Classifying payment patterns with artificial neural networks: An autoencoder approach

Jeniffer Rubio, Paolo Barucca, Gerardo Gage, John Arroyo and Raúl Morales-Resendiz
Authors registered in the RePEc Author Service: Jeniffer Rubio Abril () and Raul Morales Resendiz

Latin American Journal of Central Banking (previously Monetaria), 2020, vol. 1, issue 1

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.

Keywords: Market infrastructure; Neural network; Anomaly detection; Autoencoder; Artificial intelligence; Retail payments; Machine learning (search for similar items in EconPapers)
JEL-codes: C45 E42 E58 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Related works:
Chapter: Classifying payment patterns with artificial neural networks: an autoencoder approach (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:lajcba:v:1:y:2020:i:1:s2666143820300132

DOI: 10.1016/j.latcb.2020.100013

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