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GraphSIF: analyzing flow of payments in a Business-to-Business network to detect supplier impersonation
- Source :
- Applied Network Science, Applied Network Science, Springer, 2020, 5 (1), ⟨10.1007/s41109-020-00283-1⟩, Applied Network Science, Vol 5, Iss 1, Pp 1-31 (2020)
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- Supplier Impersonation Fraud (SIF) is a rising issue for Business-to-Business companies. The use of remote and quick digital transactions has made the task of identifying fraudsters more difficult. In this paper, we propose a data-driven fraud detection system whose goal is to provide an accurate estimation of financial transaction legitimacy by using the knowledge contained in the network of transactions created by the interaction of a company with its suppliers. We consider the real dataset collected by SIS-ID for this work.We propose to use a graph-based approach to design an Anomaly Detection System (ADS) based on a Self-Organizing Map (SOM) allowing us to label a suspicious transaction as either legitimate or fraudulent based on its similarity with frequently occurring transactions for a given company. Experiments demonstrate that our approach shows high consistency with expert knowledge on a real-life dataset, while performing faster than the expert system.
- Subjects :
- Computer Networks and Communications
Computer science
Accurate estimation
media_common.quotation_subject
B2B network
02 engineering and technology
Computer security
computer.software_genre
Financial networks
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
ComputingMilieux_MISCELLANEOUS
Graph-based feature engineering
media_common
Multidisciplinary
business.industry
lcsh:T57-57.97
Business-to-business
Payment
Expert system
Computational Mathematics
Fraud detection
Financial transaction
lcsh:Applied mathematics. Quantitative methods
Graph (abstract data type)
020201 artificial intelligence & image processing
Anomaly detection
business
computer
Database transaction
Subjects
Details
- Language :
- English
- ISSN :
- 23648228
- Database :
- OpenAIRE
- Journal :
- Applied Network Science, Applied Network Science, Springer, 2020, 5 (1), ⟨10.1007/s41109-020-00283-1⟩, Applied Network Science, Vol 5, Iss 1, Pp 1-31 (2020)
- Accession number :
- edsair.doi.dedup.....e8a82eff7b955539690608f290143e38