Anomaly Detection in Real-Time Gross Payment Data

RJMA (Ron Johannes Matheus Antonius) Triepels, R Heijmans, Hennie Daniels

Research output: Chapter/Conference proceedingConference proceedingProfessional

3 Citations (Scopus)


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, (ICEIS 2017)
Number of pages9
Publication statusPublished - 2017

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