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AI-powered financial chatbot

AIGen

AI-powered financial chatbot

Financial Chatbot Documentation

Overview: The Financial Chatbot is a simple web-based application developed using

Flask, a lightweight Python web framework. The chatbot leverages a predefined set of queries to provide users with financial insights based on a dataset of company financial data.

How It Works:

1. User Input: Users interact with the chatbot by entering company names, years, and predefined queries into the input fields provided by the web interface.
2. Query Processing: The Flask backend receives the user input and processes it using a Python function called financial_chatbot.
3. Data Retrieval: The financial_chatbot function accesses a dataset containing financial data for various companies and years.
4. Response Generation: Based on the user's input query, the financial_chatbot function retrieves the relevant financial data from the dataset and generates a response.
5. Output: The response is then returned to the frontend and displayed to the user in the web interface.

Predefined Queries: The Financial Chatbot can respond to the following predefined queries:

1. Total Revenue: “What is the total revenue?”
2. Net Income Change: "How has the net income changed?"
3. Total Assets and Liabilities: "What are the total assets and liabilities?"

Limitations:

1. Predefined Queries: The chatbot can only respond to queries that match the predefined formats. Queries outside these formats may not be recognized or processed correctly.
2. Dataset Coverage: The chatbot's responses are limited to the data available in the dataset. If the requested company or year is not present in the dataset, the chatbot will return a "Data not available" response.
3. Natural Language Understanding: The chatbot does not perform advanced natural language processing (NLP) to understand variations in user input. It relies on exact matches to predefined query formats.
4. Error Handling: Limited error handling is implemented in the current version of the chatbot. Invalid inputs or unexpected errors may result in generic error messages.

Future Enhancements:

1. NLP Integration: Improve the chatbot's ability to understand variations in user input through NLP techniques.
2. Expanded Query Support: Add support for additional financial queries and more flexible query formats.
3. Data Visualization: Integrate data visualization tools to provide graphical representations of financial data.
4. User Authentication: Implement user authentication to personalize responses or access restricted data.
5. Error Handling: Enhance error handling to provide more informative and context-specific error messages.