Transformative Impacts of Big Data Technologies on the Credit Reporting Industry: Drivers, Challenges, and Future Trajectories - Zhiming Song; Huilin Mo (Journal of Frontier in Economic and Management Research, 2025)
Amid the rapid evolution of the digital economy, big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources, refining modeling methods, and enhancing risk response capacity. From the integrated perspective of the “technology–institution–ethics” triad, this paper systematically reviews 33 studies published between 2012 and 2025, supplemented by representative case analyses. The review follows PRISMA 2020 guidelines, covering both international literature and China-specific practices. The analysis shows that while big data enables more dynamic, precise, and intelligent credit evaluation, it also generates systemic risks, including privacy infringement, algorithmic bias, model opacity, and regulatory lag. To address these dilemmas, a comprehensive governance framework is proposed that combines explainable artificial intelligence, privacy-preserving computation, cross-sector regulatory coordination, and ethical algorithmic norms. The study acknowledges its limitations as a review-based work—particularly in terms of proprietary data accessibility, interpretability of complex models, and empirical cross-platform validation—and suggests future research directions involving real- world experimentation, interpretable deep models, and multi-institutional governance mechanisms. Overall, this research aims to provide theoreticalfoundations and policy insights for building an open, transparent, and sustainable digital credit ecosystem.