Real-Time Data Pipelines: Enhancing Efficiency in AI-Driven Financial Crime Detection Systems

Abstract

Financial crime poses significant threats to the global economy, necessitating robust detection and prevention mechanisms. AI-driven systems have emerged as pivotal tools in identifying and mitigating such illicit activities. However, the efficiency and effectiveness of these systems heavily rely on the underlying data infrastructure. This research explores the role of real-time data pipelines in enhancing AI-driven financial crime detection systems. We examine the architecture, technologies, and methodologies that enable the seamless flow of data from diverse sources to analytical models. Through a comprehensive literature review and case study analysis, we demonstrate how real-time data pipelines improve detection accuracy, reduce latency, and increase the scalability of financial crime detection systems. Additionally, we address the challenges associated with data integration, processing speeds, and system reliability. Our findings highlight best practices and provide actionable insights for financial institutions aiming to leverage real-time data pipelines to bolster their AI-driven financial crime detection capabilities.