It is essential for banks to accurately predict whether clients will use their credit and analyze key factors influencing credit utilization after these clients have been approved for credit, so as to improve their client service level and profitability. Currently, machine learning algorithms are rarely applied to credit utilization prediction, and there is a lack of research on model interpretability in the financial credit utilization field. Therefore, this study proposes an interpretative TreeSHAP credit utilization prediction model based on CatBoost. Specifically, a credit utilization prediction model is constructed by CatBoost and is compared and optimized by using three hyperparameter optimization algorithms. Then, the model is experimentally compared with baseline models in terms of four main performance metrics. The results show that the model optimized by the TPE algorithm outperforms other models. Finally, the interpretability of the model is enhanced locally and globally by the TreeSHAP method. Furthermore, factors influencing client credit utilization are interpretively analyzed, so as to provide a decision-making basis for banks to make accurate marketing to clients.