Using Large Language Models to Disrupt Scammers in Southeast Asia

An aspiring elderly author named Tony Sacramoni contacts a sales representative at a publishing company to pitch his manuscript, a supposed “bestseller in the works” titled How I Learned to Eat Soup. During the call, he repeatedly derails the conversation with bizarre requests. After an hour, the frustrated sales representative disconnects.

Neither party is genuine in the above example: the ‘sales representative’ was a scammer from a call centre likely based in India or Pakistan. ‘Tony Sacramoni’ was an AI-generated character created by Western Amber, a Large Language Model (LLM)-driven scambaiting system developed by YouTuber Kitboga and his team. Trained on voice and speech data from consenting volunteers, Western Amber autonomously calls known scam centres while convincingly mimicking elderly scam victims. It can engage multiple scammers simultaneously with minimal human input in tedious conversations intended to waste the scammers’ time.

In North America, scambaiting has grown into organised disruption operations led by YouTubers like Kitboga and Scammer Payback. What began as prank calls and meticulous trolling campaigns has gradually expanded into technology-driven approaches like Western Amber to automate scambaiting at scale, occupying the attention of multiple scammers and even entire scam centres. This directly targets the economic viability of scam operations, as scammers depend on high volume and efficiency to be profitable, and thus rely heavily on mass outreach to identify a small number of vulnerable victims. Therefore, the more time scammers spend interacting with fake victims, the fewer opportunities they have to reach genuine targets.

Figure 1: Multiple ongoing calls by Western Amber to a single scam call centre.

An AI-driven scambaiting approach may be particularly promising in Southeast Asia, where massive scam compounds in Cambodia and Myanmar depend on high call volumes and scripted workflows to swindle victims at scale. For instance, scam syndicates in Southeast Asia caused losses totalling MYR 2.7 billion (USD 680 million) in Malaysia and SGD 913.1 million (USD 714 million) in Singapore in 2025. Such operations rely on tactics like robocalls impersonating companies or government agencies to provoke financial or legal anxiety, prompting victims to call back and be swindled. Like their counterparts in India and Pakistan who prey on Westerners, scammers in Southeast Asia employ structured scripts, multilingual capabilities and social engineering tactics tailored to local contexts to prolong interactions and pressure victims.

Unlike North America, however, scambaiting in Southeast Asia remains anecdotal. Netizens in online communities such as r/askSingapore and r/Malaysia occasionally share stories of prolonged exchanges with scammers, but such efforts rarely disrupt organised scam operations. Regional governments rely primarily on defensive measures such as public education campaigns and financial safeguards to protect potential victims. Unlike scambaiting YouTubers or irritated civilians, though, public servants cannot realistically engage individual scammers in prolonged calls, as this would neither be an efficient use of public resources nor be consistent with the professionalism expected of state institutions. Moreover, scam syndicates often operate outside the territorial jurisdiction of local law enforcement, despite ongoing regional cooperation between countries to crack down on scams.

Rather than relying on manpower-intensive individual efforts, these AI-driven systems can automate disruption at scale and quietly operate alongside existing defensive anti-scam technologies…

Thus, the adoption of LLM-driven scambaiting systems may represent a potential breakthrough. Rather than relying on manpower-intensive individual efforts, these AI-driven systems can automate disruption at scale and quietly operate alongside existing defensive anti-scam technologies, such as Singapore’s ScamShield. Law enforcement agencies, such as the Singapore Police Force’s Cyber Command, are particularly well-positioned to explore this approach, because they possess the institutional capacity, technical expertise and legal authority to responsibly develop and deploy such systems.

Localisation is essential for AI-driven scambaiting systems to work in Southeast Asia. Scambaiting LLMs would need to reflect the region’s linguistic and cultural diversity, including languages such as English, Malay and Mandarin, alongside dialects and colloquial speech patterns that scammers commonly exploit. The system would also require realistic victim archetypes tailored to local scam patterns. Scammers vary their tactics when targeting different demographics, such as elderly individuals, young professionals, or non-English speakers. Without such localisation, AI-generated characters will struggle to sustain engagement with experienced scammers.

The creation of effective AI-generated ‘victims’ or characters would therefore require not only technical sophistication, but also behavioural insights into how the different demographic groups may communicate and respond under pressure. Moreover, voicing these characters will require substantial voice datasets collected from volunteers. Given the widespread public frustration with scams across Southeast Asia, governments may be able to attract volunteers if appropriate safeguards for transparency, consent and data protection are established.

However, LLM-driven scambaiting should not be viewed as a replacement for existing government anti-scam measures. Public education, financial safeguards and regional cooperation via joint law enforcement operations remain essential as the primary defence against scams. AI-assisted scammer disruption should aim to complement existing strategies by making it significantly harder for scammers to find real victims.

In Southeast Asia, LLM-driven scambaiting could potentially emerge as an effective tool in national efforts against organised scam operations. There are legitimate concerns surrounding the adoption of scambaiting systems, such as diplomatic complications due to major scam operations being linked to prominent Southeast Asian political leaders, and the possibility of scammers adapting their tactics in response to AI systems being used against them. However, as scams become increasingly industrialised and sophisticated, Southeast Asian governments will need to explore more proactive and creative tactics to support existing defensive measures.

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