Big Data Technology Empowers Credit Risk Management in Commercial Banks

The big data era, driven by internet and AI advancements, has revolutionized finance, particularly challenging commercial banks post-pandemic with rising credit fraud risks and surging funding demands from SMEs, necessitating innovation in traditional credit risk management. This study examines how big data technology enhances banks' risk management through risk identification, dynamic prevention, and response to emerging risks. By analyzing pre-loan, in-loan, and post-loan processes, it highlights the role of multi-source data fusion, generative AI, and decentralized credit scoring in improving risk assessment speed and accuracy. However, challenges persist: poor data quality, "black-box" AI models, and cross-institutional collaboration barriers in data governance and privacy protection. Findings show big data significantly boosts risk control efficiency via intelligent early-warning systems and real-time monitoring, yet its full potential requires standardized data protocols, modular IT upgrades, and privacy-preserving computation frameworks. The research offers actionable strategies for banks' digital transformation while proposing innovative approaches for the financial sector to support SMEs, mitigate systemic risks, and align with real-economy needs.