BCG GenAI Financial Chatbot Internship

Overview

This repository documents the work done as part of a virtual internship with BCG GenAI Consulting. The primary goal of the internship is to develop an AI-powered financial chatbot that can analyze and provide insights on corporate financial performance, particularly using data from 10-K and 10-Q reports. The project aims to assist Global Finance Corp. (GFC) in enhancing its financial analysis capabilities by leveraging AI to transform raw financial data into actionable insights.

Task 1: Data Extraction and Initial Analysis

Steps:

1. Data Extraction:

   • Access the SEC's EDGAR database to retrieve the 10-K filings for Microsoft, Tesla, and Apple for the last three fiscal years.
   • Extract key financial figures, including Total Revenue, Net Income, Total Assets, Total Liabilities, and Cash Flow from Operating Activities.
   • Organize the extracted data into an Excel spreadsheet for easy reference during the analysis phase.

2. Preparing the Jupyter Notebook Environment:

   • Install and set up Jupyter Notebook for the analysis.
   • Create a new notebook to document the analysis process.

3. Python Analysis in Jupyter:

   • Import necessary libraries and load the financial data into a pandas DataFrame.
   • Perform trend analysis, focusing on year-over-year percentage changes for each financial metric.
   • Explore other aggregate functions and groupings to analyze the data across different dimensions, such as by company and over the years.

4. Documentation and Submission:

   • Thoroughly document the methodology, observations, and conclusions within the Jupyter Notebook using markdown cells.
   • Export the completed notebook as a PDF or HTML file for submission.

Task 2: Developing the AI-Powered Financial Chatbot

Steps:

1. Defining the Chatbot's Objective:

   • Develop a chatbot designed to respond to user queries about key financial metrics such as net income and revenue growth, using the data extracted in Task 1.

2. Implementing Rule-Based Logic:

   • Create a framework where the chatbot uses predefined rules to map user queries to appropriate financial insights. The chatbot's responses should be clear, concise, and informative.

3. Data Structuring and Retrieval:

   • Organize the financial data in a structured format to facilitate easy retrieval. The chatbot should be able to access this structured data to provide accurate responses based on user queries.

4. Communicating Financial Insights:

   • Focus on crafting responses that simplify complex financial data into understandable insights. Consider enhancing the chatbot's interactivity by suggesting related queries or offering additional information based on user interest.

Conclusion

This internship project has successfully laid the groundwork for developing a comprehensive AI-powered financial chatbot. The completed tasks include data extraction and analysis, as well as the implementation of basic rule-based logic to facilitate user interaction with financial data.

Future Work

• Enhancing NLP Capabilities: Future iterations could incorporate natural language processing (NLP) features to enable the chatbot to understand and respond to a broader range of user queries with greater accuracy.
• Dynamic Data Retrieval: Integrating real-time financial databases or APIs will allow the chatbot to provide up-to-date financial insights, enhancing its value to users.
• Machine Learning Integration: The addition of machine learning models could enable the chatbot to offer predictive insights and more sophisticated financial analysis.
• User Interface Development: A user-friendly interface would make the chatbot more accessible to a wider audience, regardless of their financial expertise, ensuring a seamless user experience.