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

2025-12-24

AI Summary

Addressing the practical challenge faced by middle-income households in balancing budgetary constraints and nutritional requirements amid volatile food prices, this paper proposes a price-aware multi-agent AI system. The system integrates personal financial data, health profiles, and real-time food pricing information within a modular multi-agent architecture—comprising dedicated agents for budgeting, nutrition planning, price monitoring, and health personalization—and introduces a novel financial–nutritional co-optimization mechanism. To enable adaptive cost-efficient substitutions, we propose a knowledge-sharing strategy based on a substitution graph, ensuring ≥95% nutritional adequacy under dynamic cost constraints. Evaluated on simulated Saudi households, the system reduces dietary expenditure by 12–18% and maintains high robustness against 20–30% food price fluctuations. This work establishes a scalable, interpretable paradigm for intelligent, resource-constrained dietary decision-making.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Optimizes meal plans balancing nutrition and budget constraints
• Integrates real-time food prices for cost-effective diet adjustments
• Addresses nutritional adequacy in fluctuating market conditions affordably

Innovation

• Agentic AI integrates finance and nutrition planning
• Modular multi-agent architecture with specialized agents
• Substitution graph ensures nutrition at minimal cost