M.RATNA KUMARI 1 , M.NAGA SUNANDA 2 03 07 This project introduces an AIbased financial identification system designed to analyze and classify the financial status of individuals or communities using demographic and economic data With increasing demand for datadriven solutions in finance and policymaking the proposed system provides a reliable and scalable approach to assess financial conditions across various population segmentsThe 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 KMeans Clustering the model can predict financial standing and group individuals into low medium or high financial categoriesThis 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 microloans 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 dataOverall this project demonstrates how Artificial Intelligence can enhance financial inclusion by leveraging publicly available or surveybased data It promotes transparent ethical and intelligent economic planning ensuring that financial services and benefits reach those who need them most Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026