Universal representations for financial transactional data: embracing local, global, and external contexts
<|reference_start|>Universal representations for financial transactional data: embracing local, global, and external contexts: 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\%.<|reference_end|>
@article{bazarova2024universal,
    title={Universal representations for financial transactional data: embracing
  local, global, and external contexts},
    author={Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov, Evgenia
  Romanenkova, Alexander Stepikin, Alexandr Yugay, Dzhambulat Mollaev, Ivan
  Kireev, Andrey Savchenko, Alexey Zaytsev},
    journal={arXiv preprint arXiv:2404.02047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02047},
    primaryClass={cs.LG cs.AI}
}