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Authors: Ron Triepels 1 ; Hennie Daniels 2 and Ronald Heijmans 3

Affiliations: 1 Tilburg University and De Nederlandsche Bank, Netherlands ; 2 Tilburg University and Erasmus University, Netherlands ; 3 De Nederlandsche Bank, Netherlands

Keyword(s): Anomaly Detection, Neural Network, Autoencoder, Real-Time Gross Settlement System.

Related Ontology Subjects/Areas/Topics: Applications of Expert Systems ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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.

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Paper citation in several formats:
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; ISSN 2184-4992, SciTePress, pages 433-441. DOI: 10.5220/0006333004330441

@conference{iceis17,
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},
year={2017},
pages={433-441},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006333004330441},
isbn={978-989-758-247-9},
issn={2184-4992},
}

TY - CONF

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
IS - 2184-4992
AU - Triepels, R.
AU - Daniels, H.
AU - Heijmans, R.
PY - 2017
SP - 433
EP - 441
DO - 10.5220/0006333004330441
PB - SciTePress

- Science and Technology Publications, Lda.
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