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International Conference on Analysis of Images, Social Networks and Texts

AIST 2020: Analysis of Images, Social Networks and Texts pp 344–355Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12602))

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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References

  1. Bergmeir, C., Benítez, J.M.: On the use of cross-validation for time series predictor evaluation. Inf. Sci. 191, 192–213 (2012). data Mining for Software Trustworthiness

    Article  Google Scholar 

  2. Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  3. Casals, J., Garcia-Hiernaux, A., Jerez, M., Sotoca, S., Trindade, A.: State-Space Methods for Time Series Analysis: Theory, Applications and Software. Chapman and Hall/CRC Monographs on Statistics and Applied Probability. CRC Press, Boca Raton (2018)

    Google Scholar 

  4. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2016, pp. 785–794. ACM, New York (2016)

    Google Scholar 

  5. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734. Association for Computational Linguistics, Doha (2014)

    Google Scholar 

  6. Du, S., Li, T., Yang, Y., Horng, S.J.: Multivariate time series forecasting via attention-based encoder-decoder framework. Neurocomputing 388, 269–279 (2020)

    Article  Google Scholar 

  7. van den End, J.W., Hoeberichts, M., Tabbae, M.: Modelling scenario analysis and macro stress-testing. DNB Working papers 119, Netherlands Central Bank, Research Department, November 2006. https://ideas.repec.org/p/dnb/dnbwpp/119.html

  8. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org

  9. Habib, R., Barber, D.: Auxiliary variational MCMC. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=r1NJqsRctX

  10. Kingma, D.P., Welling, M.: An introduction to variational autoencoders. Found. Trends Mach. Learn. 12(4), 307–392 (2019)

    Article  Google Scholar 

  11. Ratzlaff, N., Fuxin, L.: HyperGAN: a generative model for diverse, performant neural networks. arXiv preprint arXiv:1901.11058 (2019)

  12. Rose, C., et al.: Introduction to Time Series and Forecasting. STSSTS. Springer, New York (2002). https://doi.org/10.1007/b97391

    Book  Google Scholar 

  13. Salimans, T., Kingma, D., Welling, M.: Markov chain monte carlo and variational inference: bridging the gap. In: International Conference on Machine Learning, pp. 1218–1226 (2015)

    Google Scholar 

  14. Song, J., Zhao, S., Ermon, S.: A-NICE-MC: adversarial training for MCMC. In: Advances in Neural Information Processing Systems, pp. 5140–5150 (2017)

    Google Scholar 

  15. Taylor, K.M., Procopio, M.J., Young, C.J., Meyer, F.G.: Estimation of arrival times from seismic waves: a manifold-based approach. Geophys. J. Int. 185(1), 435–452 (2011). https://doi.org/10.1111/j.1365-246X.2011.04947.x

    Article  Google Scholar 

  16. Watter, M., Springenberg, J., Boedecker, J., Riedmiller, M.: Embed to control: a locally linear latent dynamics model for control from raw images. In: Advances in Neural Information Processing Systems, pp. 2746–2754 (2015)

    Google Scholar 

  17. Yildiz, C., Heinonen, M., Lahdesmaki, H.: ODE VAE: deep generative second order ODEs with Bayesian neural networks. arXiv preprint arXiv:1905.10994 (2019)

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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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Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Skolkovo, 121205, Russia

    Evgeny Burnaev

Authors
  1. Evgeny Burnaev
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Correspondence to Evgeny Burnaev .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre lès Nancy, France

    Amedeo Napoli

  11. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  12. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  13. Università Ca' Foscari Venezia, Venice, Italy

    Marcello Pelillo

  14. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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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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  • DOI: https://doi.org/10.1007/978-3-030-72610-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72609-6

  • Online ISBN: 978-3-030-72610-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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