TY - GEN
T1 - InfDetect
T2 - 2019 IEEE International Conference on Big Data, Big Data 2019
AU - Chen, Cen
AU - Liang, Chen
AU - Lin, Jianbin
AU - Wang, Li
AU - Liu, Ziqi
AU - Yang, Xinxing
AU - Zhou, Jun
AU - Shuang, Yang
AU - Qi, Yuan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - The insurance industry has been creating innovative products around the emerging online shopping activities. Such ecommerce insurance is designed to protect buyers from potential risks such as impulse purchases and counterfeits. Fraudulent claims towards online insurance typically involve multiple parties such as buyers, sellers, and express companies, and they could lead to heavy financial losses. In order to uncover the relations behind organized fraudsters and detect fraudulent claims, we developed a large-scale insurance fraud detection system, i.e., InfDetect, which provides interfaces for commonly used graphs, standard data processing procedures, and a uniform graph learning platform. InfDetect is able to process big graphs containing up to 100 millions of nodes and billions of edges.In this paper, we investigate different graphs to facilitate fraudster mining, such as a device-sharing graph, a transaction graph, a friendship graph, and a buyer-seller graph. These graphs are fed to a uniform graph learning platform containing supervised and unsupervised graph learning algorithms. Cases on widely applied e-commerce insurance are described to demonstrate the usage and capability of our system. InfDetect has successfully detected thousands of fraudulent claims and saved over tens of thousands of dollars daily.
AB - The insurance industry has been creating innovative products around the emerging online shopping activities. Such ecommerce insurance is designed to protect buyers from potential risks such as impulse purchases and counterfeits. Fraudulent claims towards online insurance typically involve multiple parties such as buyers, sellers, and express companies, and they could lead to heavy financial losses. In order to uncover the relations behind organized fraudsters and detect fraudulent claims, we developed a large-scale insurance fraud detection system, i.e., InfDetect, which provides interfaces for commonly used graphs, standard data processing procedures, and a uniform graph learning platform. InfDetect is able to process big graphs containing up to 100 millions of nodes and billions of edges.In this paper, we investigate different graphs to facilitate fraudster mining, such as a device-sharing graph, a transaction graph, a friendship graph, and a buyer-seller graph. These graphs are fed to a uniform graph learning platform containing supervised and unsupervised graph learning algorithms. Cases on widely applied e-commerce insurance are described to demonstrate the usage and capability of our system. InfDetect has successfully detected thousands of fraudulent claims and saved over tens of thousands of dollars daily.
KW - Graph learning
KW - e-commerce insurance
KW - fraud detection system
KW - network learning
UR - https://www.scopus.com/pages/publications/85081374055
U2 - 10.1109/BigData47090.2019.9006115
DO - 10.1109/BigData47090.2019.9006115
M3 - 会议稿件
AN - SCOPUS:85081374055
T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
SP - 1765
EP - 1773
BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
A2 - Baru, Chaitanya
A2 - Huan, Jun
A2 - Khan, Latifur
A2 - Hu, Xiaohua Tony
A2 - Ak, Ronay
A2 - Tian, Yuanyuan
A2 - Barga, Roger
A2 - Zaniolo, Carlo
A2 - Lee, Kisung
A2 - Ye, Yanfang Fanny
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2019 through 12 December 2019
ER -