Real-time Anomaly Detection on Financial Data

Real-time Anomaly Detection on Financial Data

This work presents an investigation of tailoring Network Representation Learning (NRL) for an application in the Financial Industry. NRL approaches are data-driven models that learn how to encode graph structures into low-dimensional vector spaces, which can be further exploited by downstream Machin...

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Bibliographic Details
Main Author: Martignano, Anna
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2020
Subjects:
Network Representation Learning
Anomaly Detection
Financial Industry
Graph Neural Networks
Dynamic Graphs
Heterogeneous Graphs
Nätverkiskt Representationsinlärning
Avvikelse av Anomali
Finansiell Industri
Grafiskt Neuralt Nätverk
Dynamiska Grafer
Heterogena Grafer
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281832

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281832

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