Bayesian Filtering in a Latent Space to Predict Bank Net Income from Acquiring

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAnalysis of Images, Social Networks and Texts - 9th International Conference, AIST 2020, Revised Selected Papers
EditorsWil M. van der Aalst, Vladimir Batagelj, Dmitry I. Ignatov, Michael Khachay, Olessia Koltsova, Andrey Kutuzov, Sergei O. Kuznetsov, Irina A. Lomazova, Natalia Loukachevitch, Amedeo Napoli, Alexander Panchenko, Panos M. Pardalos, Marcello Pelillo, Andrey V. Savchenko, Elena Tutubalina
PublisherSpringer Science and Business Media Deutschland GmbH
Pages344-355
Number of pages12
ISBN (Print)9783030726096
DOIs
Publication statusPublished - 2021
Event9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020 - Moscow, Russian Federation
Duration: 15 Oct 202016 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12602 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020
Country/TerritoryRussian Federation
CityMoscow
Period15/10/2016/10/20

Keywords

  • Acquiring
  • Filtering
  • Latent dynamics
  • Net income

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