A NOVEL BEHAVIORAL BASED FRAUD DETECTION SYSTEM USING MACHINE LEARNING workers. It impacts user experiences, effects platform's repu
DOI:
https://doi.org/10.48047/3y74w983Keywords:
Fraud, Economics, Behavior, Machine Learning and Support Vector Machine.Abstract
Frauds are caused by increasing e-commerce platforms by developments of rapid commercial and technologies that effects harm of these platforms. Now a day’s credit cards usage is becoming highly popular, so, it is important to detect fraud to secure user accounts timely and accurately. To identify the frauds existing models are using manually process like original or aggregated features as their transactional representations but hidden behaviors of fraudulent are not identified. Because of fraudulent activity, company's reputation will be damaged and it leads to large financial losses, so, in financial industry fraud detection is becoming challenging.
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References
P. Li, H. Yu, X. Luo and J. Wu, "LGM GNN: A Local and Global Aware Memory Based Graph Neural Network for Fraud Detection," in IEEE Transactions on Big Data, vol. 9, no. 4, pp. 1116-1127, 1 Aug. 2023, doi: 10.1109/TBDATA.2023.3234529.
J. Yu et al., "Temporal Insights for Group-Based Fraud Detection on eCommerce Platforms," in IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 2, pp. 951-965, Feb. 2025, doi: 10.1109/TKDE.2024.3485127.
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