Abstract: Digital transactions are becoming faster and easier, providing numerous benefits to businesses and consumers alike. However, the increase in digital transactions is also fueling a rapid rise in digital payment fraud, an illicit phenomenon that attempts to exploit or bypass the control systems of online banking services. For banks and credit card companies, consumer protection against system failure is critical, and fast and real-time solutions are essential. Most traditional fraud detection methods rely on either approaches based on customer behavior, such as analysis of past transactions, or supervised learning based on domain-specific features, such as fraud rules and regulations. However, the rush to implement new services has left many banks and credit card companies ill-prepared to protect consumers from losses during online or mobile transactions. In this paper, we explore the use of novel machine learning techniques in the detection of online payment fraud. We explore the idea of deploying high-performing detections systems for real-time detection of payment fraud, while refining and learning local classifiers that can help even in situations that are very peculiar to a region. In addition, Big Data, if used in combination with proper validation processes, can facilitate rapid analysis of fraud schemes that require real-host interaction through the use of transaction fingerprinting techniques. This provides crucial information that could possibly lead to the unmasking of the true perpetrator. Furthermore, the use of actual user profiles can provide additional valuable data, but great care must be exercised to protect sensitive digital assets. Since it takes only seconds to commit digital payment fraud, business organizations must have not only the right technological framework, but also a detailed design and well-defined functional processes. By utilizing powerful detection systems, and also properly designing and implementing functional business processes, business organizations can implement a time-critical detection framework to efficiently handle the inherent unpredictability of this digital security threat.
Keywords: Real-time fraud detection, digital payments, machine learning, big data analytics, anomaly detection, transaction monitoring, behavioral analytics, supervised learning, unsupervised learning, feature engineering, predictive modeling, streaming data processing, fraud prevention, payment security, data pipelines, real-time scoring, model training, model deployment, decision trees, neural networks, support vector machines, clustering, risk scoring, false positive reduction, precision and recall, scalability, data lake, data ingestion, data enrichment, latency minimization, financial fraud, AI-driven security, fintech analytics.
Copyright Form