High-quality representation of transactional sequences is vital for modern banking applications, including risk management, churn prediction, and personalized customer offers. Different tasks require distinct representation properties: local tasks benefit from capturing the client's current state, while global tasks rely on general behavioral patterns. Previous research has demonstrated that various self-supervised approaches yield representations that better capture either global or local qualities. This study investigates the integration of two self-supervised learning techniques - instance-wise contrastive learning and a generative approach based on restoring masked events in latent space. The combined approach creates representations that balance local and global transactional data characteristics. Experiments conducted on several public datasets, focusing on sequence classification and next-event type prediction, show that the integrated method achieves superior performance compared to individual approaches and demonstrates synergistic effects. These findings suggest that the proposed approach offers a robust framework for advancing event sequences representation learning in the financial sector.

本研究解决了现代银行应用中事务序列的高质量表示问题，尤其是风险管理和个性化客户推广。论文提出了结合实例对比学习与生成学习的创新方法，以平衡本地与全局事务数据特性。实验表明，该方法在序列分类和下一个事件类型预测任务上表现优秀，具有显著的协同效应，为金融领域事件序列表示学习提供了有效框架。

将对比学习与生成学习结合用于事件序列模型