Smart Anomaly Detection for Monetization Metrics: A Technical Deep Dive

Authors

  • Kaarthikshankar Palraj University of Florida, USA

DOI:

https://doi.org/10.32996/jcsts.2025.7.4.62

Keywords:

Anomaly Detection, Machine Learning, Monetization Metrics, Real-time Monitoring, Alert Management.

Abstract

The anomaly detection system represents a significant advancement in monitoring monetization metrics, addressing the challenges of complex digital transactions. This intelligent framework leverages machine learning and Meta Prophet forecasting for real-time monitoring across multiple dimensions. By implementing sophisticated data processing pipelines and intelligent alert management, the system enables organizations to identify and respond to anomalies quickly while minimizing false positives. The implementation demonstrates significant improvements in detection accuracy, operational efficiency, and business value through automated monitoring and smart alert management. Built on scalable architecture, the system processes millions of data points daily while maintaining performance integrity. It incorporates dynamic threshold adjustments based on historical patterns, seasonal variations, and business context, ensuring relevance across varying transaction volumes. The framework's modular design facilitates seamless integration with existing business intelligence systems, providing actionable insights to stakeholders through customizable dashboards and comprehensive reporting capabilities.

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Published

2025-05-17

Issue

Section

Research Article

How to Cite

Kaarthikshankar Palraj. (2025). Smart Anomaly Detection for Monetization Metrics: A Technical Deep Dive. Journal of Computer Science and Technology Studies, 7(4), 529-534. https://doi.org/10.32996/jcsts.2025.7.4.62