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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Keywords Anomaly detection, Autoencoders, Payment behavior, Real-Time gross settlement systems
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Series Lecture Notes in Business Information Processing (LNBIP)
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Triepels, R.J.M.A, Daniels, H.A.M, & Heijmans, R. (Ronald). (2018). Detection and explanation of anomalous payment behavior in real-time gross settlement systems. In Lecture Notes in Business Information Processing (LNBIP). doi:10.1007/978-3-319-93375-7_8