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

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 for different combinations of key credit indicators, aiming to mitigate default risks. Experimental results show that NERHF demonstrates significant advantages in credit risk prediction, especially when using recurrent neural networks for feature extraction in conjunction with lightweight gradient boosting machine algorithms. Additionally, the Pre-DDQN algorithm outperforms comparative algorithms in credit risk control, highlighting its potential for practical applications.