Graph Neural Networks for Transaction Fraud: Inductive Generalisation to Unseen Accounts

Authors

  • Milena Yankova-Dim Centre for Machine Learning Research, Dobrudzha Institute of Applied Artificial Intelligence, Dobrich, Bulgaria Author
  • Melina Antoniadou Department of Quantitative Finance, Marathasa Centre for Machine Intelligence, Kalopanayiotis, Cyprus Author
  • Folke Wahlgren Department of Information Security, Bohuslan Institute of Applied Artificial Intelligence, Uddevalla, Sweden Author

DOI:

https://doi.org/10.5281/zenodo.22657259

Keywords:

Graph Neural Networks for Transaction Fraud; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Graph Neural Networks for Transaction Fraud is an area of active development in applied machine learning, with evidence that
has accumulated unevenly across its component questions. We review the current evidence base for graph neural networks for
transaction fraud, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has
converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We
conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance

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Castellani R, Ristic C, et al. "Graph Neural Networks for Transaction Fraud: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.2196/48633

Dumitrescu A, Andric D, et al. "Graph Neural Networks for Transaction Fraud: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.1371/journal.pone.0305949

Eriksdottir B, Brankovic E, et al. "Graph Neural Networks for Transaction Fraud: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.1371/journal.pdig.0000514

Farkas I, Ionescu F, et al. "Graph Neural Networks for Transaction Fraud: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.17219/acem/208596

Grabowski P, Petrescu G, et al. "Graph Neural Networks for Transaction Fraud: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.7759/cureus.104086

Hendriksen W, Wojcik H, et al. "Graph Neural Networks for Transaction Fraud: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.1038/s41746-023-00852-5

Ionescu F, Foscarini I, et al. "Graph Neural Networks for Transaction Fraud: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.1016/j.mayocpiqo.2022.09.004

Jankowiak G, Grabowski J, et al. "Graph Neural Networks for Transaction Fraud: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.1177/08404704241291226

Kowalczyk N, Novotna K, et al. "Graph Neural Networks for Transaction Fraud: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.1093/medlaw/fwad013

Lindqvist U, Urbanek L, et al. "Graph Neural Networks for Transaction Fraud: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.2196/89834

Marchetti D, Doncheva M, et al. "Graph Neural Networks for Transaction Fraud: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.1007/s10916-024-02104-9

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Published

2022-08-12

Issue

Vol. 1 No. 2 (2022): Vol 1 Issue 2 2022

Section

Articles

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