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Classifying payment patterns with artificial neural networks: an autoencoder approach

In: Machine learning in central banking

Author

Listed:
  • Luis Gerardo Gage
  • Raúl Morales-Resendiz
  • John Arroyo
  • Jeniffer Rubio
  • Paolo Barucca

Abstract

Payments and market infrastructures are the backbone of modern financial systems and play a key role in the economy. One of their main goals is to manage systemic risk, especially in the case of systemically important payment systems (SIPS) serving interbank funds transfers. We develop an autoencoder for the Sistema de Pagos Interbancarios (SPI) of Ecuador, which is the largest SIPS, to detect potential anomalies stemming from payment patterns. Our work is similar to Triepels et al. (2018) and Sabetti and Heijmans (2020). We train four different autoencoder models using intraday data structured in three time-intervals for the SPI settlement activity to reconstruct its related payments network. We introduce bank run simulations to feature a baseline scenario and identify relevant autoencoder parametrizations for anomaly detection.
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Suggested Citation

  • Luis Gerardo Gage & Raúl Morales-Resendiz & John Arroyo & Jeniffer Rubio & Paolo Barucca, 2022. "Classifying payment patterns with artificial neural networks: an autoencoder approach," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    • Handle: RePEc:bis:bisifc:57-33
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    Cited by:

    1. is not listed on IDEAS
    2. Jan Paulick & Ron Berndsen & Martin Diehl & Ronald Heijmans, 2024. "No more tears without tiers? The impact of indirect settlement on liquidity use in TARGET2," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(2), pages 425-458, May.
    3. Arévalo, Franklim & Barucca, Paolo & Téllez-León, Isela-Elizabeth & Rodríguez, William & Gage, Gerardo & Morales, Raúl, 2022. "Identifying clusters of anomalous payments in the salvadorian payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(1).
    4. Sabetti, Leonard & Heijmans, Ronald, 2021. "Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).
    5. Heijmans, Ronald & Wendt, Froukelien, 2023. "Measuring the impact of a failing participant in payment systems," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(4).
    6. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.
    7. Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    8. Carolina E S Mattsson & Teodoro Criscione & Frank W Takes, 2022. "Circulation of a digital community currency," Papers 2207.08941, arXiv.org, revised Jun 2023.
    9. Ludivia Hernandez Aros & Luisa Ximena Bustamante Molano & Fernando Gutierrez-Portela & John Johver Moreno Hernandez & Mario Samuel Rodríguez Barrero, 2024. "Financial fraud detection through the application of machine learning techniques: a literature review," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-22, December.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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