The Experts below are selected from a list of 60 Experts worldwide ranked by ideXlab platform
Shuang Yang - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Uncovering Insurance Fraud Conspiracy with Network Learning
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019Co-Authors: Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Shuang YangAbstract:Fraudulent Claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially Fraudulent Claims everyday. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent Fraudulent insurance Claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among Claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.
Chen Liang - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Uncovering Insurance Fraud Conspiracy with Network Learning
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019Co-Authors: Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Shuang YangAbstract:Fraudulent Claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially Fraudulent Claims everyday. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent Fraudulent insurance Claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among Claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.
Bin Liu - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Uncovering Insurance Fraud Conspiracy with Network Learning
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019Co-Authors: Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Shuang YangAbstract:Fraudulent Claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially Fraudulent Claims everyday. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent Fraudulent insurance Claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among Claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.
Ziqi Liu - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Uncovering Insurance Fraud Conspiracy with Network Learning
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019Co-Authors: Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Shuang YangAbstract:Fraudulent Claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially Fraudulent Claims everyday. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent Fraudulent insurance Claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among Claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.
Jun Zhou - One of the best experts on this subject based on the ideXlab platform.
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SIGIR - Uncovering Insurance Fraud Conspiracy with Network Learning
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019Co-Authors: Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Shuang YangAbstract:Fraudulent Claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially Fraudulent Claims everyday. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent Fraudulent insurance Claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among Claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.
