Scaling Rule Based Anomaly and Fraud Detection and Business Process Monitoring Through Apache Flink
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I1P111Keywords:
Anomaly Detection, Fraud Detection, Business Process Monitoring, Apache Flink, Stream Processing, Rule-Based Systems, Scalability, Real-Time Data Processing, Real-Time Analytics, Big Data, Data Streams, Complex Event Processing (CEP), Event-Driven Architecture, Data Pipelines, Fault Tolerance, Distributed Systems, Data Processing Frameworks, Data Integration, Predictive Analytics, Machine Learning, Event Correlation, Pattern Recognition, Data Governance, Operational MonitoringAbstract
Rule-based anomaly and fraud detection systems are the heart of irregularity identification. They might be referred to as continuously running monitors, albeit of a different type that is applied in different domains such as finance, e-commerce and healthcare. As the amount of data increases rapidly and the complexity of that data also grows, the traditional methods certainly cannot cope with the task of handling and processing the data continuously without a strain. Apache Flink is a powerful stream processing framework that is capable of meeting these challenges by providing rule-based systems. This article delves into how the Apache Flink framework can be used to establish anomaly detection and business process monitoring at scale by pointing out its made-for-the-job characteristic of enabling continuous data. Mixing rule-based methods with Flink’s facility, businesses can catch frauds and anomalies as they happen, thus making up-to-date decisions and cutting the risks. Also, this article is shining a light on some of Flink’s most important features namely: stateful processing, windowing, etc. Activating stateful processing allows the events of the system to be kept continuously over time in regard to the system flow. Allowing a system like this to receive and process data continuously by partitioning data into short windows is called windowing. Pairing Flink with rule-based systems is like hooking it up with your circuits for fraud detection, thus enabling the continuous monitoring and the immediate response to the suspicious activities. Real-life usages of this technology are: Monitoring financial transactions for fraudulent activities, Detecting unusual patterns in e-commerce transactions, and Ensuring compliance in healthcare systems. Nevertheless, the implementation of these systems has its downsides and challenges, such as system complexity management, data quality issues, and guarantees of low latency processing. The article also talks about the issue of working on the scale and keeping the installed systems effective over a longer period of time
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