Big Data Analytics and Machine Learning Techniques for Real-Time Credit Card Fraud Detection

Abstract

Big Data is commonly characterized by the 4 V's: Volume, Variety, Velocity, and Veracity. In today’s digital age, data is generated in terabytes and petabytes, far exceeding the storage capabilities of a single machine. With data constantly circulating across cloud platforms, the risk of leakage and fraud has increased significantly credit card fraud being one of the most pressing global concerns. As numerous shopping platforms and businesses operate around us, each domain generates vast amounts of data, often reaching into yottabytes. Manually handling, analyzing, or detecting anomalies in such large-scale data is extremely challenging. However, with the advancement of computing and emerging technologies, detecting fraud has become much more efficient and scalable. This study examines the application of big data in analysing credit card consumer behaviour, specifically in the context of online transactions, password creation, age, income, and other relevant factors. The focus is on identifying anomalies in these data points to detect potentially fraudulent activities quantitative approach is employed to identify statistical patterns, and the performance of seven different machine learning algorithms, such as Logistic Regression, K-Nearest Neighbours (KNN), and XGBoost, is evaluated for their effectiveness. As technology advances, factors such as age and increasing reliance on online transactions, e-commerce, and digital banking contribute to rising vulnerabilities, making fraud detection more critical than ever. Result: In the Real-time credit card fraud detection using big data, different algorithms are discussed and implemented, so KNN & XGBOOST give better results than other ML algorithms. The impact of compliance on sophisticated data-based security systems will be examined in a later study, which can make use of historical fraud typologies and trends to comprehend potential shifts over time.