AI-Powered Personal Financial Twin: An Intelligent System for Financial Health Analysis and Personalized Recommendations

Personal financial management requires individuals to understand the relationship between income, expenditure, savings, and financial goals. Manually reviewing these values can make it difficult to identify spending patterns and assess whether current savings behaviour supports personal goals. This paper presents AI Financial Twin, a web-based prototype for personal finance analysis and recommendation. The system accepts user-provided financial inputs and calculates monthly savings, savings rate, a rule-based financial health score, an illustrative spending-category breakdown, and goal-related recommendations. A Next.js and React interface communicates with a Python FastAPI backend. Google Gemini 2.5 Flash is selected as the generative-AI service for AI-assisted financial insights. The prototype aims to make basic financial information easier to interpret through an interactive dashboard. The current implementation focuses on user-entered values and deterministic calculations; it does not connect directly to banks or independently verify transactions. Document extraction, retrieval-augmented generation, predictive machine learning, and production MLOps are identified as future extensions. This paper describes the system design, calculation workflow, implementation approach, limitations, and evaluation considerations without claiming unperformed experiments or measured improvements.