Abstract
Fraudulent account detection is essential for businesses and online Internet enterprises, which can help to avoid financial loss and improve user experience. However, conventional solutions suffer from two main challenges which remain unresolved; first, it’s hard to monitor and detect fraud behaviors in real-time, and second, the features of the cheaters keep changing dynamically, which makes it hard to capture the most relevant features for the detection models. In this demonstration, we present a fraudulent account identification system called FAI, which can help to address the above challenges by exploring a multi-granularity sliding window strategy to construct the dynamic features, and both dynamic and static features are embedded together as the input of pre-training models. FAI also provides an interface that allows users to select sets of features in the spatio-temporal dimension flexibly, visualize the feature aggregation results, and assess the quality of fraud detection results. Demo video click here.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant No. 62002216), the Shanghai Sailing Program (Grant No. 20YF1414400), the Collaborative Innovation Platform of Electronic Information Master of Shanghai Polytechnic University (Grant NO. A10GY21F015), the Research Projects of Shanghai Polytechnic University (Grant No. EGD23DS05).
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Tian, Y., Zhang, Y., Chen, F., Wang, B., Wang, J., Meng, X. (2024). FAI: A Fraudulent Account Identification System. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14474. Springer, Singapore. https://doi.org/10.1007/978-981-99-9119-8_23
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DOI: https://doi.org/10.1007/978-981-99-9119-8_23
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