Developing a Machine Learning Model for Real-Time Fraud Detection in Online Transactions

The rapid growth of online transactions has significantly increased the risk of fraudulent activities, making fraud detection an important challenge for financial institutions. Traditional rule-based fraud detection systems often fail to adapt to evolving fraud patterns and large volumes of transaction data. To address this issue, this paper presents a machine learning-based approach for detecting fraudulent online transactions using deep learning models. The proposed system utilizes Convolutional Neural Networks (CNN) to extract meaningful patterns from transaction features and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to analyze sequential transaction behavior. The model is trained on a publicly available transaction dataset and evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. A user-driven web interface is also developed to allow fraud prediction based on input transaction details. Experimental results indicate that the proposed approach is effective in identifying fraudulent transactions and can support decision-making in online payment systems. This work focuses on practical implementation and performance evaluation.