Marco thought he’d found an easy side job. The offer came through social media — an opening for a “financial assistant” with quick money and flexible hours. His task sounded simple: receive bank transfers, forward the funds to company partners, and keep a small commission for himself.
But within weeks, Marco, a young man from Naples, was unknowingly laundering money for an organized crime network. He had become a money mule.
While Marco's story is hypothetical, it reflects a growing crisis across Italy, where billions in money laundering activity flows through unwitting accomplices each year. As digital payments accelerate and criminals adapt their tactics, Italy faces a silent epidemic that threatens the integrity of its entire financial system.
Bigger than you think
Money mules now form a key part of the invisible infrastructure behind modern financial crime. Often young, unemployed, or desperate for income, these individuals are recruited — sometimes unknowingly — to lend or rent their bank accounts be used to move illicit funds. As with Marco, they’re often paid small commissions and/or misled into thinking they’re engaged in legitimate employment.
Criminal organizations have professionalized this laundering process, employing convincing job ads, romance scams, and targeted social media campaigns to lure vulnerable individuals. Increasingly, they rely on AI-powered tools to identify potential targets, tailor messaging, and manage ongoing communication with recruited mules. These operations often mimic legitimate businesses, complete with onboarding processes, training materials, and even performance tracking.
The scale of the problem is staggering. Across Europe, hundreds of billions of euros in illicit funds flow through the continent’s financial institutions every year, accounting for 2.3% of the region’s total GDP. In Italy, where youth unemployment sits above 20% (six points higher than the EU average), the reach of sophisticated criminal networks, and the country’s role as a bridge between Europe and global trafficking routes have made it a strategic hub for money laundering.
Online scams have fueled the crisis, leading to massive financial losses. In Italy, losses from authorized fraud, where victims are tricked into making payments themselves, now exceed those from unauthorized transactions by a factor of 13. This shift reveals a critical gap in many legacy fraud detection systems, which are designed to block unauthorized access but remain largely ineffective against scams that manipulate victims into transferring money themselves.
Detecting deception through behavior
Traditional anti-money laundering systems struggle with money mule detection because these mule accounts often appear legitimate at first. Mules typically have valid identities, established banking relationships, and normal transaction histories before they're recruited. The challenge lies in spotting the subtle behavioral changes that signal when a legitimate account goes bad and starts to move illicit funds.
This is where behavioral intelligence plays a critical role. Unlike legacy, rules-based systems that flag obvious anomalies — such as unusually large transfers or sudden spikes in account activity — behavioral detection systems identify patterns in how users interact with their banking platforms. Subtle changes in typing rhythm, navigation patterns, or transaction timing can indicate when an account holder is acting under external influence rather than independently.
By analyzing thousands of behavioral data points in real time, these systems can spot inconsistencies that reveal when someone is being coached through a transaction, using an unfamiliar device, or accessing an account under duress.
Mapping the mule network
Effective mule detection goes beyond monitoring individual accounts. It requires identifying broader network patterns. Criminal organizations often manage multiple mules simultaneously, creating transaction chains that advanced analytics can trace. By analyzing connections between accounts, transaction timing, and geographical patterns, financial institutions can map entire mule networks rather than just flagging individual suspicious accounts.
This network-level detection is particularly important in Italy, where organized crime groups operate sophisticated laundering operations that span multiple regions and institutions.
The regulatory response
Italian regulators have recognized the growing threat of money mules. The Bank of Italy has proposed expanding anti-money laundering regulations to include crypto-asset service providers, acknowledging that criminals are increasingly using digital assets to obscure money trails.
The European Anti-Money Laundering Agency (AMLA), which began operations in July 2025, is now strengthening cross-border coordination through concrete mechanisms. AMLA's Support and Coordination Framework creates a central hub for Financial Intelligence Units in member states, helping them jointly analyze cross-border intelligence and coordinated case investigations. AMLA also conducts thematic reviews and joint market analyses, particularly in high-risk sectors like crypto assets, to ensure consistent enforcement standards across jurisdictions where money mule networks often operate.
However, regulatory frameworks alone cannot solve the mule problem. The challenge requires proactive detection systems that can identify suspicious behavior patterns before illicit funds complete their journey through the financial system.
The path forward
Still, regulation alone isn't enough to solve the money mule problem. The future of detection lies in combining advanced behavioral analytics with network analysis and artificial intelligence.
Italy's money mule crisis reflects a broader global challenge. As payments become faster and more digital, criminals are finding new ways to exploit human behavior, not just technical loopholes. Traditional security measures remain necessary, but they're no longer sufficient.
What’s needed is a shift from focusing solely on system defenses to understanding how people behave within those systems. By analyzing user interactions, institutions can detect signs of manipulation and coercion, even when credentials and transactions appear legitimate.
In the fight against money laundering, the strongest defense isn’t just tracking the money. It’s understanding the people behind it.
