Detection and explanation of anomalous payment behavior in real-time gross settlement systems

Ron Triepels; Hennie Daniels; Ronald Heijmans

doi:10.1007/978-3-319-93375-7_8

Detection and explanation of anomalous payment behavior in real-time gross settlement systems

Ron Triepels, Hennie Daniels, Ronald Heijmans

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

Abstract

In this paper, we discuss how to apply an autoencoder to detect anomalies in payment data derived from an Real-Time Gross Settlement system. Moreover, we introduce a drill-down procedure to measure the extent to which the inflow or outflow of a particular bank explains an anomaly. Experimental results on real-world payment data show that our method can detect the liquidity problems of a bank when it was subject to a bank run with reasonable accuracy.

Original language	English
Title of host publication	Enterprise Information Systems
Subtitle of host publication	19th International Conference, ICEIS 2017
Editors	S. Hammoudi, M. Smialek, O. Camp, J. Filipe
Place of Publication	Cham
Publisher	Springer Verlag
Pages	145-161
DOIs	https://doi.org/10.1007/978-3-319-93375-7_8
Publication status	Published - 2018

Publication series

Name	Lecture Notes in Business Information Processing
Volume	321

Keywords

anomaly detection
autoencoders
payment behavior
real-time gross settlement systems

Access to Document

10.1007/978-3-319-93375-7_8

Cite this

Triepels, R., Daniels, H., & Heijmans, R. (2018). Detection and explanation of anomalous payment behavior in real-time gross settlement systems. In S. Hammoudi, M. Smialek, O. Camp, & J. Filipe (Eds.), Enterprise Information Systems: 19th International Conference, ICEIS 2017 (pp. 145-161). (Lecture Notes in Business Information Processing; Vol. 321). Springer Verlag. https://doi.org/10.1007/978-3-319-93375-7_8

Triepels, Ron ; Daniels, Hennie ; Heijmans, Ronald. / Detection and explanation of anomalous payment behavior in real-time gross settlement systems. Enterprise Information Systems: 19th International Conference, ICEIS 2017. editor / S. Hammoudi ; M. Smialek ; O. Camp ; J. Filipe. Cham : Springer Verlag, 2018. pp. 145-161 (Lecture Notes in Business Information Processing).

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abstract = "In this paper, we discuss how to apply an autoencoder to detect anomalies in payment data derived from an Real-Time Gross Settlement system. Moreover, we introduce a drill-down procedure to measure the extent to which the inflow or outflow of a particular bank explains an anomaly. Experimental results on real-world payment data show that our method can detect the liquidity problems of a bank when it was subject to a bank run with reasonable accuracy.",

keywords = "anomaly detection, autoencoders, payment behavior, real-time gross settlement systems",

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Triepels, R, Daniels, H & Heijmans, R 2018, Detection and explanation of anomalous payment behavior in real-time gross settlement systems. in S Hammoudi, M Smialek, O Camp & J Filipe (eds), Enterprise Information Systems: 19th International Conference, ICEIS 2017. Lecture Notes in Business Information Processing, vol. 321, Springer Verlag, Cham, pp. 145-161. https://doi.org/10.1007/978-3-319-93375-7_8

Detection and explanation of anomalous payment behavior in real-time gross settlement systems. / Triepels, Ron; Daniels, Hennie; Heijmans, Ronald.

Enterprise Information Systems: 19th International Conference, ICEIS 2017. ed. / S. Hammoudi; M. Smialek; O. Camp; J. Filipe. Cham : Springer Verlag, 2018. p. 145-161 (Lecture Notes in Business Information Processing; Vol. 321).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AB - In this paper, we discuss how to apply an autoencoder to detect anomalies in payment data derived from an Real-Time Gross Settlement system. Moreover, we introduce a drill-down procedure to measure the extent to which the inflow or outflow of a particular bank explains an anomaly. Experimental results on real-world payment data show that our method can detect the liquidity problems of a bank when it was subject to a bank run with reasonable accuracy.

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Triepels R, Daniels H, Heijmans R. Detection and explanation of anomalous payment behavior in real-time gross settlement systems. In Hammoudi S, Smialek M, Camp O, Filipe J, editors, Enterprise Information Systems: 19th International Conference, ICEIS 2017. Cham: Springer Verlag. 2018. p. 145-161. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-319-93375-7_8