Anomaly Detection in Real-Time Gross Settlement Systems

Ron Triepels, Hennie Daniels, Ronald Heijmans

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

Abstract

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
Pages433-441
Volume3
ISBN (Print)978-989-758-247-9
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event19th International Conference on Enterprise Information Systems - Porto, Portugal
Duration: 26 Apr 201729 Apr 2017
http://www.iceis.org/?y=2017

Conference

Conference19th International Conference on Enterprise Information Systems
Abbreviated titleICEIS 2017
CountryPortugal
CityPorto
Period26/04/1729/04/17
Internet address

Keywords

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

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