Recognizing potential defaulters is a crucial problem for financial institutions. Therefore, many credit scoring methods have been proposed in the past to address this issue. However, these methods rarely consider the interaction among customers such as bank transfer and remittance. With rapid growth in the number of customers adopting online banking services, such interaction information plays a significant role in assessing their credit score. In this paper, we propose a novel scalable credit scoring approach called CDGAT (Graph attention network for credit card defaulters) for predicting potential credit card defaulters. In CDGAT, a customer’s credit score is calculated based on transaction embedding and neighborhood embedding. To obtain the neighborhood embedding, CDGAT first utilizes the Amount-bias Sampling (AbS) strategy to extract a subgraph for each customer. Next, CDGAT directly aggregates neighbors’ features according to their influence weights. The experimental results on the dataset from Industrial and Commercial Bank of China (Macau) Limited (ICBC (Macau)) show that CDGAT significantly outperforms the baseline methods. Furthermore, experimental results reveal that the proposed method is also superior to several state-of-the-art Graph Convolutional Neural Network models in terms of scalability and performance.