This paper explores various socioeconomic factors that contribute to individual financial success using machine learning algorithms and approaches. Financial success, a critical aspect of all individual's well-being, is a complex concept influenced by various factors. This study aims to understand the determinants of financial success. It examines the survey data from the National Longitudinal Survey of Youth 1997 by the Bureau of Labor Statistics (1), consisting of a sample of 8,984 individuals's longitudinal data over years. The dataset comprises income variables and a large set of socioeconomic variables of individuals. An in-depth analysis shows the effectiveness of machine learning algorithms in financial success research, highlights the potential of leveraging longitudinal data to enhance prediction accuracy, and provides valuable insights into how various socioeconomic factors influence financial success. The findings highlight the significant influence of highest education degree, occupation and gender as the top three determinants of individual income among socioeconomic factors examined. Yearly working hours, age and work tenure follow as three secondary influencing factors, and all other factors including parental household income, industry, parents' highest grade and others are identified as tertiary factors. These insights allow researchers to better understand the complex nature of financial success, and are also crucial for fostering financial success among individuals and advancing broader societal well-being by providing insights for policymakers during decision-making process.

A new study published in the journal PLOS One revealed a link between a person's body type and their family's earnings. According to the study's findings, physically appealing people are likely to earn more than those who aren't. According to researchers, the beauty premium is a reality. However, a University of Iowa associate professor and his colleagues found that the metrics employed to assess physical attractiveness had some severe shortcomings. Most earlier studies frequently defined physical appearance from subjective evaluations based on surveys. In addition, these metrics are too simplistic to provide a thorough description of body forms.

Throughout the last months, I had the chance to enable various organizations and leaders leveraging their large databases with machine learning. I was particularly engaging with member organisations which struggle with rising dropout rates (churns) -- an issue that became even more serious throughout the pandemic when individual income has been on a declining and the fear of job loss on a rising path. With machine learning, we used very large membership databases with individual-level information (e.g. Machine Learning tells us the "What", Causal Inference the "Why" Despite the overall good performance of the machine learning models, our clients were always interested in one obvious question: Why does an individual member leave? Unfortunately, machine learning models are not suited to identify the causes of things but rather they are built to predict things.