Using Apache Flink, scaling rule-based abnormality and fraud detection and corporate procedure monitoring.
Keywords:
Anomaly Detection, Fraud Detection, Business Process Monitoring, ScalabilityAbstract
Recognizing abnormalities in several disciplines like banking, e-commerce, and healthcare depends on rule-based anomaly and fraud detection systems. Still, traditional methods find it difficult to handle and understand this data in real-time as data volumes grow and develop more complex. Thanks to the scalability of rule-based systems, Apache Flink has developed as a potent stream processing tool that solves these challenges. This paper highlights Apache Flink's effectiveness in properly handling continuous data streams, hence improving anomaly detection and business process monitoring at scale. Notwithstanding the promise, the use of these systems brings challenges related to system complexity management, data quality assurance, and low-latency processing assurance. The paper addresses the operational issues related to the extensive implementation of these systems and their preservation of effectiveness over time. It also provides understanding of the evolution of anomaly detection systems and the transforming power of stream processing architectures like Flink. Companies may enhance their detection abilities by using advanced techniques such as machine learning, therefore reducing false positives and increasing the accuracy of their fraud detection systems.
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