Capit-AI: An AI driven Financial Advisory System
AI-driven financial advisory systems are transforming investment planning, tax optimization, and financial literacy by automating key decision-making processes. Capit-AI is an intelligent AI-powered financial advisor that integrates machine learning (ML), Natural Language Processing (NLP), and automation to assist users in managing mutual funds, stocks, tax saving strategies, and real-time expense tracking. The system consists of multiple AI agents, including a Web Agent, Finance Agent, Mutual Fund Agent, and Tax Agent, to provide personalized financial insights. Capit-AI overcomes traditional challenges such as high financial advisory costs, static investment models, and lack of real-time data integration. This paper explores CapitAI’s architecture, implementation strategies, and system performance, highlighting its potential to revolutionize automated financial management. The results suggest that Capit-AI enhances financial decision-making by dynamically adjusting portfolios, optimizing taxes, and providing real-time financial alerts, thereby empowering users with data-driven financial strategies. Additionally, Capit-AI employs predictive analytics to forecast market trends and assess financial risks, ensuring proactive investment planning. Its intuitive dashboard provides users with an interactive interface to track financial health, review investment performance, and receive personalized recommendations, making financial management more accessible and efficient. Team’s capabilities are accessible via a user-friendly interface created using react.js and strong python backend Keywords: Tax Optimization, Smart Invest, AI agents, Machine Learning, Natural language Processing, Financial news
Authors
- Hruturaj Atole
Publication Details
- Journal
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2025-04-26
- DOI
- https://doi.org/10.22214/ijraset.2025.69398
- Primary Topic
- FinTech, Crowdfunding, Digital Finance
- Type
- article
- Field-Weighted Citation Impact
- 0.00