Universal representations for financial transactional data: embracing
  local, global, and external contexts
Alexandra Bazarova Maria Kovaleva Ilya Kuleshov Evgenia Romanenkova Alexander Stepikin Alexandr Yugay Dzhambulat Mollaev Ivan Kireev Andrey Savchenko Alexey Zaytsev
Effective processing of financial transactions is essential for banking data
analysis. However, in this domain, most methods focus on specialized solutions
to stand-alone problems instead of constructing universal representations
suitable for many problems. We present a representation learning framework that
addresses diverse business challenges. We also suggest novel generative models
that account for data specifics, and a way to integrate external information
into a client's representation, leveraging insights from other customers'
actions. Finally, we offer a benchmark, describing representation quality
globally, concerning the entire transaction history; locally, reflecting the
client's current state; and dynamically, capturing representation evolution
over time. Our generative approach demonstrates superior performance in local
tasks, with an increase in ROC-AUC of up to 14\% for the next MCC prediction
task and up to 46\% for downstream tasks from existing contrastive baselines.
Incorporating external information improves the scores by an additional 20\%.