With the continuous advancement of the "3.0 era" of financial technology and the innovation of bank credit function, deep learning has once again become a hot issue in the formulation, implementation and effect evaluation of bank credit policy. This paper combed through the relevant literature on the impact of deep learning models on bank credit, and found that: in terms of the development path of deep learning models, most of the articles choose the optimal model by comparing machine learning and deep learning. These studies explore and act as new derivative models on the basis of the applicability of deep learning, so that the deep learning model is constantly revised and improved in the test and criticism; in terms of the impact of deep learning on bank credit risk management, the existing literature mainly focuses on the three parts of risk identification, early warning and avoidance. This paper tries to put forward reasonable suggestions recommendations on the basis of reviewing the existing research.

Huang Mingyu. Research progress on the influence of deep learning models on bank credit management. Academic Journal of Business & Management (2024) Vol. 6, Issue 1: 119-126. https://doi.org/10.25236/AJBM.2024.060117.

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