Analysis and Evaluation of Various Fraud Detection Methods for Electronic Payment Cards Transactions in Big Data

In today’s digital world, the vast volume of data generated, often referred to as big data, presents both challenges and opportunities. One significant challenge is the risk of fraud in electronic cash transactions. This study examines and compares 20 common online fraud detection methods within the context of big data, evaluating them based on 11 criteria: type of learning, speed, accuracy, cost (time), complexity, interpretability, scalability, robustness, flexibility, and temporal and spatial complexity. The evaluation highlights the performance of each method against various types of online cash fraud, including identity theft, card skimming, phishing, malware, money laundering, account takeover, refund fraud, and friendly fraud. Performance scores, derived from real-world data and simulations, indicate the effectiveness of each method in identifying and countering fraud in a big data environment. Our findings show that deep learning methods and artificial neural networks outperform other methods in most fraud scenarios, while general rule-based and inferential methods are less effective. This research provides valuable insights for financial institutions, e-commerce platforms, and other online services to enhance their fraud detection capabilities and protect sensitive customer data in the era of big data.