Artificial Intelligence + Financial Risk Control: Application and Development Analysis of Credit Default Prediction Models Based on Machine Learning and Deep Learning

Abstract

Driven by the "AI+" national strategy, artificial intelligence technologies—represented by machine learning, deep learning, and large language models (LLMs)—are revolutionizing the core paradigm of financial risk management with unprecedented depth. This paper focuses on the critical task of credit default prediction, systematically tracing the evolution of credit risk assessment techniques from traditional statistical learning to integrated machine learning and deep learning, while examining their theoretical boundaries and trade-offs in prediction accuracy, model interpretability, and complex data representation capabilities. Through case studies of WeBank, MYbank, and Du Xiaoman Finance, the study demonstrates three cutting-edge paradigms: privacy computing that breaks down data compliance silos, high-dimensional behavioral data-driven microcredit optimization, and LLM-powered unstructured risk reasoning. The research reveals that AI has evolved beyond being merely an efficiency-enhancing tool for risk control into a critical infrastructure that expands the reach of inclusive financial services through alternative data mining. However, practical implementation faces challenges including data compliance dilemmas, conflicts between model "black-box" characteristics and regulatory requirements, and algorithmic bias-induced heterogeneity effects. Finally, the paper envisions a next-generation intelligent risk management ecosystem—more open, sophisticated, and responsible—driven by the synergy of privacy computing, graph neural networks, and large language models. This study provides a systematic analytical framework for understanding the technical logic, application value, and developmental trajectory of intelligent risk management.