The AI Regulatory Pyramid A New Model for Fighting Future Crimes
- The study introduces an "AI-empowered regulatory pyramid" with three layers: capacity building (victim protection), restorative community policing (disrupting mules), and incapacitative policing (targeting syndicates)
- China employs all three layers effectively, while Southeast Asia focuses primarily on the bottom layer (capacity building) due to resource limitations, leading to a migration of crime groups to the region
- Technological solutions alone are insufficient; financial instability among gig workers drives the supply of money mules, making poverty alleviation a critical component of long-term crime prevention.
 |
| Photo by Charles Postiaux on Unsplash |
Read The Full Research Here
The rapid evolution of artificial intelligence has birthed a new era of "future crimes" that traditional law enforcement is ill-equipped to handle. AI-enabled offenses such as deepfake scams, synthetic identity fraud, and automated money laundering operate with speed and anonymity that render conventional regulatory frameworks obsolete. In response to this crisis, researchers have proposed a novel "AI-empowered regulatory pyramid" based on extensive fieldwork in China and Southeast Asia. This framework represents a shift from reactive policing to a dynamic, multi-layered defense system designed to combat the decentralized and adaptive nature of modern cybercrime syndicates.
At the foundation of this new pyramid lies AI-empowered capacity building, a strategy heavily relied upon in Southeast Asia due to limited state resources. This layer focuses on protecting potential victims through public-private partnerships with digital technology firms. Companies like Zoloz and various digital wallet operators deploy advanced identity verification systems, such as e-KYC (Know Your Customer) and liveness detection, to intercept scams before they succeed. By integrating AI risk analytics with targeted community education, authorities can identify high-risk zones and demographics, delivering precise anti-fraud warnings to vulnerable populations.
AI-Empowered Responsive Regulation for Preventing Future Crimes: An Empirical Inquiry into the Regulatory Pyramid to Combat Future Crimes in China and Southeast Asia
View on PublisherMoving up the pyramid, the second layer introduces AI-empowered restorative community policing, a method primarily observed in China. This approach targets the "money mule" networks that facilitate the flow of illicit funds. Instead of relying solely on punitive measures for these low-level accomplices—who are often financially desperate gig workers or unwitting participants—police use behavioral biometrics and real-time fraud recognition to disrupt their activities. For example, systems can detect abnormal login patterns or hesitation during transactions, triggering immediate alerts that allow community officers to intervene and freeze accounts within minutes, effectively cutting off the criminal supply chain.
The apex of the pyramid is AI-empowered incapacitative policing, designed to dismantle the organized crime syndicates themselves. This involves the use of predictive financial intelligence and machine learning-driven Anti-Money Laundering (AML) detection. By analyzing vast amounts of transaction data, law enforcement can identify complex laundering schemes involving cryptocurrencies and trade-based fraud. Advanced platforms, such as the K-AFA Digital Police Assistant in Zhuhai, utilize robotic process automation to freeze thousands of accounts and process cases in seconds, a task that previously took days.
Despite these technological advancements, the research highlights a critical socio-economic dimension often overlooked by purely technical solutions. Interviews with fintech executives reveal that financial instability is a primary driver for the recruitment of money mules. In China, millions of delivery riders and gig workers lacking stable income or social security are vulnerable to exploitation by crime syndicates. Without addressing the underlying poverty and lack of social safety nets, high-tech policing remains a temporary fix, suppressing symptoms rather than curing the root cause of the criminal ecosystem.
The disparity in state capacity creates a significant divide in how these strategies are implemented. While China utilizes all three layers of the pyramid, Southeast Asian nations often struggle to move beyond the foundational capacity-building stage due to resource constraints and cross-border jurisdictional conflicts. This uneven enforcement landscape has inadvertently led to the relocation of cybercrime groups from China to regions in Southeast Asia, turning them into new epicenters for transnational future crimes.
Effectively combating these threats requires more than just AI; it demands a holistic approach integrating technology with social reform. Long-term resilience depends on adaptive regulatory "sandboxes" to test new policies, robust cross-border data-sharing frameworks, and social media campaigns to educate the public on emerging threats like deepfakes. Ultimately, treating poverty alleviation as a cybersecurity imperative is essential to dismantling the human infrastructure that supports these high-tech criminal networks