What are the best practices for handling imbalanced classes in a fraud detection model?

All answers to this question.

First off, stop looking at "Accuracy" immediately! For fraud detection, you should be focused on the Precision-Recall curve and the Area Under the Precision-Recall Curve (AUPRC). As for the data, I’d recommend starting with SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic examples of the fraud class. However, be careful not to oversample before your train-test split, or you'll leak information. Another powerful approach is using "Cost-Sensitive Learning," where you tell the algorithm that misclassifying a fraud case is much more "expensive" than a false alarm. Most Scikit-Learn classifiers have a class_weight='balanced' parameter for this.

   Answered 2025-11-05 by Megan Foster

Are you currently using a tree-based ensemble like XGBoost or LightGBM? These often have internal hyperparameters specifically designed to handle scale-positives. Also, have you considered an Anomaly Detection approach rather than standard classification?

   Answered 2025-11-08 by Kevin Bradley

• Kevin, I am actually using Random Forest right now, but I haven't tried XGBoost's scale_pos_weight yet. I’m intrigued by the anomaly detection idea though. Would something like Isolation Forest or a One-Class SVM be effective here, or would they be too sensitive to noise in the "normal" transaction data? I’ve heard mixed things about using them for financial data where legitimate spending patterns can vary quite a bit.

     Commented 2025-11-10 by Patricia Bennett

Try undersampling the majority class. Sometimes throwing away some of the "normal" data helps the model see the boundary for the "fraud" class much more clearly.

   Answered 2025-11-12 by Steven Wright

• That can work, Steven, but it’s risky because you lose so much information. I usually prefer Megan's suggestion of using class weights first before deleting any actual data points from the set.

     Commented 2025-11-14 by Kevin Bradley