Real-Time Fraud Detection: Leveraging Apache Kafka and Spark for Financial Transaction Processing

Author(s)	Sahini Dyapa
Country	United States
Abstract	This article examines the implementation of real-time fraud detection systems in modern financial institutions using Apache Kafka and Apache Spark streaming technologies. The article explores how these technologies address the challenges of increasing transaction volumes while maintaining security in digital banking environments. The article analyzes the performance improvements achieved through optimized data streaming architectures and machine learning integration, focusing on fraud detection accuracy, processing efficiency, and operational cost reduction. By implementing these advanced technological solutions, the findings demonstrate significant advancements in transaction processing capabilities, fraud prevention, and customer trust.
Keywords	Keywords: Real-time Fraud Detection, Apache Kafka, Apache Spark Streaming, Machine Learning in Finance, Digital Banking Security
Field	Computer
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-22
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2654
Short DOI	https://doi.org/g892fz

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology