REAL-TIME AI-POWERED FRAUD DETECTION: A MICROSERVICES APPROACH

Authors

  • Akhilesh Kota Sams West, Inc, USA. Author

Keywords:

Fraud Detection, Microservices Architecture, Machine Learning, Real-time Processing, Security Framework.

Abstract

This article presents a comprehensive analysis of a real-time AI-powered fraud detection system built on microservices architecture. The system addresses the growing challenges of digital payment fraud through an innovative combination of distributed computing, machine learning, and advanced security measures. By implementing a three-tier architecture comprising data ingestion, processing, and decision layers, the system delivers robust fraud detection capabilities while maintaining high performance and scalability. The implementation leverages cutting-edge technologies including Apache Kafka for stream processing, Spring Boot microservices for distributed computing, and ensemble machine learning models for fraud detection. The article details the system's architecture, performance optimizations, security considerations, and future enhancement pathways, demonstrating how modern software engineering practices and artificial intelligence can be effectively combined to create sophisticated fraud prevention solutions.

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Published

2024-12-31

How to Cite

Akhilesh Kota. (2024). REAL-TIME AI-POWERED FRAUD DETECTION: A MICROSERVICES APPROACH. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 15(6), 2011-2024. https://mylib.in/index.php/IJCET/article/view/IJCET_15_06_172