The Experts below are selected from a list of 5019 Experts worldwide ranked by ideXlab platform
Sharon Tennyson - One of the best experts on this subject based on the ideXlab platform.
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Insurance Fraud and optimal claims settlement strategies
Social Science Research Network, 2002Co-Authors: Sharon Tennyson, Keith J CrockerAbstract:We examine the optimal claims settlement strategy for a liability insurer when claimants can permanently misrepresent their loss by engaging in costly claims falsification. In this environment claims auditing is not a possible deterrent to Fraud, and the settlement strategy consists of an indemnification profile relating the Insurance payment to the claimed amount of loss. The optimal indemnification profile is shown to involve systematic underpayment of claims at the margin as a means to deter loss exaggeration, with the extent of underpayment limited by expected litigation costs and potential bad faith claims. The key testable implication of the theory is that the extent of underpayment should be greater for classes of claims for which loss exaggeration is easier. Empirical analysis of Insurance settlements for bodily injury liability in automobile accidents confirms this prediction. This suggests that liability insurers optimally choose claims payment strategies to mitigate claimants' incentives to exaggerate loss.
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Insurance Fraud and optimal claims settlement strategies
The Journal of Law and Economics, 2002Co-Authors: Keith J Crocker, Sharon TennysonAbstract:Abstract We examine the optimal claims settlement strategy for a liability insurer when claimants can permanently misrepresent their losses by engaging in costly claims falsification. In this environment, claims auditing is not a possible deterrent to Fraud, and the settlement strategy consists of an indemnification profile that relates the Insurance payment to the claimed amount of loss. The optimal indemnification profile is shown to involve systematic underpayment of claims at the margin as a means to deter loss exaggeration, with the extent of underpayment limited by expected litigation costs and potential bad‐faith claims. The key testable implication of the theory is that the extent of underpayment should be greater for classes of claims for which loss exaggeration is easier. Empirical analysis of Insurance settlements for bodily injury liability in automobile accidents confirms this prediction. This suggests that liability insurers optimally choose claims payment strategies to lessen a claimant's i...
Keith J Crocker - One of the best experts on this subject based on the ideXlab platform.
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Insurance Fraud and optimal claims settlement strategies
Social Science Research Network, 2002Co-Authors: Sharon Tennyson, Keith J CrockerAbstract:We examine the optimal claims settlement strategy for a liability insurer when claimants can permanently misrepresent their loss by engaging in costly claims falsification. In this environment claims auditing is not a possible deterrent to Fraud, and the settlement strategy consists of an indemnification profile relating the Insurance payment to the claimed amount of loss. The optimal indemnification profile is shown to involve systematic underpayment of claims at the margin as a means to deter loss exaggeration, with the extent of underpayment limited by expected litigation costs and potential bad faith claims. The key testable implication of the theory is that the extent of underpayment should be greater for classes of claims for which loss exaggeration is easier. Empirical analysis of Insurance settlements for bodily injury liability in automobile accidents confirms this prediction. This suggests that liability insurers optimally choose claims payment strategies to mitigate claimants' incentives to exaggerate loss.
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Insurance Fraud and optimal claims settlement strategies
The Journal of Law and Economics, 2002Co-Authors: Keith J Crocker, Sharon TennysonAbstract:Abstract We examine the optimal claims settlement strategy for a liability insurer when claimants can permanently misrepresent their losses by engaging in costly claims falsification. In this environment, claims auditing is not a possible deterrent to Fraud, and the settlement strategy consists of an indemnification profile that relates the Insurance payment to the claimed amount of loss. The optimal indemnification profile is shown to involve systematic underpayment of claims at the margin as a means to deter loss exaggeration, with the extent of underpayment limited by expected litigation costs and potential bad‐faith claims. The key testable implication of the theory is that the extent of underpayment should be greater for classes of claims for which loss exaggeration is easier. Empirical analysis of Insurance settlements for bodily injury liability in automobile accidents confirms this prediction. This suggests that liability insurers optimally choose claims payment strategies to lessen a claimant's i...
Yuzhe Zhang - One of the best experts on this subject based on the ideXlab platform.
