Machine learning analysis of financial behavior: A study of Gen Y and Gen Z preferences

To be financially sound and successful, it is necessary for an individual to have fundamental financial insight. Globalization is leading to several transformations across the globe. This leads to both good and bad impact on the financial behavior of the individuals. Even though globalization has created multiple growth avenues, it has also led to creation of a consumptive lifestyle among people. This consumptive lifestyle leads to several unhealthy financial behaviors like, overspending, lack of budget and lack of saving. Current literature suggests that Generation Y and Z have more disposable income than any other previous generation. Thus, these two generation’s consumption and investment choices certainly shape the economy. Many studies in the past have been conducted to study the unique characteristics of Generation Y and Z, the current study emphasizes on studying the financial behavior of Generation Y and Z. The study also aims at comparing the financial preferences of these two generations using machine learning algorithms. The major findings reveal reveals distinctive preferences in predictive algorithms for different financial behavior categories among Gen Y and Gen Z for spending behavior prediction, Random Forest emerges as the algorithm for choice for Gen Y while Support Vector Machine stands out for Gen Z with Random Forest prevailing overall. These findings are instrumental in understanding the distinct financial behavior patterns exhibited by Gen Y and Gen Z providing valuable guidance for financial institutions, planners and Advisors in tailoring strategies and services.