Adaptive Time‐Aware Feature Augmentation and Improved Harris Hawks Optimization for Credit Risk Modelling

Credit Risk Prediction (CRP) is crucial for financial stability but faces challenges from high‐dimensional, dynamic data streams, which can cause redundancy and obscure temporal risk patterns. To address these issues, this study proposes an integrated framework that combines adaptive time‐aware feature augmentation with an Improved Binary Harris Hawks Optimization (IBHHO) algorithm for feature selection. Firstly, the original credit features are augmented using four classical statistical approaches, and a Bayesian optimization mechanism is also employed to select parameters for temporal variables. Then, the IBHHO algorithm, which was developed based on Tent map initialization, nonlinear energy update, and a Whale Optimization Algorithm (WOA) fusion strategy, is introduced to identify an optimal feature subset. The integrated framework has been tested on a real Chinese dataset and two public datasets. On the dataset of Chinese small and medium‐sized enterprises (SMEs), the framework achieved a prediction accuracy of 0.961 and an AUC value of 0.882, while reducing the characteristic dimension by 77.3%. These results show that the proposed framework provides a potential solution to the problem of dynamic CRP, especially for financial institutions that manage complex credit information.