In a release shared with Bitcoin.com News, Nexo disclosed that its system is default-on and uses contextual analysis tied to blockchain security integrations. When elevated risk is detected, the platform surfaces plain-language prompts that let users review details before proceeding. The crypto company says interventions are “right-sized,” aiming to avoid unnecessary friction while reducing exposure to fraud.
The Anti-Scam Engine rollout targets scams that blend social engineering and cross-border tactics, including so-called pig-butchering schemes, romance ploys, tech-support impersonation, and high-yield Ponzi-style pitches. Nexo cites that Chainalysis estimated crypto scam losses at roughly $9.9 billion in 2024, illustrating the scale of the problem the tool is meant to address.
The Anti-Scam Engine currently supports activity on networks like Ethereum, Optimism, BNB Chain, Polygon, Arbitrum, Avalanche, and Base. A phased expansion to Bitcoin, Solana, Tron, and XRP is underway, Nexo told Bitcoin.com News, extending the tool’s coverage across several major networks.
According to Nexo, alerts appear only when specific risk signals are present, and most transactions proceed without interruption. In limited instances of elevated risk, transfers may be paused for a quick review to prevent potential losses.
The company frames the upgrade as part of a broader effort to lower operational risk while protecting client assets. The system is powered by external intelligence feeds and is designed to update as new threat patterns emerge.
Nexo, founded in 2018, operates a platform for crypto savings, trading, lending, and card services. The firm reported more than $11 billion in assets under management (AUM) and $371 billion in processed volume across more than 150 jurisdictions.
The company did not disclose technical partners, model specifics, or a timeline for full multi-chain coverage beyond the named networks. Industry participants will watch whether targeted prompts meaningfully reduce successful fraud without increasing user friction.
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