Coinbase fine-tunes Qwen3.5-9B model for fraud prevention, beating frontier models

Coinbase has spent the past stretch teaching a relatively small AI model to catch fraudsters. According to the exchange, the small model now does that job better than some of the most expensive AI systems on the market. In its “Owning Intelligence” blog series, published October 7-8, 2026, Coinbase said a fine-tuned version of Alibaba’s Qwen3.5-9B model outperformed frontier models on fraud detection for its Onramp service. It was faster and far cheaper to train. What Coinbase actually built The project centers on an LLM-based fraud risk agent built specifically for Onramp, Coinbase’s service for buying crypto. The agent sits on top of Coinbase’s existing machine-learning models and looks at transactions that have already cleared those systems. The agent’s output is deliberately narrow. It returns a risk level and nothing more, so upstream systems keep running as they were. The results from A/B testing were meaningful. Coinbase said the agent cut fraudulent transactions by 30% and lowered the dollar value of fraud by 22%. The savings were estimated at three times the cost of running the model. The benchmark showdown To compare models, Coinbase built a proprietary Onramp fraud bench...