FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning

TL;DR

FinAgent is an agentic AI framework that dynamically integrates personal finance and nutrition planning to optimize meal costs while maintaining nutritional quality, adapting to market fluctuations for sustainable household diet management.

Contribution

The paper introduces a modular multi-agent AI system that combines financial and nutritional planning, adapting in real-time to market changes for cost-effective, nutritious meal planning.

Findings

Achieved 12-18% cost reduction compared to static menus.

Maintained over 95% nutrient adequacy.

Demonstrated robustness to 20-30% price fluctuations.

Abstract

The issue of limited household budgets and nutritional demands continues to be a challenge especially in the middle-income environment where food prices fluctuate. This paper introduces a price aware agentic AI system, which combines personal finance management with diet optimization. With household income and fixed expenditures, medical and well-being status, as well as real-time food costs, the system creates nutritionally sufficient meals plans at comparatively reasonable prices that automatically adjust to market changes. The framework is implemented in a modular multi-agent architecture, which has specific agents (budgeting, nutrition, price monitoring, and health personalization). These agents share the knowledge base and use the substitution graph to ensure that the nutritional quality is maintained at a minimum cost. Simulations with a representative Saudi household case study show a steady 12-18\% reduction in costs relative to a static weekly menu, nutrient adequacy of over 95\% and high performance with price changes of 20-30%. The findings indicate that the framework can locally combine affordability with nutritional adequacy and provide a viable avenue of capacity-building towards sustainable and fair diet planning in line with Sustainable Development Goals on Zero Hunger and Good Health.