Strengthening digital trust in Asia Pacific: preventing scams through shared intelligence

Fraud and scams have become one of the most significant threats to digital trust across Asia Pacific, fuelled by organised crime gangs adopting AI-powered phishing, deepfakes, social engineering and increasingly sophisticated cross-platform attack models.

As fraudsters simultaneously exploit legitimate infrastructure such as telecoms networks, digital platforms, financial services and other digital channels, traditional siloed approaches to detection and prevention are proving inadequate. No single organisation has complete visibility of the scam lifecycle, creating information gaps that fraudsters can exploit. As a result, scam prevention is increasingly being recognised as a shared responsibility that requires coordinated action by digital ecosystem participants.

Telecoms operators can play an important role within this ecosystem, especially because operators have direct contact with victims (though scams affect the entire digital ecosystem). Through access to network intelligence such as call records, SIM-swap activity, messaging patterns and device signals, operators often observe scam indicators earlier in the attack chain. When combined with intelligence from other ecosystem participants, telecoms signals can support earlier detection, improve risk assessment and enable more proactive intervention against scams. However, unlocking this value requires trusted, scalable and sustainable mechanisms for data sharing that address challenges around trust, interoperability, incentives, privacy and regulation.

The GSMA Data Exchange Proof of Concept provides valuable evidence that privacy-preserving telecoms data sharing is both feasible and effective. It highlights four critical foundations for sustainable ecosystem development: governance frameworks built on trust and accountability; privacy-preserving approaches that enable collaboration without exposing sensitive data; operational models focused on data quality, scale and timing; and commercial frameworks that reward prevention outcomes. Importantly, the commercial opportunity is not limited to data sharing itself. Telecoms signals can potentially be transformed into actionable risk intelligence, including indicators, scores and preventative capabilities that create value for participants while supporting earlier scam prevention. These learnings provide a blueprint for evolving telecoms data sharing from isolated pilots into trusted, ecosystem-wide scam-intelligence infrastructure.

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