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Knowledge Challenge
A friend thinks you can answer this question about LLM vs Traditional ML Decision
Your fraud team wants to score 50M card transactions/day for fraud risk. Each transaction has 80 structured features (amount, merchant category, time, location, card-history aggregates). They're considering: (A) GPT-4o on each transaction, (B) a fine-tuned Llama 3.1 8B, (C) a gradient-boosted tree (XGBoost/LightGBM), (D) a regex rules engine. Which architecture should you ship?