The Application of Artificial Intelligence in Credit Assessment and Dynamic Risk Monitoring

Abstract

The traditional credit assessment system relies on static financial data and historical credit records and has inherent limitations such as financial exclusion and lagging in risk early warning. Credit assessment and risk monitoring have been fundamentally reshaped in recent years, fuelled by advances in artificial intelligence and big data analytics. This article provides a systematic review of how artificial intelligence is applied in credit assessment and dynamic risk monitoring. It delves into the innovative breakthroughs concerning data dimensions, model construction, and monitoring paradigms. Research shows that AI has achieved a paradigm shift from static assessment to dynamic monitoring by introducing alternative data (for example social behavior, transaction records, etc.) and advanced algorithms (such as natural language processing and reinforcement learning), significantly enhancing the accuracy, timeliness and inclusiveness of assessment. This study scrutinizes critical challenges—such as data privacy, algorithmic fairness, and model interpretability—while also mapping out future trajectories for public data infrastructure and intelligent regulatory frameworks. Its conclusions are intended to guide further academic research and inform practice within the domain.