Title: Real-time analytics for big data stream :Fraud Detection.
Authors: Victoria Evelyn
Abstract: The escalating volume and velocity of data streams in modern business operations present both opportunities and challenges. While offering valuable insights, these streams also create a fertile ground for fraudulent activities. Traditional batch processing methods are ill-equipped to detect fraud in real-time, necessitating the adoption of advanced real-time analytics techniques. This research investigates the application of real-time analytics to big data streams for effective fraud detection. We explore various methods, including stream processing frameworks (e.g., Apache Kafka, Apache Flink), machine learning algorithms (e.g., anomaly detection, classification), and rule-based systems, for identifying suspicious patterns and anomalies in real-time. The research examines the challenges of processing high-velocity data streams, such as data quality issues, concept drift, and the need for low-latency processing. We analyze different approaches to feature engineering and model training in a streaming environment, considering factors like windowing techniques and incremental learning. Furthermore, we discuss the importance of scalability and fault tolerance in real-time fraud detection systems. Through case studies in financial transactions, online retail, and social media, we demonstrate the effectiveness of real-time analytics in detecting various types of fraud, such as credit card fraud, identity theft, and account takeover. The research concludes by outlining best practices for designing and implementing real-time fraud detection systems that can adapt to evolving fraud patterns and provide timely alerts for preventative action.
Keywords: Real-time analytics, big data streams, fraud detection, stream processing, Apache Kafka, Apache Flink
International Journal of Applied Pattern Recognition, Vol. 6, No. 2, 2019 (Special Issue)
Received: 12 Jan 2019
Accepted: 24 Mar 2019
Published online: 10 April 2019