Anomaly Detection in Real-Time Gross Settlement Systems

Ron Triepels*, Hennie Daniels, Ronald Heijmans

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


We discuss how an autoencoder can detect system-level anomalies in a real-time gross settlement system by reconstructing a set of liquidity vectors. A liquidity vector is an aggregated representation of the underlying payment network of a settlement system for a particular time interval. Furthermore, we evaluate the performance of two autoencoders on real-world payment data extracted from the TARGET2 settlement system. We do this by generating different types of artificial bank runs in the data and determining how the autoencoders respond. Our experimental results show that the autoencoders are able to detect unexpected changes in the liquidity flows between banks.
Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Enterprise Information Systems
Place of PublicationPorto, Portugal
PublisherScitepress - Science And Technology Publications
ISBN (Print)978-989-758-247-9
Publication statusPublished - 2017
Externally publishedYes
Event19th International Conference on Enterprise Information Systems - Porto, Portugal
Duration: 26 Apr 201729 Apr 2017


Conference19th International Conference on Enterprise Information Systems
Abbreviated titleICEIS 2017
Internet address


  • Anomaly detection
  • Neural network
  • Autoencoder
  • Real-Time gross settlement system

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