Issue #8 - How Regulators Are Using AI to Identify Suspicious Activity — And What That Means for You
Issue #8 | Week of 23 April 2025
🧊 Introduction
Hi there,
Here’s your weekly download of need-to-know updates in financial crime.
This edition dives into something that’s fundamentally changing the game: regulators across the globe are turning to artificial intelligence (AI) to sniff out illicit financial activity faster and more effectively.
From Washington to London to Singapore, authorities are putting machine learning to work, uncovering suspicious behaviour that might have otherwise slipped through the cracks. If you’re in compliance, this isn’t just a fascinating tech story. It’s something that could reshape how you manage risks, respond to regulations, and prepare for audits.
The message is loud and clear: the way enforcement happens is shifting. It’s time to adapt.
Here’s what you’ll find inside this week’s issue:
📌 Top Story – A deep look at how regulators like FinCEN, FCA, and MAS are using AI for financial oversight, and what this means for compliance teams.
🌍 Regulatory Roundup – Key updates from around the world, including U.S. AI initiatives, new EU AML plans, and tougher crypto oversight in APAC.
🔎 Case Study – The story of “Operation Chimera,” where AI helped crack a money laundering ring using synthetic IDs and crypto.
🧰 Compliance Toolkit – Practical guides, webinars, and alerts to help you stay ahead of AI and AML (anti-money laundering) challenges.
📌 Top Story
How Regulators Are Using AI to Identify Suspicious Activity
Here’s what’s happening:
Financial regulators are stepping up their oversight game by leaning into AI and machine learning.
FinCEN in the U.S. is testing algorithms on the millions of suspicious activity reports (SARs) submitted every year. These machine learning tools can spot subtle patterns and complex networks that are often missed by manual processes.
The UK’s FCA has ramped up efforts with tools like network analysis and entity resolution, even encouraging banks to upgrade their transaction monitoring systems to include AI capabilities.
Over in Singapore, MAS is taking a big-picture approach, analysing mountains of banking data and scanning media reports with AI to pinpoint red flags worth investigating.
By adopting advanced tech, regulators can sift through noise and zero in on bad actors with speed and precision.
Why This Matters
This is a seismic shift in how compliance operates. If the regulators are using AI to raise the bar, financial institutions need to keep up. Here’s why:
Tighter Scrutiny: AI gives regulators sharper eyes. You might get flagged for a pattern or anomaly that wouldn’t have been obvious to a human examiner. Brushing up on proper documentation and having a plan for fast investigations will be critical.
Investment in Tech: If your systems are still relying on outdated rules-based monitoring or manual reviews, it’s time to upgrade. AI-driven systems can help you discover issues before regulators do, bridging the gap and keeping you compliant.
Data Challenges: AI is only as good as the data it’s fed. Regulators may push institutions to improve data quality and could even require more data sharing. This will trigger discussions about privacy and how to explain what AI models decide.
What Industry Leaders Are Saying
“Money launderers operate across different financial institutions, and you need to be able to join the dots across them,” said Ravi Menon, Managing Director of the Monetary Authority of Singapore.
The takeaway? AI is a must-have tool for connecting pieces no human could easily match up.
The Local Perspective: Australia’s AUSTRAC is making similar moves. Through its Fintel Alliance, it’s piloting tools that read into data from multiple institutions. This effort helps to identify shared bad actors while maintaining data privacy. Local banks should be ready for Australia’s regulators to go all in on AI too.
🌍 Regulatory Roundup
Here’s what’s shifting around the globe this week in financial oversight.
AMERICAS
The U.S.: FinCEN launched a “Financial Crimes AI Lab” to develop cutting-edge models for spotting financial crime buried deep in Bank Secrecy Act (BSA) data. Meanwhile, U.S. Bancorp settled with the DOJ for $75 million after failing to act on algorithmic warnings about crypto transactions.
Key takeaway? Ignoring red flags isn’t an option.
EUROPE
The EU: A new anti-money laundering authority (AMLA) was approved and is set to start in 2026, with sweeping supervisory powers. It will heavily lean on AI to oversee risky firms. On enforcement, the FCA fined a fintech £5 million for ignoring known issues in its automated monitoring system.
Stay sharp on system fixes; regulators are watching.
ASIA/OCEANIA
Hong Kong: Regulators are holding crypto exchanges to higher standards under a new licensing regime, with penalties for those who fall short.
Australia: AUSTRAC flagged misuse of fintech platforms for laundering and hinted at deploying more AI for rapid detection of suspicious digital payments.
🔎 Case Study
Operation Chimera – Breaking a Synthetic-Identity Crypto Ring
Earlier this year, an international ring used synthetic IDs and cryptocurrencies to launder more than $50 million.
Here’s how it worked:
Criminals created fake personas by combining stolen and fake identity details.
These identities were used to open dozens of bank accounts across multiple countries.
The funds were shuttled through these accounts, moved to crypto exchanges, and passed through mixers to mask their origins. From there, the cleaned money entered shell companies.
What brought them down?
One European bank’s machine-learning system picked up odd patterns. About 30 accounts had similar behaviours, like cycling funds through crypto at strange hours and keeping just under reporting thresholds. The AI also noticed that some accounts shared device IDs, while others had phone numbers that looked like part of a sequence.
Human analysts might not have caught these connections. But the AI saw the bigger picture and escalated the findings to law enforcement. From there, public-private collaboration revealed the full size of the operation, leading to arrests and asset seizures worth millions.
Lessons to Take Away:
AI shines where traditional systems might miss out. It can spot complex webs of activity and hint at possible security flaws.
Sharing intel across banks, law enforcement, and regulators is key. Your findings could be part of a bigger case, so communication matters.
🧰 Compliance Toolkit
Here are tools and resources you can use today:
Deepfake Fraud Alert (via FinCEN): This guide walks you through spotting signs of ID fraud using deepfake technologies. Perfect material for fraud teams to review and update processes.
Link https://www.fincen.gov/sites/default/files/shared/FinCEN-Alert-DeepFakes-Alert508FINAL.pdf
Webinar on AI for AML: Watch an on-demand session featuring experts sharing insights on using AI in compliance while meeting regulatory requirements. It’s practical and less than an hour long.
Link https://www.acams.org/en/training/webinars/keeping-an-ai-on-aml-efficiency-webinar
EU AML Changes: Get familiar with pending reforms for crypto oversight, harsher fines, and streamlined data-sharing. Delays in preparation could hurt later.
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