Transforming Real-Time Transactions: Distributed AI, Fraud Detection, and Advanced Analytics in Regulated Financial Environments
Abstract
The convergence of distributed artificial intelligence (AI), streaming analytics, and real-time payment infrastructure is transforming the financial services sector by enabling faster, more intelligent, and scalable transaction processing. However, this transformation also introduces significant technical, regulatory, and sustainability challenges for financial institutions operating in highly regulated environments. With global fraud losses exceeding USD 485 billion in 2023 and increasing regulatory demands for transparency, fairness, and algorithmic explainability, financial systems must simultaneously achieve high accuracy, low latency, operational efficiency, auditability, and environmental sustainability. This article examines the architectural and operational foundations of compliant distributed AI deployment in modern financial ecosystems. Drawing on current academic and industry literature, the study analyzes the integration of microservices architectures, event-driven processing pipelines, container-based inference engines, edge-cloud computing environments, and federated learning frameworks in supporting real-time fraud detection and payment intelligence. Particular attention is given to fraud detection model architectures, streaming data analytics, distributed model orchestration, and the trade-offs between performance, scalability, and energy consumption. The analysis also explores the broader social and governance dimensions of AI deployment, including fairness, accountability, privacy preservation, regulatory compliance, and carbon-aware computing practices. The findings indicate that well-designed distributed AI systems can achieve fraud-scoring latencies below 20 milliseconds while maintaining high predictive accuracy and compliance with regulatory requirements. Furthermore, federated learning and event-driven architectures enable institutions to improve fraud detection capabilities without centralizing sensitive customer data, thereby enhancing privacy and security. The study concludes that the successful integration of distributed AI and real-time payment infrastructure requires a balanced approach that aligns technological innovation with governance, sustainability, and ethical considerations. The proposed framework offers practical guidance for financial institutions seeking to modernize payment systems while ensuring resilience, trustworthiness, and long-term operational sustainability.
Keywords
References
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DOI: https://doi.org/10.52088/ijesty.v6i1.1833
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