AI-Powered Expense Splitter and Trip Management System for Intelligent Group Financial Coordination

Group travel and shared activities often involve multiple participants contributing to common expenses, which can lead to calculation errors, inefficient settlements, and financial misunderstandings. Traditional bill-splitting applications mainly focus on recording transactions and still require manual data entry and reconciliation. This paper presents an AI-Powered Expense Splitter and Trip Management System designed to automate shared expense tracking and assist users in trip budget planning. The proposed platform integrates a graph-based settlement optimization algorithm, an Optical Character Recognition (OCR) receipt scanner for automatic expense entry, and a machine-learning-based trip cost prediction model that estimates total and per-person expenses using parameters such as destination, group size, duration, and transport mode. Additional features include multi-currency support, budget alerts, payment reminders, and visual analytics dashboards. The system architecture focuses on reducing manual effort while improving financial transparency among group members. Experimental evaluation demonstrates that the proposed system minimizes redundant transactions, improves expense tracking accuracy, and supports proactive financial planning for collaborative travel environments.