The complexity of financial wellness: examining survey patterns via kernel metric learning and clustering of mixed-type data

Recent market events and inflation have significantly affected the financial stress facing many individuals, but understanding the main stressors is paramount to supporting them in making better long-term financial decisions. Financial advisors must understand the types of stress their clients face to provide tailored advice. While recent high inflation rates may underpin the cause of their clients' stress, we ask: what are the major sources of stress that affect an individual's financial wellness? In this study, we analyze the responses of 1874 individuals to 68 mixed-type questions from 2022 using distance-based clustering that is widely used in finance to group data into similar groups. Distance-based clustering is widely used in finance to group data into similar groups, which requires a predefined distance measurement between data points based on their (dis)similarity. We use a mixed-type metric that utilizes a variable-specific kernel functions with cross-validated bandwidths to optimally balance variables important for similarity, and smooth out variables irrelevant to the difference between data points. Applying the metric to the high-dimensional survey, we found two clusters of respondents: (1) the 'steady savers', who represent approximately one third of survey respondents and expressed stronger financial well-being with respect to day-to-day financial obligations and future outlooks, and (2) the 'financial strivers' who currently find themselves in more financially stressful situations. This segmentation provides financial advisors with useful results to allocate products, services, or advice tailored to support each group's unique financial wellness needs. By leveraging this methodology, we strive to advance the realm of personalized financial advising and the landscape of robo-advising. Enhanced precision and tailored strategies allow this work to elevate the quality of investment recommendations, contributing to the future of automated financial guidance.