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Classifying payment patterns with artificial neural networks: An autoencoder approach

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  • Rubio, Jeniffer
  • Barucca, Paolo
  • Gage, Gerardo
  • Arroyo, John
  • Morales-Resendiz, Raúl

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.

Suggested Citation

  • Rubio, Jeniffer & Barucca, Paolo & Gage, Gerardo & Arroyo, John & Morales-Resendiz, Raúl, 2020. "Classifying payment patterns with artificial neural networks: An autoencoder approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    • Handle: RePEc:eee:lajcba:v:1:y:2020:i:1:s2666143820300132
    DOI: 10.1016/j.latcb.2020.100013
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. León, Carlos, 2020. "Detecting anomalous payments networks: A dimensionality-reduction approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Paulick, Jan & Berndsen, Ron & Diehl, Martin & Heijmans, Ronald, 2021. "No more Tears without Tiers? The Impact of Indirect Settlement on liquidity use in TARGET2," Other publications TiSEM 57477131-2199-46bf-a2f1-5, Tilburg University, School of Economics and Management.
    2. Carolina E S Mattsson & Teodoro Criscione & Frank W Takes, 2022. "Circulation of a digital community currency," Papers 2207.08941, arXiv.org, revised Jun 2023.
    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).

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    More about this item

    Keywords

    Market infrastructure; Neural network; Anomaly detection; Autoencoder; Artificial intelligence; Retail payments; Machine learning;
    All these keywords.

    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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