FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910058145644544 |
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| author | Syed, Toqeer Ali Alshahrani, Abdulaziz Ullah, Ali Akarma, Ali Khan, Sohail Nauman, Muhammad Jan, Salman |
| author_facet | Syed, Toqeer Ali Alshahrani, Abdulaziz Ullah, Ali Akarma, Ali Khan, Sohail Nauman, Muhammad Jan, Salman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20991 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning Syed, Toqeer Ali Alshahrani, Abdulaziz Ullah, Ali Akarma, Ali Khan, Sohail Nauman, Muhammad Jan, Salman Artificial Intelligence Multiagent Systems 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. |
| title | FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2512.20991 |