  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.
