Credit Card Fraud Detection

The rapid growth of digital payment systems has significantly increased the volume of credit card transactions worldwide. However, this growth has also led to a rise in fraudulent activities, causing substantial financial losses to banks and customers. Traditional fraud detection systems struggle to process large-scale transaction data and detect fraud in real time. This paper proposes a real-time credit card fraud detection framework using Big Data technologies and Machine Learning algorithms. The system leverages distributed storage through Apache Hadoop and real-time data processing using Apache Spark Streaming. Various classification algorithms, including Logistic Regression and Random Forest, are implemented to identify fraudulent transactions. Experimental results demonstrate improved detection accuracy, reduced processing time, and scalability for large transaction datasets.