Technical identification of an automobile insurance fraud case

2021 ◽  
Author(s):  
Xingfeng Mao ◽  
Xiaoyong Jiang ◽  
Hongjian Zhou
Keyword(s):  
Automobile Insurance ◽  
Insurance Fraud

Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection

2019 ◽  
Vol 36 (3) ◽  
pp. 2333-2344 ◽  
Author(s):  
Santosh Kumar Majhi ◽  
Subho Bhatachharya ◽  
Rosy Pradhan ◽  
Shubhra Biswal
Keyword(s):  
Fuzzy Clustering ◽  
Fraud Detection ◽  
Automobile Insurance ◽  
Insurance Fraud ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm

Examining Teachers Intention in Committing Automobile Insurance Fraud Using Theory of Reasoned Action

2018 ◽  
Vol 24 (4) ◽  
pp. 2312-2315
Author(s):  
Ahmad Zainal Abidin Abd Razak ◽  
Nurul Hidayah Mohd Yusof ◽  
Norsamsinar Samsudin ◽  
Zaiton Wahid
Keyword(s):  
Theory Of Reasoned Action ◽  
Reasoned Action ◽  
Automobile Insurance ◽  
Insurance Fraud

Research on automobile insurance fraud identification based on fuzzy association rules

2021 ◽  
pp. 1-14
Author(s):  
Chun Yan ◽  
Jiahui Liu ◽  
Wei Liu ◽  
Xinhong Liu
Keyword(s):  
Simulated Annealing ◽  
Association Rules ◽  
Insurance Industry ◽  
Automobile Insurance ◽  
Insurance Fraud ◽  
Fuzzy Data ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Fuzzy C Means

With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims.

The Effectiveness of Insurance Fraud Statutes: Evidence from Automobile Insurance

2005 ◽  
Author(s):  
Robert E. Hoyt ◽  
David B. Mustard ◽  
Lawrence S. Powell
Keyword(s):  
Automobile Insurance ◽  
Insurance Fraud
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