IDEAS home Printed from https://ideas.repec.org/h/bis/bisifc/57-33.html
   My bibliography  Save this book chapter

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.
(This abstract was borrowed from another version of this item.)

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
    as

    Download full text from publisher

    File URL: https://www.bis.org/ifc/publ/ifcb57_33.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Klee, Elizabeth, 2010. "Operational outages and aggregate uncertainty in the federal funds market," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2386-2402, October.
    2. León, Carlos, 2020. "Detecting anomalous payments networks: A dimensionality-reduction approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    3. Leonard Sabetti & Ronald Heijmans, 2020. "Shallow or deep? Detecting anomalous flows in the Canadian Automated Clearing and Settlement System using an autoencoder," Working Papers 681, DNB.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.
    4. Carolina E S Mattsson & Teodoro Criscione & Frank W Takes, 2022. "Circulation of a digital community currency," Papers 2207.08941, arXiv.org, revised Jun 2023.
    5. 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).
    6. 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).
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Irving Fisher Committee, 2022. "Machine learning in central banking," IFC Bulletins, Bank for International Settlements, number 57.
    2. León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020. "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    3. 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.
    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. 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.
    6. Eisenbach, Thomas M. & Kovner, Anna & Lee, Michael Junho, 2022. "Cyber risk and the U.S. financial system: A pre-mortem analysis," Journal of Financial Economics, Elsevier, vol. 145(3), pages 802-826.
    7. Nellen, Thomas, 2019. "Intraday liquidity facilities, late settlement fee and coordination," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 124-131.
    8. Foote, Elizabeth, 2014. "Information asymmetries and spillover risk in settlement systems," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 179-190.
    9. Bech, Morten & Monnet, Cyril, 2016. "A search-based model of the interbank money market and monetary policy implementation," Journal of Economic Theory, Elsevier, vol. 164(C), pages 32-67.
    10. Neville Arjani & Ronald Heijmans, 2020. "Is there anybody out there? Detecting operational outages from LVTS transaction data," Working Papers 683, DNB.
    11. Kopchak, Seth J., 2011. "The liquidity effect for open market operations," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3292-3299.
    12. 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).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bis:bisifc:57-33. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.