Transaction fraud persistence based on graph algorithm

TianLin Zhang; SongHua Li; RongZhi Hou; JiaYao Cao; YunZhuo Qiao; XiaoRui Jing

doi:10.1117/12.2672786

28 March 2023 Transaction fraud persistence based on graph algorithm

TianLin Zhang, SongHua Li, RongZhi Hou, JiaYao Cao, YunZhuo Qiao, XiaoRui Jing

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125973U (2023) https://doi.org/10.1117/12.2672786
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

At the time mobile devices and online payment make people’s life more convenient, they caused an increasing number of fraud cases in recent years. In this paper, we represented trading data as graphs by graph machine learning and setting up a high-performance model which could detect fraudulent transactions automatically. The datasets that the paper used were the fraudulent transactions dataset on Kaggle’s credit cards. By random under-sampling, it was processed and shown as bipartite graphs which were substituted into our training models after being processed by graph embedding algorithm. Finally, the optimal model was found by the coming out results. The result reveals that average embedder algorithm could detect fraud more precisely than the other three algorithms.

Citation Download Citation

TianLin Zhang, SongHua Li, RongZhi Hou, JiaYao Cao, YunZhuo Qiao, and XiaoRui Jing "Transaction fraud persistence based on graph algorithm", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125973U (28 March 2023); https://doi.org/10.1117/12.2672786

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