Application of Graph Neural Networks to fraud classification problems in the insurance and financial domain
| dc.contributor.author | Becher, Jona | |
| dc.contributor.author | Schäfer, Andreas | |
| dc.contributor.editor | Klein, Maike | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Winter, Cornelia | |
| dc.contributor.editor | Gergeleit, Martin | |
| dc.contributor.editor | Martin, Ludger | |
| dc.date.accessioned | 2024-10-21T18:24:13Z | |
| dc.date.available | 2024-10-21T18:24:13Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Intentional loan defaults and fraudulent insurance claims result in billions of euros in losses each year in Germany. Detecting suspicious networks of individuals involved in credit, loans, and insurance claims is crucial for fraud prevention in the financial and insurance domain. These networks can be modeled as undirected graphs with properties assigned to nodes and edges. In this paper, we apply Graph Neural Networks (GNNs) to set up graph- and node-level classifications. Taking this novel approach enables us to share insights into the advantages and practical challenges of using GNNs. The classification results reveal that node-level classification with network background is superior to conventional classification without network background. Graph-level classification shows promising performance in selecting subnetworks for further investigation at node level. GNN explainability is used for analyzing, interpreting, and visualizing classification results for a better understanding of fraudulent networks. | en |
| dc.identifier.doi | 10.18420/inf2024_117 | |
| dc.identifier.isbn | 978-3-88579-746-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45089 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2024 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-352 | |
| dc.subject | GNN classification | |
| dc.subject | Explainability | |
| dc.subject | Fraud patterns | |
| dc.subject | Financial and insurance domain | |
| dc.title | Application of Graph Neural Networks to fraud classification problems in the insurance and financial domain | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 1345 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 1335 | |
| gi.conference.date | 24.-26. September 2024 | |
| gi.conference.location | Wiesbaden | |
| gi.conference.sessiontitle | AI@WORK |
Dateien
Originalbündel
1 - 1 von 1
Lade...
- Name:
- Becher_Schaefer_Application_of_graph_neuronal_networks.pdf
- Größe:
- 936.16 KB
- Format:
- Adobe Portable Document Format