Faculty

Science

Supervisor Name

Matt Davison and Adam Metzler

Keywords

financial wellbeing, FWB, clustering, cluster migration, financial wellbeing of working canadians, financially stressed, financially coping, financially comfortable, cluster

Description

The National Payroll Institute (NPI) conducts an annual survey to measure the financial situation of their members. Using this data, the Financial Wellness Lab (FWL) then analyzes and applies advanced machine learning techniques to gain meaningful insights on the respondents. Of the many areas the FWL investigates regarding this data, the primary focus is identifying similar groups of individuals (clusters), and how these groups differ from one another. Aspects such as financial residency, savings, spending, and debt all play an important role in differentiating between these groups, and also play a role in financial wellbeing. Since this survey has been ongoing for upwards of 10 years, aspects of financial wellbeing and cluster migration can also be tracked and examined through time. Identifying correlated and predictive factors of cluster migration is another key emphasis of the labs.

My project specifically aims to follow the procedures mentioned above with the latest year’s data, while also investigating new and emerging aspects of financial wellbeing, such as inflation. Upon completion of this analysis, myself along with the FWL produced a presentation for NPI, which is identical to this publication.

Acknowledgements

Thank you to Matt Davison, Adam Metzler, the Financial Wellness Lab, and the Western USRI program for their support.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Document Type

Event

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Financial Wellbeing of Working Canadians

The National Payroll Institute (NPI) conducts an annual survey to measure the financial situation of their members. Using this data, the Financial Wellness Lab (FWL) then analyzes and applies advanced machine learning techniques to gain meaningful insights on the respondents. Of the many areas the FWL investigates regarding this data, the primary focus is identifying similar groups of individuals (clusters), and how these groups differ from one another. Aspects such as financial residency, savings, spending, and debt all play an important role in differentiating between these groups, and also play a role in financial wellbeing. Since this survey has been ongoing for upwards of 10 years, aspects of financial wellbeing and cluster migration can also be tracked and examined through time. Identifying correlated and predictive factors of cluster migration is another key emphasis of the labs.

My project specifically aims to follow the procedures mentioned above with the latest year’s data, while also investigating new and emerging aspects of financial wellbeing, such as inflation. Upon completion of this analysis, myself along with the FWL produced a presentation for NPI, which is identical to this publication.