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.

翻译：交易序列的高质量表示对于现代银行应用至关重要，包括风险管理、客户流失预测和个性化客户服务。不同任务需要不同的表示特性：局部任务受益于捕捉客户的当前状态，而全局任务则依赖于一般行为模式。先前研究表明，各种自监督方法产生的表示能更好地捕捉全局或局部特征。本研究探讨了两种自监督学习技术的整合——实例级对比学习和基于潜在空间掩码事件恢复的生成方法。该组合方法创建的表示能够平衡交易数据的局部与全局特征。在多个公共数据集上进行的实验，聚焦于序列分类和下一事件类型预测任务，表明整合方法相较于单一方法实现了更优性能，并展现出协同效应。这些发现表明，所提出的方法为推进金融领域事件序列表示学习提供了一个稳健的框架。