You are encouraged to attend the defence. The details of the defence and how to attend are included below: 

DATE: 18 July 2024

TIME: 2:00 PM (PT)

DEFENCE MODE: Remote

Virtual Attendance: Zoom

LINK TO JOIN: Please contact the Office of Graduate Administration for information regarding remote attendance for online defences.

To ensure the defence proceeds with no interruptions, please mute your audio and video on entry and do not inadvertently share your screen. The meeting will be locked to entry 5 minutes after it begins: ensure you are on time. 

THESIS ENTITLED: LEVERAGING MACHINE LEARNING TO DECODE INSURANCE PURCHASING DISPARITIES IN CANADIAN HOUSEHOLDS: A PCA APPROACH

ABSTRACT: This thesis investigates the factors influencing insurance spending among Canadian households, employing advanced machine learning techniques and Principal Component Analysis (PCA). This research develops an integrated predictive model to forecast household expenditures on life, health, and auto insurance, incorporating a comprehensive range of determinants such as household characteristics, economic conditions, and regional differences.

Utilizing a robust dataset from the Survey of Household Spending (SHS) for the years 2010 to 2017, with 2019 serving as the validation year, the study applies PCA to manage high-dimensional data effectively, thereby enhancing the predictive performance of the machine learning algorithms used. The results indicate that the model predicts insurance expenditures with notable accuracy; however, it slightly underestimates life insurance costs with an actual expenditure of $1,381 compared to the predicted $1,263, while providing highly accurate forecasts for health insurance. The predictions for car insurance expenditures exhibit larger variances.

The findings highlight the substantial benefits of integrating PCA and machine learning to advance predictive analytics in the insurance industry. The study offers critical insights for insurance providers, policymakers, and consumers, laying a data-driven groundwork for strategic decision-making and policy development. Recommendations for future research include refining the predictive models and investigating additional variables that may influence insurance spending. This thesis not only contributes to the academic discourse but also provides actionable strategies to enhance the accuracy and efficacy of forecasting models in the insurance sector.

COMMITTEE MEMBERSHIP: 

Chair: Dr. Wootae Chun, University of Northern British Columbia 

Examining Committee Members: 

Supervisor: Dr. Chenbo Fu, University of Northern British Columbia 

Committee Member: Dr. Kafui Monu, University of Northern British Columbia 

Committee Member: Dr. Fan Jiang, University of Northern British Columbia 

External Examiner: Dr. Nanying Lin, Arkansas State University