The Application of Artificial Intelligence in Financial Risk Pr ediction

Abstract

This paper systematically reviews the relevant research literature of artificial intelligence technology (covering machine learning, deep learning, etc.) in credit risk, market risk and operational risk prediction in the financial field. By comparing the application effects of different artificial intelligence algorithms in various risk prediction scenarios, this paper clarifies the advantages of machine learning algorithms in multi-dimensional data integration, as well as the value of deep learning models in capturing timing dependence and potential risk clues, and summarizes the shortcomings of the two algorithms in scene adaptation and extreme case equivalence. On this basis, this paper deeply discusses the core difficulties faced by technology application: there are some problems at the data level, such as fragmentation, difficult balance between sharing and privacy, and compliance restrictions; At the model level, it highlights the dilemma of lack of explainability caused by the characteristics of “black box”; At the technical level, it is facing challenges such as high adaptation cost and weak robustness. Finally, the future research direction of artificial intelligence in financial risk prediction is prospected to provide a reference framework for follow-up related research.