Xbank: Cross-Bank Transfer of Transaction Foundation Models

Self-supervised learning enables banks to derive reusable representations from customer transaction histories. However, the large datasets needed for pretraining typically remain proprietary, limiting opportunities to study how these representations transfer across institutions. We introduce , a dataset comprising around 91.5 million daily-aggregated transaction records from 378,305 corporate clients, with 13 categorical features, two numerical features, and three monthly product-propensity targets. Together with the Multimodal Banking Dataset (), which contains transaction histories for approximately 1.5 million corporate clients, supports a benchmark for cross-bank transfer of frozen representations. We evaluate five models spanning contrastive, autoregressive, masked-modelling, and temporal point-process objectives. By evaluating -pretrained encoders on raw transactions, their daily aggregates, and , we isolate the effect of aggregation within the source institution and assess cross-bank transfer at the same aggregation level under each dataset’s native evaluation protocol. To handle unknown field correspondences, we propose a dictionary-free schema-alignment method based on semi-relaxed Fused Gromov-Wasserstein optimization. It combines statistical column profiles with within-bank feature dependencies, accommodates schemas of different sizes, and determines a fixed mapping before evaluation without using downstream labels. Together, these contributions provide a framework for studying the transfer of transaction representations across banks with heterogeneous data schemas.