Fraud Detection in Financial Transactions Using Machine Learning: Insights from the PaySim Mobile Money Dataset

The rapid digital transformation of financial systems has increased the risk of fraud in mobile payment ecosystems. This paper analyzes fraudulent behavior in the PaySim mobile-money dataset using feature engineering and supervised classification. We trained and compared Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest classifiers using stratified 80:20 splitting and class-weighting to counter extreme class imbalance. For the test set, Decision Tree achieved the best overall balance between precision and recall (Precision = 0.6835, Recall = 0.9696, F1 = 0.8018, ROC-AUC = 0.9845). Random Forest produced very high recall (0.9838) and ROC-AUC (0.9990) but low precision (0.1576), resulting in many false positives. These results indicate ensemble and tree-based methods can detect most fraud events in this dataset, but there is a trade-off between minimizing missed fraud (false negatives) and limiting false alarms for legitimate users. We recommend using precision–recall analysis, threshold tuning, and cost-sensitive methods in operational settings to control that trade-off.