Bank transactions embeddings help to uncover current macroeconomics

• URL: http://arxiv.org/abs/2110.12000v2
• Date: Tue, 26 Oct 2021 21:27:57 GMT
• Title: Bank transactions embeddings help to uncover current macroeconomics
• Authors: Maria Begicheva, Alexey Zaytsev
• Abstract summary: We use clients' financial transactions data from a large Russian bank to get macroeconomic indexes. We develop an efficient approach that allows fast and accurate estimation of macroeconomic indexes based on a stream of transactions.
• Score: 0.8029971974118232
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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