Building an Integrated Data Architecture for Real-time Predictive Compliance in Financial Technology Platforms

Abstract

Today's financial technology (FinTech) platforms are living in an environment of rapidly changing regulations, multi-jurisdictional compliance, and exponentially increasing transaction volumes with intense regulatory enforcement. Traditional compliance models are largely batch-based and involve post-hoc auditing routines, and are unable to identify real-time transaction anomalies, patterns of money laundering, or system-wide risks. The regulatory risk is a structural problem that can inflict catastrophic penalties on companies, as well as financial losses. In this paper, the authors develop and propose the Real-Time Predictive Compliance Architecture (RPCA), a multi-layered data topology designed to combine streaming data pipelines and distributed machine learning models for proactive risk management. The RPCA is based on the Complex Adaptive Systems (CAS) theory and the Dynamic Capabilities Theory and uses a Kappa data architecture framework to process high throughput transaction telemetry data in an integrated processing loop. In this study, three non-empirical research propositions are articulated that outline two distinct mediation paths (streaming architectural velocity and cross-functional regulatory engineering alignment) and two distinct moderation paths (relationship outcomes and architectural velocity). Finally, the paper presents a comprehensive empirical validation matrix, a detailed structural blueprint developed by Covariance-Based Structural Equation Modeling (CB-SEM), and an actionable deployment roadmap for the industrial and enterprise FinTech engineering operators.