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Alyssa Iyer, Head of AML – Lynx Tech Learn moreWhy predictive intelligence, not just rules, is the future of fraud prevention
How Argentina’s Policy Impacts AML Controls
Alyssa Iyer
Argentina’s latest financial policies are making headlines, particularly the government’s effort to draw hidden dollars out of the shadows and into the formal banking system. Central to that effort is a significant shift: raising the thresholds for reporting large cash deposits and purchases of real estate or vehicles. But what does this mean for banks and why should everyday citizens care?
To encourage Argentines to deposit previously undeclared cash, the government has nearly doubled the reporting threshold. Under the updated rules, banks are now required to notify authorities only when a person deposits more than 40 times the monthly minimum wage in cash, as outlined in Ley 25.246. Similar adjustments have been made for cash-intensive purchases, such as property and vehicles. Additionally, financial institutions can no longer rely solely on tax returns to confirm a customer’s identity during onboarding.
At first glance, these resolutions might seem to indicate that regulatory requirements are relaxing. But the foundational obligations under Argentina’s anti-money laundering (AML) regime remain firmly in place. Banks are required under Argentina’s AML regulation to monitor all their customers’ transactions, including all cash transactions, not just those at or above the government reporting threshold. And they must investigate transactions that deviate from expected financial behavior. Though changes will need to be made to policies and systems to reflect the new reality, the foundational AML requirements remain intact.
This continuity is especially important in light of broader economic policy shifts. The government’s cash normalization plan is expected to increase the flow of physical currency into the banking system, reintroducing cash into formal channels. While this move can support financial inclusion and economic stability, it also heightens exposure to the inherent risks of cash: limited traceability and a degree of anonymity that make it attractive for money laundering.
It’s important to recognize that holding cash is not illegal. Many individuals do so for entirely legitimate reasons, such as safeguarding their savings against inflation. However, cash may in some cases originate from illicit sources, and individuals involved may be unwilling to provide information that could reveal the true origin of their funds. To avoid detection, some may engage in “structuring”, a practice involving repeated cash deposits just below mandatory reporting thresholds or spreading deposits over several days.
These behaviors are among the high-risk typologies outlined in Resolution 14/2023, which remain relevant in today’s context. For financial institutions, having a comprehensive view of the customer across behavior, transaction history, and expected activity can support stronger assessments of whether the volume, frequency, and patterns of cash movement reflect legitimate use or indicate elevated risk.
Furthermore, because banks can no longer rely on tax documents for client onboarding or verification, they must adopt alternative methods to confirm a customer’s identity and financial profile. In addition to Know Your Customer (KYC) practices, critical components of that holistic risk profile include understanding the customer’s line of business to better gauge the potential source of funds, as well as screening customers for sanctions, adverse media or PEP (Politically Exposed Person) status. Having these insights helps to assess whether cash activity aligns with the customer´s risk profile and source of funds.
These policy changes present both an opportunity and a challenge. They offer a chance to bring more funds into the formal economy, while also requiring financial institutions to sharpen their oversight. These changes may prompt banks to review their AML controls, enhance customer due diligence and invest in tools that provide an understanding of customer behavior and risk in an effective manner.
By taking a proactive, risk-based approach, financial institutions can help ensure that this new chapter in Argentina’s financial policy leads to sustainable growth, not unintended exposure.
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Emerging AI technologies are transforming the fight against financial crime with real-time detection and prevention capabilities, improved risk scoring, and reduced false positives. New paradigms define which solutions make the largest impact in protecting global financial systems: AI-enabled technology must not only demonstrate accuracy but also must meet explainability and regulatory demands to resonate with customers. Trust, transparency, and privacy are at a premium.
