###### Abstract
With the development of technology, more and more transactions are completed through the network, and the transaction data have characteristics such as high dimensionality and multiple sources, far exceeding the processing capacity of traditional solutions. The efficiency, accuracy, and strong robustness demonstrated by ensemble learning in multiple fields have brought new solutions and development opportunities to these problems. Ensemble learning can be divided into two types, homogeneous and heterogeneous, according to whether each base learner belongs to the same category. Homogeneous ensemble learning means that all base learners belong to the same category, which is also the most widely used. For example, [PERSON]. [PERSON] [15] proposed an SVM-based ensemble learning system when studying customer risk identification and customer relationship management. The results showed that the model has multiple components and spatial selection strategies and performs best in various test samples. Heterogeneous ensemble learning refers to not all individual learners being of the same kind. [PERSON] et al. [16] proposed a default prediction model that combines deep learning classifiers with tree-based classifiers using the ensemble learning method, and the proposed model has significantly improved the overall performance. In previous work, each model performed well in certain aspects, but there were also limitations. Through ensemble learning, we can combine the prediction results of these models, such as through voting or weighted averaging, to obtain more accurate and robust prediction results. In this study, to ensure the accuracy of the experiments, we first used Hot-deck imputation and Mean imputation to fill the missing values in the data variables. To mitigate the impact of differences in the magnitude among the variables on the classification results, prior to model construction, we employed the z-score standardization method to adjust the numerical ranges of different features to similar levels, eliminating the influence of scale on the data. Additionally, standardization improved the distribution of existingdata, making it closer to a normal distribution, thereby enhancing the performance and interpretability of the model. Furthermore, we utilized three feature selection methods based on statistical learning, regression coefficients, and principal component analysis to select appropriate feature variables. The obtained data were then inputted into both homogeneous and heterogeneous ensemble learning models, such as CatBoost, RUSBoost, and gcForest, to predict user defaults. A comparison was made with traditional single learning models to determine the optimal model and analysis. The experimental results of this paper show that the method based on adjusted homogeneous and heterogeneous ensemble learning can achieve good prediction of user defaults, and the accuracy of user default prediction based on the CatBoost and RUSBoost ensemble algorithms reaches 100%. This paper contributes to the development of existing user default prediction algorithms through a novel and effective approach based on homogeneous and heterogeneous ensemble learning, achieving higher performance and stronger generalization than traditional methods and state-of-the-art techniques. Our findings have important implications for practitioners and researchers in the fields of finance, banking, and insurance and lay the foundation for future research in this field. The Section 2 describes the data and methods, the Section 3 describes the results, the Section 4 describes the discussion, and the Section 5 summarizes and comments on the paper.