GUFAD: A Graph-based Unsupervised Fraud Account Detection Framework
Authors: 
Wei Zhang, Yufei Zhang, Yilin Huang, Fangshu Chen, Jiahui Wang, Xiaoming HuAuthors Info & Claims
Wei Zhang, Yufei Zhang, Yilin Huang, Fangshu Chen, Jiahui Wang, Xiaoming HuAuthors Info & ClaimsPages 401 - 406
Abstract
The detection of fraud accounts at scenarios of registration and login is a critical task for Internet enterprises, which can help to avoid economic losses at a very early stage. In industry, most companies tend to deploy supervised models such as rule-based models. However, these methods are significantly restricted in scalability since they highly rely on domain knowledge and manual annotations. Therefore, we designed a novel account graph analysis approach for uncovering fraudulent patterns.
The framework explores an advanced feature-account bigraph to calculate the aggregation of accounts and applies a community detection algorithm to detect organized fraud groups. Next, a graph embedding and clustering algorithm is introduced to further analyze the types of aggregated communities, which can help to reduce account misclassification. Furthermore, a novelty method POMV is designed to explore the patterns of missing values. And two dynamic feature aggregation methods based on multi-granularity sliding windows are proposed to construct expressive features that can help to improve the quality of the account graph. The proposed framework achieves 0.82 F1_Score on average, which significantly outperforms the off-the-shelf models by 3% to 16%.
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Index Terms
- GUFAD: A Graph-based Unsupervised Fraud Account Detection Framework
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Published In
October 2023
1065 pages
ISBN:9798400709449
DOI:10.1145/3650215
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Published: 16 April 2024
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ICMLCA 2023
ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application
October 27 - 29, 2023
Hangzhou, China
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