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<\/p>\n
Coinbase has fine-tuned an open-weight AI model on its proprietary transaction data to outperform a leading closed-source model in fraud detection, with better detection scores and lower serving latency.<\/p>\n
In a three-part technical series, the exchange argued that companies can get better results from specialized models trained for their own needs than from relying on increasingly capable general-purpose AI systems.<\/p>\n
In an October 12 post, Coinbase CEO Brian Armstrong explained<\/a> that the company has fine-tuned Qwen3.5-9B using internal data and found that it “outperformed frontier models, at much lower cost and speed,” on the specialized task of fraud prevention.<\/p>\n The intended point was not that smaller models are universally better, but that performance depends on the job they are trained to do.<\/p>\n “The frontier models are amazing generalists,” Armstrong wrote. “But increasingly you’ll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.”<\/p>\n<\/blockquote>\n Coinbase’s Owning Intelligence series described how that approach evolved, with the company first adding an AI risk agent to its existing machine-learning model and rules engine for Onramp, which lets users buy crypto through partner applications, including guest checkout without a Coinbase account.<\/p>\n An online experiment found 30% fewer fraudulent transactions and 22% less fraud value in the group using the additional AI review. The agent examined recent transaction sequences to identify patterns that might not be apparent from a single payment. It could block a transaction that passed the initial checks, but could not reverse an upstream block.<\/p>\n Coinbase then tested newer versions of closed-source models against a fixed benchmark of 16,140 transactions across 7,293 users, including 813 confirmed fraudulent transactions. It reported that newer Opus, Sonnet, and GPT versions performed worse on several fraud-detection measures than their predecessors.<\/p>\n GPT-5.6 improved precision but recorded lower recall, meaning it identified a smaller share of fraudulent transactions. That result motivated the move to an open-weight model, whose parameters could be adapted directly rather than accessed only through a provider’s API.<\/p>\n Coinbase’s approach fits into a wider discussion about AI agents taking on financial crime and security work, with Certik warning<\/a> last week that more autonomous security agents could bring risks from incorrect outputs and reduced human scrutiny. The firm recommended clear limits on autonomous decisions and a named owner for every agent.<\/p>\n However, the crypto exchange’s agent works within such limits, returning a risk score and having application codes that apply the decision policy. It can also block a transaction that earlier checks allowed, but it cannot override a block that’s already been made.<\/p>\n The post Coinbase AI Agent Cuts Fraud by 30% in Onramp Experiment<\/a> appeared first on CryptoPotato<\/a>.<\/p>\n\n\n
Issue Flagged Over AI Autonomy<\/h2>\n
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