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Project: Financial Advisory Bot using RAG with GenAI #133

Open ShwetaNagapure opened 2 months ago

ShwetaNagapure commented 2 months ago

Project Name

Financial Advisory Bot using RAG with GenAI

Description

RagHack

Financial Advisory Bot using RAG with GenAI

Team Name: TechX

Financial Advisory Bot using RAG with GenAI

1. Objective

Transform financial advisory services through generative AI, delivering customized financial advice based on individualized data analysis. This approach utilizes advanced algorithms to interpret customer data and market trends, ensuring tailored recommendations that adapt to changing financial landscapes, ultimately enhancing client outcomes and satisfaction.

2. Challenges

  1. Analyze customer financial data and market trends to generate tailored investment strategies.
  2. Offer real-time advisory services that adapt to changing financial conditions and customer goals.

  3. Ensure transparency and explainability in the AI-driven advisory process to build customer trust and confidence.

3. Problem Statement

In today's complex financial landscape, individuals struggle to access personalized, timely, and data-driven financial advice. Traditional advisory services are often expensive, not readily available 24/7, and may not always incorporate real-time market trends. There's a pressing need for an innovative solution that democratizes access to high-quality financial advice, adapts to changing market conditions, and aligns with individual financial goals.

4. Solution Overview

The Financial Advisory Assistant Bot aims to address this gap by delivering swift, precise, and context-aware financial advice using advanced AI technologies. Leveraging Retrieval-Augmented Generation (RAG) and Generative AI, this bot integrates real-time data from financial news and banking websites, stored in a MySQL relational database, to ensure up-to-date and accurate responses.

5. Target Beneficiaries

5.1 Target Audiences

  1. Individual Investors
  2. Financial Advisors and Planners
  3. Small Business Owners
  4. Retirees and Pre-Retirees
  5. Young Professionals

5.2 Targeted Benefits

Individual Investors

Small Business Owners

Retirees and Pre-Retirees

5.3 Generalized Benefit for all Audiences

Financial Advisory Bot is an AI-powered tool designed to assist users in making informed financial decisions. It leverages LangChain for natural language processing and document retrieval, helping users access personalized financial advice based on their specific needs, such as investment planning, loan management, and retirement savings. The bot dynamically adapts its responses according to user inputs, including income, expenses, risk tolerance, and financial goals.

6. Key Features:

Personalized Financial Planning: Provides tailored financial advice by analyzing user data such as income, expenses, savings, and investment goals. Loan Management: Offers insights on home loans, personal loans, and alternatives, taking into account factors like loan terms, interest rates, and monthly installments. Risk Tolerance Evaluation: Suggests investment strategies based on the user’s risk appetite. Easy Maintenance and Deployment: The bot is designed to be easily integrated into systems, ensuring smooth deployment and minimal maintenance. Real-Time Document Processing: The bot uses real-time document retrieval and AI-driven recommendations for financial products, ensuring up-to-date advice.

7. Accessibility

8. Technology & Languages

https://github.com/ShwetaNagapure/Finacial-Advisory-using-RAG-with-GenAI

10. Deployed Endpoint URL

N/A

11. Project Video

Web-App Video Link: Click Here

12. Our repository is organized as follows:

project-root/
├── source/
│   └── (codebase files)
│   └── (README.md)

Navigating the Repository

Source Folder

The source/ folder contains our project's codebase. Here you'll find:

To access the codebase:

  1. Navigate to the source/ folder in the GitHub repository.

  2. Browse through the files and folders to find the specific code you're looking for.

Getting Started

  1. Explore the source/ folder to understand the codebase structure.

Team Members: Shweta Nagapure, Atharva Mundke, Prasad Kumbhar and Virendra Bagul

Technology & Languages

Project Repository URL

https://github.com/ShwetaNagapure/Finacial-Advisory-using-RAG-with-GenAI/blob/main

Deployed Endpoint URL

N/A

Project Video

https://drive.google.com/file/d/1W-Naj5z4EGK2QzWuOwBjRH879T2go4L8/view?usp=sharing

Team Members

Shweta Nagapure, Atharva Mundke, Prasad Kumbar, Virendra Bagul

multispark commented 1 month ago

Hello @ShwetaNagapure, thank you for participating in RAG Hack!

The team is working hard to distribute badges. Please have each team member fill out this form: aka.ms/raghack/badge-dist

Thank you!