For many years, financial institutions have used batch processing to detect fraud, which involves analyzing transaction data at intervals, often resulting in delays that allow fraudsters to exploit stolen information rapidly. Real-time data streaming presents a solution by allowing banks and payment providers to monitor transactions continuously, detecting and blocking fraudulent activities instantaneously. Technologies like Apache Kafka and Apache Flink enable financial institutions to analyze transactions as they occur, minimizing financial losses and enhancing customer trust through immediate responses. This shift from batch to real-time processing not only improves efficiency by reducing false positives and resource expenditure but also enhances customer experience by preventing fraud without disrupting legitimate transactions. Institutions embracing this approach, such as Evo Banco, have seen dramatic reductions in fraud losses, highlighting the effectiveness of real-time streaming in creating a proactive, secure financial ecosystem.