Bank transactions embeddings help to uncover current macroeconomics
This work addresses the need for banks to make timely risk-control decisions in rapidly changing environments, though it appears incremental as it applies neural networks to an existing data source.
The authors tackled the problem of predicting macroeconomic indexes with long lags by using client transaction data from a Russian bank, developing a neural network approach that outperforms a baseline method on hand-crafted features.
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.