LEGACY PLANNER: AN EXPLAINABLE HYBRID INTELLIGENCE FRAMEWORK FOR LONG-TERM FINANCIAL AND PROPERTY PLANNING

Abstract: Long-term financial planning and property acquisition decisions are often made using fragmented tools such as manual budgeting methods, basic online calculators, or informal financial advice. These approaches fail to provide integrated, personalized, and explainable insights that account for income patterns, expenses, liabilities, and future financial commitments. This paper presents Legacy Planner, a web-based decision-support framework that combines rule-based financial logic with machine learning–assisted analysis to evaluate property affordability and long-term financial feasibility. The proposed system enables users to construct a structured financial profile, analyze savings potential, estimate loan obligations, and assess affordability through transparent scoring mechanisms. Unlike black-box financial tools, the framework emphasizes explainability by enforcing interpretable financial constraints alongside data-driven predictions. The system is implemented using a modular web architecture and demonstrates how hybrid intelligence can improve clarity and reliability in personal financial decision-making. Experimental evaluation through simulated user scenarios indicates that the proposed approach reduces calculation errors, improves financial awareness, and supports realistic goal planning. The framework highlights the role of explainable AI in consumer-centric financial applications and provides a scalable foundation for intelligent financial planning systems.

Keywords: Financial Planning, Property Affordability, Hybrid Intelligence, Explainable AI, Decision Support Systems