Enhancing Credit Card Fraud Detection Using Random Forest and SMOTE
TL;DR
This research integrates a bespoke Random Forest classifier with the Synthetic Minority Over-sampling Technique (SMOTE), and results indicate that SMOTE-RF is a feasible and interpretable solution for real-world fraud detection.
Abstract
Credit card theft costs the global banking system billions of dollars every year and erodes user confidence in electronic payments. Fraudulent transactions are rare (usually < 0.2 % of all records) and this rarity poses a basic problem for automated detection: a classifier trained on raw data learns to predict the majority class. The resulting model is surprisingly accurate in general, yet fails to detect much of the fraud it was created for. To directly address such imbalance, this research integrates a bespoke Random Forest classifier with the Synthetic Minority Over-sampling Technique (SMOTE). The pipeline is tested on the public European credit card transaction dataset with 284,807 transactions with just 492 (0.172%) fraud cases. The SMOTE-RF model therefore performs better than Logistic Regression, Decision Trees and the normal Random Forest without oversampling, with a precision of 0.947, a recall of 0.921, an F1-score of 0.934, a Matthews Correlation Coefficient (MCC) of 0.929 and an AUC-ROC of 0.983. An ablation research reveals that most of the recall gain is driven by SMOTE, whereas precision is mostly enhanced by tuning Random Forest hyperparameters. All these results indicate that SMOTE-RF is a feasible and interpretable solution for real-world fraud detection.