Fingerprints, Iris Scans, and the Law: Regulating Biometric Human-Computer Interfaces

Household budgeting is crucial for financial stability, yet many individuals find it challenging due to the lack of structured financial planning tools. This paper introduces a rule-based system that optimizes expenses by considering family size, age distribution, income, and overall budget. Unlike traditional budgeting tools, our system dynamically distributes income across essential categories—such as housing, food, medical care, education, and savings—using predefined rules. The system leverages dynamic input processing and rule-based allocation to provide real-time insights into budgeting constraints. Upon completing the expense distribution, the system evaluates whether the household maintains a cash in hand or requires debt payment, offering actionable financial insights. Experimental results show that the proposed model achieves 90% accuracy in budget allocation, ensuring financial sustainability and preventing overspending. The system offers a transparent, flexible, and user-friendly alternative to machine learning-based budget models, making it accessible to households of all financial backgrounds