**Class imbalance**: Fraudulent transactions represented only 0.12% of all transactions. The team used SMOTE oversampling and cost-sensitive learning to address this. - **Feature engineering at scale**: Computing real-time features (e.g., "number of transactions in the last hour") required a streami