Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/336293 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] Latin American Journal of Central Banking (LAJCB) [ISSN:] 2666-1438 [Volume:] 1 [Issue:] 1/4 [Article No.:] 100001 [Year:] 2020 [Pages:] 1-16
Publisher: 
Elsevier, Amsterdam
Abstract: 
Anomaly-detection methods are aimed at identifying observations that deviate manifestly from what is expected. Such methods are usually run on low-dimensional data, such as time series data. However, the increasing importance of high-dimensional payments and exposure data for financial oversight requires methods for detecting anomalous networks. To detect an anomalous network, dimensionality reduction allows measuring of the extent to which the network's main connective features (i.e. the structure) deviate from those regarded as typical. The key to dimensionality-reduction methods is the ability to reconstruct data with an error; this reconstruction error serves as a yardstick for deviation from what is typical. Principal component analysis (PCA) is used as a dimensionality-reduction method, and a clustering algorithm is used to classify reconstruction errors as normal or anomalous. Based on data from Colombia's large-value payments system and a set of synthetic anomalous networks created through simulations of intraday payments, detecting anomalous payments networks is feasible and promising for financial-oversight purposes.
Subjects: 
Anomaly detection
Clustering
Dimensionality
Network
Payments
JEL: 
E42
C38
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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