Publication Date

2021

Document Type

Dissertation/Thesis

First Advisor

Xia, Chaoxiong (Michelle)

Degree Name

Ph.D. (Doctor of Philosophy)

Legacy Department

Department of Mathematical Sciences

Abstract

A commonly encountered risk in insurance business is misrepresentation risk. Misrepresentation is a type of insurance fraud where a policyholder or a policy applicant falsifies his or her risk status in order to pay cheaper premiums for more expensive future risks. It is difficult and expensive for insurance companies to detect this kind of risk. With high cost of sophisticated underwriting, it becomes a norm for insurance companies to regularly rely on the policy applicant to self-report most of their risk statuses. We employ a frequentist approach by using expectation-maximization (EM) algorithm to carry out maximum likelihood estimation of the effects and prevalence of misrepresentation based on the misrepresentation model from Xia and Gustafson [2016]. We apply the EM algorithm approach to the misrepresentation model when there is a single risk factor. An extension to the single risk factor model is the multi-risk factor model (multiple risk factors).In the extension of the single risk factor to multi-risk factor, we look at some other distributions from the exponential family. We develop a use case of the methodology by determining if there is any evidence of misrepresentation in the Medical Expenditure Panel Survey (MEPS) data.

Extent

136 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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