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Unemployment Insurance Fraud and optimal monitoring
American Economic Journal: Macroeconomics, 2015Co-Authors: David L. Fuller, Brinda Ravikumar, Yuzhe ZhangAbstract:An important incentive problem for the design of unemployment Insurance is the Fraudulent collection of unemployment benefits by workers who are gainfully employed. We show how to efficiently use a combination of tax/subsidy and monitoring to prevent such Fraud. The optimal policy monitors the unemployed at fixed intervals. Employment tax is nonmonotonic: it increases between verifications but decreases after a verification. Unemployment benefits are relatively flat between verifications but decrease sharply after a verification. Our quantitative analysis suggests that the optimal monitoring cost is 60 percent of the cost in the current U.S. system.
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unemployment Insurance Fraud and optimal monitoring
Research Papers in Economics, 2012Co-Authors: David L. Fuller, Brinda Ravikumar, Yuzhe ZhangAbstract:The most prevalent incentive problem in the U.S. unemployment Insurance system is that individuals collect unemployment benefits while being gainfully employed. We show how the unemployment Insurance authority can efficiently use a combination of tax/subsidy and monitoring to prevent such Fraud. The optimal policy monitors the unemployed at fixed intervals. Employment tax is nonmonotonic: it increases between verifications but decreases after a verification. Unemployment benefits are relatively flat between verifications but decrease sharply after a verification.
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unemployment Insurance Fraud and optimal monitoring
American Economic Journal: Macroeconomics, 2012Co-Authors: David L. Fuller, Brinda Ravikumar, Yuzhe ZhangAbstract:*An important incentive problem for the design of unemployment Insurance is the Fraudulent collection of unemployment benefits by workers who are gainfully employed. We show how to efficiently use a combination of tax/subsidy and monitoring to prevent such Fraud. The optimal policy monitors the unemployed at fixed intervals. Employment tax is nonmonotonic: it increases between verifications but decreases after a verification. Unemployment benefits are relatively flat between verifications but decrease sharply after a verification. Our quantitative analysis suggests that the optimal monitoring cost is 60 percent of the cost in the current US system. (JEL D82, H24, J64, J65)
David L. Fuller - One of the best experts on this subject based on the ideXlab platform.
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Unemployment Insurance Fraud and optimal monitoring
American Economic Journal: Macroeconomics, 2015Co-Authors: David L. Fuller, Brinda Ravikumar, Yuzhe ZhangAbstract:An important incentive problem for the design of unemployment Insurance is the Fraudulent collection of unemployment benefits by workers who are gainfully employed. We show how to efficiently use a combination of tax/subsidy and monitoring to prevent such Fraud. The optimal policy monitors the unemployed at fixed intervals. Employment tax is nonmonotonic: it increases between verifications but decreases after a verification. Unemployment benefits are relatively flat between verifications but decrease sharply after a verification. Our quantitative analysis suggests that the optimal monitoring cost is 60 percent of the cost in the current U.S. system.
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unemployment Insurance Fraud and optimal monitoring
Research Papers in Economics, 2012Co-Authors: David L. Fuller, Brinda Ravikumar, Yuzhe ZhangAbstract:The most prevalent incentive problem in the U.S. unemployment Insurance system is that individuals collect unemployment benefits while being gainfully employed. We show how the unemployment Insurance authority can efficiently use a combination of tax/subsidy and monitoring to prevent such Fraud. The optimal policy monitors the unemployed at fixed intervals. Employment tax is nonmonotonic: it increases between verifications but decreases after a verification. Unemployment benefits are relatively flat between verifications but decrease sharply after a verification.
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unemployment Insurance Fraud and optimal monitoring
American Economic Journal: Macroeconomics, 2012Co-Authors: David L. Fuller, Brinda Ravikumar, Yuzhe ZhangAbstract:*An important incentive problem for the design of unemployment Insurance is the Fraudulent collection of unemployment benefits by workers who are gainfully employed. We show how to efficiently use a combination of tax/subsidy and monitoring to prevent such Fraud. The optimal policy monitors the unemployed at fixed intervals. Employment tax is nonmonotonic: it increases between verifications but decreases after a verification. Unemployment benefits are relatively flat between verifications but decrease sharply after a verification. Our quantitative analysis suggests that the optimal monitoring cost is 60 percent of the cost in the current US system. (JEL D82, H24, J64, J65)
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.