Anomaly Detection in Real-Time Gross Settlement Systems

Ron Triepels, Hennie Daniels, Ronald Heijmans


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.


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Paper Citation

in Harvard Style

Triepels R., Daniels H. and Heijmans R. (2017). Anomaly Detection in Real-Time Gross Settlement Systems . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 433-441. DOI: 10.5220/0006333004330441

in Bibtex Style

author={Ron Triepels and Hennie Daniels and Ronald Heijmans},
title={Anomaly Detection in Real-Time Gross Settlement Systems},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Anomaly Detection in Real-Time Gross Settlement Systems
SN - 978-989-758-247-9
AU - Triepels R.
AU - Daniels H.
AU - Heijmans R.
PY - 2017
SP - 433
EP - 441
DO - 10.5220/0006333004330441