Fraud Transaction Detection Using Machine Learning

The rapid growth of digital banking, e-commerce, and online payment systems has significantly increased fraudulent activities, resulting in financial losses and reduced customer trust. Traditional fraud detection approaches often struggle with evolving fraud patterns, data imbalance, and false positives, creating the need for more robust and efficient solutions. This project proposes a machine learning-based fraud transaction detection framework that utilizes supervised learning algorithms, specifically Logistic Regression and Random Forest, for identifying fraudulent transactions. The system focuses on data preprocessing, input validation, and feature engineering to improve data quality and enhance model performance. Transaction data is divided into training and testing sets to build predictive models capable of learning patterns associated with fraudulent behavior. A risk evaluation engine analyzes model predictions to assign transaction risk levels, while an alert generation module provides structured alerts for suspicious activities. The system further integrates a dashboard interface for visualizing fraud alerts and transaction analysis. The proposed framework is evaluated using standard classification metrics such as Accuracy, Precision, Recall, and F1-score to assess detection performance. By combining machine learning models with structured preprocessing, risk evaluation, and alert generation, the system provides an accurate, scalable, and computationally efficient fraud detection solution suitable for financial transaction monitoring and real-time fraud prevention.