AI-Driven Goal Based Financial Planning System: A Framework for Contextual Feasibility Validation

Abstract

In the wake of growing complexity in financial decision-making and the ever-changing economic environment, conventional financial planning strategies have proven inadequate. This paper presents an artificial intelligence (AI) powered goal-based financial planning system with contextual feasibility analysis to improve the precision and responsiveness of financial planning. The system combines several elements, such as personal financial data analysis, machine learning for financial predictions, reinforcement learning for financial strategies, and probabilistic modeling for feasibility validation. A holistic framework was proposed to model financial factors, including income, expenditure, savings, risk, and market dynamics, into a single framework to reflect both human and environmental factors. Time-series forecasting models are used for financial predictions, and reinforcement learning for investment strategy optimization. Monte Carlo simulation was used to assess various financial scenarios and assess the feasibility of achieving specific financial objectives. The tool offers tailored financial plans, feasibility measures, and recommendations to help make more realistic and informed decisions. Empirical experiments show that the proposed system increases the accuracy of forecasts, adaptability, and more accurate goal feasibility evaluations than traditional rule-based approaches. The results indicate that embedding contextual awareness and adaptive learning capabilities in financial planning systems greatly enhances their performance. The study presents a scalable and smart approach that integrates predictive analytics with goal-based financial planning, which can be applied in fintech applications and personal financial advisors.