Fraudulent automobile insurance claims are not only a loss for insurance companies, but also for their policyholders. In order for insurance companies to prevent significant loss from false claims, they must raise their premiums for the policyholders. The goal of this research is to develop a decision making algorithm to determine whether a claim is classified as fraudulent based on the observed characteristics of a claim, which can in turn help prevent future loss. The data includes 923 cases of false claims, 14,497 cases of true claims and 33 describing variables from the years 1994 to 1996. To achieve the goal of this research, parametric and nonparametric methods are used to determine what variables play a major role in detecting fraudulent claims. These methods include logistic regression, the LASSO (least absolute shrinkage and selection operator) method, and Random Forests. This research concluded that a non-parametric Random Forests model classified fraudulent claims with the highest accuracy and best balance between sensitivity and specificity. Variable selection and importance are also implemented to improve the performance at which fraudulent claims are accurately classified.
|Commitee:||Kim, Sung, Kim-Park, Yong Hee|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 56/01M(E), Masters Abstracts International|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be