses global sanctions lists, PEP lists, adverse media, and proprietary intelligence into a unified, explainable alert engine.
Regulatory confidence in AI is growing
But let’s also talk about the elephant in the room: compliance risk. Isn’t real-time AI too “black box” for regulators?
Not anymore.
Across jurisdictions, regulators are embracing AI as long as it’s transparent, explainable, and auditable. ThetaRay’s model, for example, provides traceable logic, feature impact scores, and an auditable decision trail, meeting expectations from bodies like the FATF, FINCEN, ACPR, FCA, and more.
We’ve also seen regulatory frameworks, like the EU’s AML package and Singapore’s MAS guidelines, begin to recognize the role of AI in real-time payment integrity, especially as the threat landscape becomes more automated.
It’s not just about speed, it’s about smart speed
The promise of real-time payments shouldn’t come at the cost of compliance. And with scalable, cloud-native AI financial crime compliance tools, it doesn’t have to.
AI screening engines like ThetaRay’s can process thousands of transactions per second, detect risks across multiple geographies, and integrate seamlessly into core banking platforms, with RESTful APIs that support both batch and real-time models.
Institutions can grow without crashing their compliance frameworks. In fact, Travelex reported 30–40% business growth after deploying ThetaRay’s solution, proof that faster doesn’t have to mean riskier.
Let’s stop asking whether AI can handle real-time payments. It already is.
The real question is: Can your current screening system keep up with regulators, criminals, and your customers’ expectations?
If not, it’s time to identify and avoid blind spots in your screening system. Read our comprehensive Guide to Testing and Auditing to embrace a future where real-time compliance is not just possible, but proven.
🔗 Want to see how near real-time AI screening works at scale?
Read more on how AI powers rapid AML efficiency and growth at Travelex in this case study, or contact our team for a demo.
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