Honors Program
Date of this Version
Spring 5-13-2024
Document Type
Thesis
Citation
Weyers, C. Building a Data Pipeline and Machine Learning Model for Insurance Data. Undergraduate Honors Thesis, University of Nebraska-Lincoln, May 2024.
Abstract
Insurance telematics is an emerging and exciting field. It combines the advancements in GPS tracking, computational analytics, data processing, and machine learning into a useful tool to help insurance companies make the best product for their consumers. This is why National Indemnity looked to implement a telematics portion to their business processes of underwriting insurance policies and sponsored a School of Computing Senior Design project. In this report, we will first review existing solutions that been used to solve problems and subproblems similar to that we are given in this project. We then propose designs for the data pipeline and machine learning model that will optimal in providing predictions on the risk level of drivers. National Indemnity will be able to use this project to leverage predictions in order to optimize insurance rates to more accurately account for risk among the insured.
Included in
Databases and Information Systems Commons, Gifted Education Commons, Higher Education Commons, Other Computer Sciences Commons, Other Education Commons
Comments
Copyright Connor Weyers 2024