<p> 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.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10147317 |
| Date | 20 October 2016 |
| Creators | Ceglia, Cesarina |
| Publisher | California State University, Long Beach |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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