Bank transactions embeddings help to uncover current macroeconomics

Maria Begicheva and Alexey Zaytsev

Papers from arXiv.org

Abstract: Macroeconomic indexes are of high importance for banks: many risk-control decisions utilize these indexes. A typical workflow of these indexes evaluation is costly and protracted, with a lag between the actual date and available index being a couple of months. Banks predict such indexes now using autoregressive models to make decisions in a rapidly changing environment. However, autoregressive models fail in complex scenarios related to appearances of crises. We propose to use clients' financial transactions data from a large Russian bank to get such indexes. Financial transactions are long, and a number of clients is huge, so we develop an efficient approach that allows fast and accurate estimation of macroeconomic indexes based on a stream of transactions consisting of millions of transactions. The approach uses a neural networks paradigm and a smart sampling scheme. The results show that our neural network approach outperforms the baseline method on hand-crafted features based on transactions. Calculated embeddings show the correlation between the client's transaction activity and bank macroeconomic indexes over time.

Date: 2021-10, Revised 2021-12
New Economics Papers: this item is included in nep-ban, nep-big, nep-cis, nep-cmp, nep-mac and nep-tra
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in ICMLA 2021

Downloads: (external link)
http://arxiv.org/pdf/2110.12000 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2110.12000

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().