Abstract
A macro stress-testing and scenario analysis is an important part of an official assessment of any bank regarding its safeguarding and stability. There is a lack of efficient tools for scenario analysis to model uncertainty of the bank financial indicators depending on the main macro-economic parameters. In this work we present a new model for prediction of the bank financial indicators. We develop an approach to filtering in a latent space capable of modeling dependence of a huge cross-section of the indicators on the set of macro-economic parameters. We demonstrate a superior ability of our model to predict bank net income from acquiring compared to standard predictive models.
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Acknowledgement
The author would like to thank Sergey Strelkov, Ksenia Gubina, Denis Orlov (Sberbank, Treasury), and Evgeny Egorov (Skoltech) for fruitfull discussions and computational experiments respectively. The work was supported by the RFBR grant 20-01-00203.
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Burnaev, E. (2021). Bayesian Filtering in a Latent Space to Predict Bank Net Income from Acquiring. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_26
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