Orchestrating real-time decision intelligence: Building resilient ML data pipelines for banking transaction systems
Acharya Nagarjuna University, India.
Research Article
Open Access Research Journal of Engineering and Technology, 2025, 08(02), 043-055.
Article DOI: 10.53022/oarjet.2025.8.2.0048
Publication history:
Received on 18 March 2025; revised on 26 April 2025; accepted on 29 April 2025
Abstract:
This article examines the intersection of machine learning (ML) and banking transaction systems, focusing on the architecture, implementation, and operational challenges of real-time decision intelligence pipelines. We explore how financial institutions can develop resilient data infrastructures that support instantaneous fraud detection, dynamic risk assessment, and personalized customer experiences while maintaining regulatory compliance. Through analysis of technical architectures, case studies, and emerging technologies, we provide a comprehensive framework for banking technology leaders seeking to transform their transaction processing capabilities with advanced ML systems. The article balances practical implementation guidance with theoretical foundations to address the unique constraints of the banking environment.
Keywords:
Machine learning; Banking transaction; Event Stream Processing; Banking sector
Full text article in PDF:
Copyright information:
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0