Profiling Financial Health: Predictive Modeling of Personal Finance Behavior in the Context of Bangladesh

Personal finance management plays a pivotal role in individuals' financial well-being and stability. In the context of Bangladesh, where financial literacy and access to personalized financial services remain limited, there is a pressing need for predictive modeling techniques to aid citizens in making informed financial decisions. This study addresses this gap by proposing a comprehensive framework for predictive modeling of personal finance behavior. Drawing inspiration from previous research on personalized decision-making and robo-advisory services, our study utilizes advanced machine learning concepts to delve into the nuances of personal finance behavior. Through the implementation of predictive modeling techniques, including linear regression analysis and machine learning algorithms, we aim to predict and understand investment preferences, debt management tactics, and saving habits of Bangladeshi individuals. By leveraging data-driven methodologies, our framework empowers individuals to make more informed investment decisions, ultimately contributing to improved financial literacy and stability in Bangladesh. This study underscores the importance of predictive modeling in personal finance and provides valuable insights for policymakers, financial institutions, and individuals alike. By bridging the gap between data analytics and financial decision-making, our research paves the way for a more inclusive and resilient financial ecosystem in Bangladesh.