With the rapid growth of credit card and personal loan in finance industry, how to detect a potential default or bad debt with limited information has become extremely crucial. Meanwhile, one of the troublesome challenges in the field of credit scoring is the lack of positive samples. In this paper, we firstly introduced the idea of conditional tabular generative adversarial network(CTGAN) to generate sufficient default transactions into the origin data. Then we proposed a hybrid ensemble learning model based on CNN-ATCN to extract static features and dynamic features simultaneously, which CNN was utilized for finance attribute learning while the TCN with attention mechanism was used for extracting temporal dependencies from data. And LR, XGBoost, Adaboost, Random Forest are regarded as heterogeneous individual learners to form a stacking machine to output the classification results. We verified the designed default risk prediction model in two real world datasets. The results of the experiment indicate that CTGAN can effectively solve the data imbalance problem and the proposed CNN-ATCN model outperforms other state-of-art deep learning models in various way of metrics.