Fraud Transaction Detection Using Machine Learning

Digital payments, online shopping, mobile banking, and card-not-present transactions have increased the speed andconvenience of financial services. These channels also create opportunities for fraudulent activity, includingstolen-card use, account takeover, identity misuse, and suspicious transaction patterns. This paper presents a proposedfraud transaction detection framework that combines transaction-data preparation, rule-based checks, machine-learningrisk scoring, and human review. Transaction attributes such as amount, time, merchant category, location consistency,and recent activity can be transformed into features for a classification model. The system assigns a risk score androutes high-risk transactions for additional verification instead of treating every unusual transaction as confirmed fraud.The paper outlines the architecture, workflow, candidate algorithms, evaluation measures, privacy safeguards, anddeployment considerations. Since no institution-specific dataset or completed experiment was supplied, performancevalues are not claimed; the model must be evaluated on representative, appropriately protected data before operationaluse.