Latent representations of transaction network graphs in continuous vector spaces as features for money laundering detection
| dc.contributor.author | Wagner, Dominik | |
| dc.contributor.editor | Becker, Michael | |
| dc.date.accessioned | 2019-10-14T12:09:09Z | |
| dc.date.available | 2019-10-14T12:09:09Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | This paper explores the construction of network graphs from a large ban ktransaction dataset and draws from Ąndings in language modeling and unsupervised learning to transform these graphs into multidimensional vector representations. Such latent representations encode relationships and community structures within the transaction network. Three classiĄers with varying complexity are trained on these latent representations to detect suspicious behavior with respect to money laundering. The specific challenges accompanying highly imbalanced classes are discussed as well and two strategies to overcome these challenges are compared. | en |
| dc.identifier.isbn | 978-3-88579-449-3 | |
| dc.identifier.pissn | 1614-3213 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/28992 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | SKILL 2019 - Studierendenkonferenz Informatik | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Seminars, Volume S-15 | |
| dc.subject | feature learning | |
| dc.subject | graph embeddings | |
| dc.subject | latent representations | |
| dc.subject | DeepWalk | |
| dc.subject | anti-money laundering | |
| dc.subject | language modeling | |
| dc.subject | class imbalances | |
| dc.subject | SMOTE | |
| dc.subject | machine learning | |
| dc.subject | support vector machine | |
| dc.subject | naive bayes | |
| dc.subject | multilayer perceptron | |
| dc.title | Latent representations of transaction network graphs in continuous vector spaces as features for money laundering detection | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 154 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 143 | |
| gi.conference.date | 25.-26. September 2019 | |
| gi.conference.location | Kassel | |
| gi.conference.sessiontitle | Security |
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