Classifying payment patterns with artificial neural networks: An autoencoder approach
John Arroyo and
Authors registered in the RePEc Author Service: Raul Morales Resendiz ()
Latin American Journal of Central Banking (previously Monetaria), 2020, vol. 1, issue 1
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)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Gold Open Access
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:lajcba:v:1:y:2020:i:1:s2666143820300132
Access Statistics for this article
Latin American Journal of Central Banking (previously Monetaria) is currently edited by Manuel Ramos-Francia
More articles in Latin American Journal of Central Banking (previously Monetaria) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().