1 link tagged with all of: fine tuning + ai + inference + fraud detection
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Coinbase fine-tuned Qwen3.5-9B on their internal fraud data and got better results than Claude Opus 4.5 on fraud detection, with 55% faster latency and at a fraction of the cost. The takeaway: smaller models trained on your own data outperform frontier models on narrow, specialized tasks.
- Qwen3.5-9B fine-tuned on Coinbase's fraud data outperformed Claude Opus 4.5 on their private benchmark, with latency dropping from 1.515 to 0.683 seconds
- Live A/B testing showed a fraud detection agent using the fine-tuned model reduced fraudulent transactions by 30% and fraud value by 22%, saving about 3x the inference cost
- Frontier models scored lower on recall and F1 for this specific task, suggesting specialized models beat generalists on narrow high-stakes problems