
Google DeepMind
· 1 min read
Google DeepMind and Singapore Government announce new AI tool to protect citizens against scams
October 14, 2026 Responsibility & Safety
JJ Geewax and Miguel de Andrés
The collaboration delivers an on-device scam detection model that helps protect citizens without compromising privacy.
Scams make up over half of Singapore’s reported crimes, and account for more than S$4 billion in losses since 2019. At the same time, scam tactics have shifted from simple phishing to complex social engineering, whereby attackers manipulate victims with convincing conversations before requesting money. Such scams now account for up to 80% of all losses.
To address this, Google DeepMind and the Government Technology Agency of Singapore (GovTech) have been working together as part of our National AI Partnership with the Singapore Government. This has allowed us to deliver an on-device scam detection model - optimized to run on typical smartphones in Singapore - to tackle an urgent safety challenge while preserving citizens' privacy.
On-device AI protection without compromising privacy
Generic models often struggle with localised deception techniques, making scam detection harder. To address this, Google DeepMind's lightweight Gemma model was fine-tuned on local scam conversations. These ranged from impersonations of public institutions, banks, and local figures, to trending regional schemes, ensuring the model can distinguish benign social chatter from genuine manipulation.
This new model also processes conversation data on the device. Traditionally, threat detection relies on server-side scanning of messages in the cloud, but many messaging platforms are end-to-end encrypted - meaning they cannot inspect private conversations. People need a way to stay safe from fraud at the same time as keeping conversations private, and our solution is to bring the threat analysis to the device. This allows faster analysis of conversation patterns to detect manipulative intent and allow citizens to act before financial or credential exposure can occur.
Original source
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