AI is Transforming Financial Crime Prevention
In the age of real-time financial crime, reactive and rigid approaches are no longer viable. Fraud and money laundering can occur digitally in just seconds. Illicit accounts and payments operate in real time. Constant watchlist changes, complex screening demands, and stringent AML regulations add to the challenges. AI technologies present a path forward to staying a step ahead of criminals. Pre-crime platforms proactively address financial crimes by using AI to analyze historical data and identify criminal patterns in real-time data. However, these innovations face several obstacles. AI and machine learning (ML) technologies must integrate a vast amount of threat intelligence data from varied sources in real time. Pre-crime solutions must also be tailored to meet industry and organization-specific needs. At the same time, robust AI data governance is necessary to ensure that AI models use high-quality training data in an explainable manner to mitigate bias, excessive false positives, and unintended risks. Privacy and transparency have become essential to developing trust, a key element of successful adoption and impact. Lynx’s AI-enabled solutions proactively detect financial crimes, with data governance and model transparency at the foundation of our approach.Enterprise-Wide Fraud and Money Mule Detection with Daily Adaptive Models and Lynx Flex
Lynx Fraud Prevention and Money Mule Account Detection leverage Daily Adaptive Models (DAMs), self-learning AI models that update daily to stay ahead of the latest fraudster and money mule techniques, customer behaviors, and payment technologies. DAMs utilize supervised ML to learn from past fraud and money muling and, once deployed, detect suspicious transactions and customer patterns in real time. DAMs are tailored to each organization and deliver highly accurate real-time risk scoring with low false positives. Lynx’s approach to AI model training prioritizes strong data governance. We curate high-quality training datasets from each organization’s transactions, and our efficient training algorithm performs feature selection, allowing DAMs to learn from all available features to mitigate bias and improve performance over time. Our solutions comply with PCI-DSS, ISO 27001, and SOC2 standards to ensure security and privacy. Lynx’s solutions address the critical need for unified enterprise-wide detection as well. Our Flex technology enables dynamic payloads: organizations can incorporate new payment channels in under 60 minutes, propagating new data fields to models, rules, and reports. The combination of DAMs and Flex provides a fully configurable 360-degree view of risk across all transactions, channels, customers, and devices: a centralized detection platform integrating all fraud and money mule threat intelligence and data.Lynx’s AI-Enabled AML Technology: Explainable Real-Time Performance and Stronger Compliance
Lynx’s AI-enabled AML solutions are architected for transparency and explainability to address ‘black box’ and compliance concerns while optimizing performance. Our AML modules maintain a clear audit trail to meet regulatory requirements and are fully configurable for risk-aligned outcomes. Our Customer Screening and Payment Screening modules utilize a sophisticated AI-enabled name screening model that intelligently applies natural language processing (NLP) algorithms based on each incoming name’s structure. Organizations can select screening methodologies and develop rules aligned with their risk appetite. Our screening solutions provide highly accurate real-time name similarity scoring and lower false positives. In addition, Lynx’s innovative ‘AI with guardrails’ approach allows organizations to implement risk-based AML strategies aligned with their specific needs and regulatory demands. Our Tailored Delta List module filters watchlists and generates custom delta lists based on the company’s policies and risk tolerance, ensuring accurate watchlist reconciliations. Plus, our AML Transaction Monitoring module offers configurable unsupervised ML models and data agnostic integration for enhanced anomaly detection. Organizations can create and immediately deploy complex rules to assess unusual transaction patterns, resulting in risk and compliance-aligned outcomes.Request a Proof of Concept
Our AI innovations and our collaboration with organizations to prevent financial crimes and safeguard global financial systems. This is further proven by the success of our customers’ experience. Companies working with Lynx save $1.6B annually* using our advanced AI-enabled fraud, money mule, and AML solutions. Ready to take the next step and transform your financial crime detection and prevention strategy? Reach out and schedule a Proof of Concept (POC).*12 month trailing
Gartner, Emerging Tech Impact Radar: 2025, Tuong Nguyen, Danielle Casey, 23 January 2025 Gartner Disclaimer GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designations. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.Lynx Tech launches Adaptive AI to tackle rising fraud
Lynx rolls out new fraud prevention models
Lynx Launches Next-Generation Fraud Prevention Models
- Lynx unveils next-generation Daily Adaptive AI models, shifting from individual transaction analysis to detecting entire fraud networks.
- The new technology has achieved a 460% uplift in money mule value detection and identifies up to 35% more Authorized Push Payment Fraud (APPF), while reducing false positives, according to internal testing.
- Unlike traditional static models, Lynx’s solution continues to improve over time, adapting to new fraud patterns.
“This breakthrough is the culmination of two years of rigorous research. We’ve developed state-of-the-art in-memory databases, algorithms, and low-level code that make our solution incredibly fast to integrate while keeping costs down and downtime at zero. In internal tests, our technology has demonstrated the ability to analyze up to 40,000 data points in real time to identify fraudulent activities with remarkable precision, representing a quantum leap in detection.”The upgraded models have demonstrated significant improvements in performance:
- Enhanced understanding of financial behaviors across all accounts connected to a financial institution, leading to a substantial reduction in false positives and an increase in the value detection rate.
- Improved adaptability through DAMS ensuring sustained performance over time and quick responses to new fraud tactics.
- Superior performance compared to static consortium models, particularly in detecting APPF.
“The next-generation models will make a big difference in preventing Authorized Push Payment Fraud. We can now spot tricky fraud patterns long before they cause harm. This means we can help banks protect their customers’ money better than ever, staying significantly ahead of sophisticated fraudsters. With the UK’s new APPF reimbursement regulations, it’s imperative that banks embrace emerging technologies such as AI to fortify their fraud detection systems.”To accompany the launch of its next-generation fraud models, Lynx has published a whitepaper on its Daily Adaptive Model. To read more, please visit https://landing.lynxtech.com/daily-adaptive-model-whitepaper.