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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Detection and Explanation of Anomalous Payment Behavior in Real-Time Gross Settlement Systems

verfasst von : Ron Triepels, Hennie Daniels, Ronald Heijmans

Erschienen in: Enterprise Information Systems

Verlag: Springer International Publishing

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Abstract

In this paper, we discuss how to apply an autoencoder to detect anomalies in payment data derived from an Real-Time Gross Settlement system. Moreover, we introduce a drill-down procedure to measure the extent to which the inflow or outflow of a particular bank explains an anomaly. Experimental results on real-world payment data show that our method can detect the liquidity problems of a bank when it was subject to a bank run with reasonable accuracy.

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Vorheriges Kapitel Triplet Markov Chains Based- Estimation of Nonstationary Latent Variables Hidden with Independent Noise
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Fußnoten
1
Many FMIs settle their positions in an RTGS system.
 
2
A feature of many RTGS systems is that banks may initiate payments on behalf of other banks. This is known as tiering, see e.g. [1]. Payments settled between indirect participants in the settlement system via the same direct participant are recorded as internal payments of the direct participant.
 
3
For a more detailed description of TARGET2, see [15, 16].
 
4
The extent to which banks actively participated in the settlement system was based on the number of payments that they initiated each month.
 
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Metadaten
Titel
Detection and Explanation of Anomalous Payment Behavior in Real-Time Gross Settlement Systems
verfasst von
Ron Triepels
Hennie Daniels
Ronald Heijmans
Copyright-Jahr
2018
Buch
Enterprise Information Systems

Print ISBN: 978-3-319-93374-0

Electronic ISBN: 978-3-319-93375-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-93375-7_8