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A significantly important aspect in the life of an individual or a society in its development is financial well-being. This is especially true in an emerging country like Vietnam. The aim of this study is to use machine learning methods to make predictions. The focus is specifically on the level of financial well-being. To this end, surveys were conducted to form the basis of the data. Financial literacy and socio-economic aspects were investigated. In this context, the predictive power of demographic, behavioral and financial variables, including gender, income, education and financial capability, was examined. Several algorithms (random forests, decision trees, multiple linear regression) are applied and compared to determine the most accurate model. A secure financial status is an important factor in a person’s life. Likewise in the development of society. This is especially true in an emerging country like Vietnam. The aim of this study is to use machine learning methods to make predictions. The focus is specifically on the level of financial well-being. To this end, surveys were conducted to form the basis of the data. Financial literacy and socio-economic aspects were investigated. In this context, the predictive power of demographic, behavioral and financial variables, including gender, income, education and financial capability, was examined. Various algorithms (decision trees, random forests and multiple linear regression) were used for the model and compared with each other. This allowed the most accurate model to be determined. The result shows key factors that influence financial well-being. It also demonstrates the effectiveness of machine learning in modeling complex socio-economic patterns. The results are suitable for targeted financial programs and political measures. The Vietnamese context is considered. On the one hand, this work enriches the pool of positive research in the field of financial well-being prediction, on the other hand, it creates a meaningful basis for the use of machine learning in the field of financial planning.

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Predictive Models for Understanding Financial Well-Being of Vietnamese People Using Machine Learning

  • 章节

  • 发表日期：

    2026年

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  • Dung Hai Dinh,
  • Alexander Styazhkov,
  • Tobias Ametsbichler

摘要

A significantly important aspect in the life of an individual or a society in its development is financial well-being. This is especially true in an emerging country like Vietnam. The aim of this study is to use machine learning methods to make predictions. The focus is specifically on the level of financial well-being. To this end, surveys were conducted to form the basis of the data. Financial literacy and socio-economic aspects were investigated. In this context, the predictive power of demographic, behavioral and financial variables, including gender, income, education and financial capability, was examined. Several algorithms (random forests, decision trees, multiple linear regression) are applied and compared to determine the most accurate model. A secure financial status is an important factor in a person’s life. Likewise in the development of society. This is especially true in an emerging country like Vietnam. The aim of this study is to use machine learning methods to make predictions. The focus is specifically on the level of financial well-being. To this end, surveys were conducted to form the basis of the data. Financial literacy and socio-economic aspects were investigated. In this context, the predictive power of demographic, behavioral and financial variables, including gender, income, education and financial capability, was examined. Various algorithms (decision trees, random forests and multiple linear regression) were used for the model and compared with each other. This allowed the most accurate model to be determined. The result shows key factors that influence financial well-being. It also demonstrates the effectiveness of machine learning in modeling complex socio-economic patterns. The results are suitable for targeted financial programs and political measures. The Vietnamese context is considered. On the one hand, this work enriches the pool of positive research in the field of financial well-being prediction, on the other hand, it creates a meaningful basis for the use of machine learning in the field of financial planning.