SMART FINANCIAL PROFILING SYSTEM USING DEMOGRAPHIC AND ECONOMIC ATTRIBUTES

Abstract

This project introduces an AI-based financial identification system designed to analyze and classify the financial status of individuals or communities using demographic and economic data. With increasing demand for data-driven solutions in finance and policymaking, the proposed system provides a reliable and scalable approach to assess financial conditions across various population segments.The system utilizes key demographic features such as age, gender, education level, occupation, marital status, and geographical location, along with economic indicators like income level, employment status, housing type, and access to banking services. By processing this data using Machine Learning algorithms such as Random Forest, Logistic Regression, and K-Means Clustering, the model can predict financial standing and group individuals into low, medium, or high financial categories.This classification helps governments, financial institutions, and NGOs to target specific groups for financial aid, credit services, or development programs. For example, banks can assess creditworthiness for micro-loans, while government bodies can identify underprivileged communities for subsidy distribution or social welfare schemes.The project also incorporates data visualization tools to present economic patterns and demographic influences in a clear, insightful manner. Regional heatmaps, statistical graphs, and predictive dashboards help stakeholders make informed decisions backed by real data.Overall, this project demonstrates how Artificial Intelligence can enhance financial inclusion by leveraging publicly available or survey-based data. It promotes transparent, ethical, and intelligent economic planning, ensuring that financial services and benefits reach those who need them most