Anomaly detection in real-time gross payment data

Jazyk:	angličtina
Rok vydání:	2017
Předmět:	autoencoder real-time gross settlement system neural network Data_CODINGANDINFORMATIONTHEORY anomaly detection
DOI:	10.5220/0006333004330441
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dris___00893::8da33c4654c689d071f40d14fe1f82fc https://doi.org/10.5220/0006333004330441 Zobrazit plný text záznamu