Orchestrating real-time decision intelligence: Building resilient ML data pipelines for banking transaction systems
Sandeep Ravichandra Gourneni · Open Access Research Journal of Engineering and Technology · 2025
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.