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Amid the ongoing evolution of the digital economy, big data technologies are fundamentally reshaping the structural foundations and risk control mechanisms of traditional credit reporting systems. From the integrated perspective of “technology–institution–ethics” coordination, this study systematically reviews the application pathways, structural transformations, and associated risks of big data in the credit reporting domain. The findings reveal that big data significantly enhances the breadth of credit evaluation, the precision of modeling, and the efficiency of risk response. However, it also gives rise to systemic risks such as data privacy infringement, algorithmic bias, model opacity, and regulatory lag. To address these challenges, the paper proposes a comprehensive governance framework supported by explainable artificial intelligence (XAI), privacy-preserving computation techniques, cross-sector regulatory coordination, and ethical algorithmic norms. Such a framework aims to promote a dynamic balance between efficiency and fairness within credit systems. Finally, the paper highlights current research limitations in terms of data accessibility, model transparency, and empirical collaboration across domains, and calls for future studies to deepen investigations through empirical validation, technical enhancement, and institutional innovation. The goal is to provide theoretical foundations and policy guidance for building an open, trustworthy, and sustainable digital credit ecosystem.