Fraud Transaction Detection

Digital payment systems and online financial services enable fast and convenient transactions, but they can also be targeted byfraudulent activity. This paper proposes a machine-learning-based Fraud Transaction Detection system that classifies transactionrecords as legitimate or potentially fraudulent using features available in a documented dataset. The proposed workflow includesdata validation, preprocessing, handling class imbalance, model training, threshold selection, and evaluation. Candidatealgorithms include Logistic Regression, Decision Tree, Random Forest, and Isolation Forest where appropriate to the availablelabels. Because fraudulent transactions are often rare compared with legitimate transactions, accuracy alone may be misleading;precision, recall, F1-score, the confusion matrix, and precision-recall analysis should be considered. This is a proposed academicsystem, and no measured accuracy or operational fraud-prevention effectiveness is claimed. A real deployment would requiresecurity controls, privacy protection, human review, and monitoring for changing fraud patterns.