Publication Date

2023

Document Type

Dissertation/Thesis

First Advisor

Xia, Chaoxiong

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Statistics and Actuarial Science

Abstract

This thesis builds upon the foundations laid out in Xia et al. [2023], which explored the utilizationof Maximum Likelihood approach to model misrepresentation data in Generalized Linear Models (GLM) ratemaking models. We introduce the concept of “underreported variables”, a form of insurance misrepresentation where insured individuals provide inaccurate information about risk factors that influence insurance eligibility, premiums, and insured amounts. Unlike fraudulent misrepresentation, underreported variables arise from a lack of awareness regarding the insured’s mental and physical health conditions, rather than fraudulent intent. The study rigorously tests the proposed model using health insurance data and extends its applicability to other insurance domains such as auto and home insurance. This research enhances claim prediction models by incorporating the probability of underreported variables, improving the accuracy of predictions. The work builds on earlier research by employing the Maximum Likelihood method for modeling and estimation, specifically in scenarios where each policy may have multiple claims. It derives partial and complete log likelihood functions for ratemaking models and uses the Expectation Maximization (EM) algorithm for parameter estimation. Notably, this research aligns with broader efforts in the insurance industry to detect fraudulent claims. It also contributes to the understanding of underreported variables in insurance ratemaking models, offering insights into improving predictive models for insurance claims across various domains.

Extent

48 pages

Language

en

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

Download

Share

COinS