Application Analysis of Artificial Intelligence in Financial Risk Assessment: A Case Study of Ant Group

Abstract

The rapid advancement of artificial intelligence (AI) technology has introduced novel methodologies and practical tools for financial risk assessment. This study focuses on Ant Group to explore the technical architecture, application scenarios, and practical outcomes of AI in financial risk evaluation. By analyzing Ant Group’s intelligent risk control system, which integrates big data and AI technologies, the research reveals that its approach—synthesizing multi-source heterogeneous data, enabling real-time dynamic monitoring, and optimizing deep learning models—significantly enhances the accuracy of credit assessments and anti-fraud capabilities while scaling inclusive financial services. However, challenges persist during implementation, including algorithmic opacity, data privacy concerns, and regulatory misalignment. To address these issues, this paper proposes optimization strategies such as improving model interpretability, refining data governance frameworks, and establishing adaptive regulatory mechanisms, offering theoretical and practical insights for the intelligent transformation of the financial industry.