AI-powered real-time fraud detection across hybrid cloud architectures using stream processing and deep learning

Up-to-date types of cyber fraud develop rapidly, making modern fraud detection systems struggle to detect challenging and real-time irregularities. The research evaluates the deployment of artificial intelligence (AI) with stream processing technology and deep learning algorithms for immediate fraud detection that functions in hybrid cloud systems. The combination of public and private cloud infrastructures in hybrid cloud systems provides better scalability and flexibility; however, it generates two security difficulties and data transmission delays. The proposed AI-driven fraud detection system depends on stream processing tools Apache Kafka and Apache Flink combined with LSTM networks and CNNs to determine real-time fraudulent actions. The research design implements data pipeline operations followed by model development and live prediction running inside a hybrid cloud environment to achieve superior speed and high-performance levels. The proposed solution achieves notable performance progress through empirical findings from standard datasets and artificial fraud simulations. A substantial research contribution exists in this work because it delivers a flexible framework for modern hybrid cloud systems which maintain security and scalability features.