Universal representations for financial transactional data: embracing local, global, and external contexts

• URL: http://arxiv.org/abs/2404.02047v1
• Date: Tue, 2 Apr 2024 15:39:14 GMT
• Title: Universal representations for financial transactional data: embracing local, global, and external contexts
• Authors: Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov, Evgenia Romanenkova, Alexander Stepikin, Alexandr Yugay, Dzhambulat Mollaev, Ivan Kireev, Andrey Savchenko, Alexey Zaytsev,
• Abstract summary: 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.
• Score: 95.7760348824795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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\%.

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