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 proceedingChapterAcademic


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 languageEnglish
Title of host publicationEnterprise Information Systems
Subtitle of host publication19th International Conference, ICEIS 2017
EditorsSlimane Hammoudi, Michal Smialek, Olivier Camp, Joaquim Filipe
Place of PublicationCham
PublisherSpringer Verlag
ISBN (Electronic)978-3-319-93375-7
ISBN (Print)978-3-319-93374-0
Publication statusPublished - 2017
Externally publishedYes

Publication series

SeriesLecture Notes in Business Information Processing


  • Anomaly detection
  • Autoencoders
  • Payment behavior
  • Real-Time gross settlement systems

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