- Built a multi-class fraud detection system processing 99% imbalanced transaction dataset, engineering 5+ features including balance mismatch flags and error signals to capture data inconsistencies and suspicious patterns. - Implemented ensemble Random Forest classifier with class weighting and hyperparameter tuning (tree depth, splits, samples), optimizing for Macro F1-score to achieve equal performance across 4 urgency classes and winning 1st place