Financial crime is advancing more quickly than compliance systems can track. Banks and fintechs have traditionally depended on reactive investigations that identify suspicious transactions only after they occur. Now, AI is transforming anti-money laundering (AML) by moving it from a back-office function into the core of transaction processing. The distinction between success and failure in this transition lies not just in the AI models themselves but in how deeply they are integrated into a bank’s infrastructure.
Leading institutions integrate AI directly into their transaction pipelines, ensuring every payment is screened in real time before funds are released. Traditional banks, however, often attach AI models to legacy systems through external APIs, introducing delays that fraudsters exploit. As a result, fraudulent payments slip through, and institutions face losses they must recover afterward.
myTU, an agentic neobank built to make money management faster and smarter, takes a different approach. Its AI-native compliance operates during transaction authorization, processing checks in milliseconds. Unlike systems relying on rigid, static rules, myTU’s platform analyzes multiple signals simultaneously: device integrity, network context, unusual behavior patterns, and transaction velocity. If the system detects an attack spreading across accounts, it acts automatically—placing holds before settlement completes.
AI cuts fraud losses with precision
This shift from reactive to preventive compliance delivers measurable outcomes. Continuous, inline monitoring cuts manual reviews by over 60% while ensuring that more than 98% of legitimate customer payments proceed without friction. The fraud prevention impact is immediate: automated attacks are stopped before funds move, saving institutions hundreds of thousands of euros monthly. Precision matters as much as speed. Overly aggressive AI settings flood analysts with false positives—reviewing 100 alerts daily when 98 are harmless risks missing genuine fraud due to fatigue.
Human judgment remains vital but focuses only on high-risk cases. The AI’s role is to filter out noise, allowing compliance teams to concentrate on complex, organized financial crime rather than low-value alerts. This balance, automation for scale, human oversight for nuance, defines effective AI-driven compliance.
The primary challenge is not the technology itself but the underlying infrastructure. Many institutions overcomplicate their approach by prioritizing advanced models before ensuring data pipelines support real-time, contextual signals. Without clean, low-latency access to transaction data, even the most sophisticated AI fails to stop fraud before it occurs. The first step for banks and fintechs exploring AI in AML is auditing data pipelines and transaction architecture. If core systems cannot handle millisecond-level decisions, third-party AI layers will only detect losses after they materialize.
Regulators demand AI transparency and control
Regulatory confidence is another critical factor. New EU rules, including PSD3 and the Payment Services Regulation, emphasize consumer protection, clear liability, and fraud prevention. Institutions must prove that AI-driven compliance improves accuracy without compromising auditability. This requires precise safeguards, minimal false positives, and detailed audit trails for every automated decision. The goal is not just to innovate faster than criminals but to demonstrate that innovation aligns with regulatory expectations.
Criminals are also adapting. Authorized push payment (APP) scams, where victims are tricked into transferring money themselves, are rising. These attacks evade traditional fraud detection because the device, credentials, and location appear legitimate. The industry’s response demands two major changes: real-time intelligence sharing across sectors and network-wide tracking of mule networks. Fraud often begins on social media or telecom platforms before reaching banking apps. If payment providers, telcos, and social platforms shared data in real time, mule networks could be frozen before funds are laundered into crypto or moved offshore.
For institutions new to AI in AML, the advice is clear: start with the foundation. Before investing in advanced models, ensure data pipelines can feed contextual, device, and behavioral signals inline. Without this infrastructure, even the most powerful AI will operate too slowly to matter. The competition is not just about adopting AI but integrating it into the transaction flow, where every payment is checked before finalization.