NERHF: a hybrid machine learning-driven efficient credit risk control framework.

ABSTRACT: As a core part of the financial industry, credit operations are accompanied by significant risks. Therefore, accurate credit risk control is crucial for financial institutions' lending decisions and overall risk management. In this paper, we propose a hybrid machine learning framework (Neural network-Ensemble learning-Reinforcement learning Hybrid Framework, NERHF) for efficient credit risk control. The framework utilizes neural network algorithms to extract features from credit data, enhancing the accuracy and robustness of credit risk prediction. Further, based on the extracted features, ensemble learning algorithms are employed for credit risk prediction. Finally, the improved deep reinforcement learning algorithm Pre-DDQN is applied to generate optimal credit risk control strategies fo

SUBMITTER: Wei L 

PROVIDER: S-EPMC12789139 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature