A CFA Charterholder Built an ML Fraud Detector: Here's What the Models Miss
I’m a CFA charterholder who builds ML systems. I trained XGBoost on 100K financial transactions to detect fraud — AUC 0.987. But the most interesting finding wasn’t the model performance. It was that CFA-informed rule-based scoring achieves 0.898 AUC on its own, and 8 of the top 20 predictive features come from domain expertise, not raw data. Here’s what happens when you bring financial analysis training to ML fraud detection. ...