Automated Financial Data Analysis Workflow

Introduction to Automated Account Analysis This presentation outlines a comprehensive workflow for analyzing financial accounts using artificial intelligence and Python. We'll start with uploading a balance sheet to an AI chatbot, then move through data extraction, formatting, and finally to in-depth analysis using custom Python code.

Uploading the Balance Sheet The first step in our process is uploading a balance sheet or equivalent financial document to an AI chatbot like ChatGPT. This can typically be done by either pasting the text directly into the chat interface or by describing the document's contents in detail to the AI.

Interacting with the AI Chatbot Once the balance sheet is uploaded, we need to instruct the AI to extract the relevant financial data. Here's an example prompt to use:

Please extract the following financial data from the balance sheet I've provided:
1. Total Assets
2. Total Liabilities
3. Total Equity
4. Current Assets
5. Current Liabilities
6. Long-term Debt
7. Cash and Cash Equivalents

For each item, provide the monetary value and the corresponding year. Format the data as a Python dictionary.

AI Data Extraction The AI will process the balance sheet and extract the requested information. It will then format the data as a Python dictionary, which can be easily used in subsequent analysis. Here's an example of what the output might look like:

financial_data = {
    "2023": {
        "Total Assets": 1000000,
        "Total Liabilities": 600000,
        "Total Equity": 400000,
        "Current Assets": 300000,
        "Current Liabilities": 200000,
        "Long-term Debt": 400000,
        "Cash and Cash Equivalents": 150000
    },
    "2022": {
        "Total Assets": 900000,
        "Total Liabilities": 550000,
        "Total Equity": 350000,
        "Current Assets": 250000,
        "Current Liabilities": 180000,
        "Long-term Debt": 370000,
        "Cash and Cash Equivalents": 120000
    }
}

Preparing for Python Analysis With the data extracted and formatted, we can now move on to analyzing it using Python. We'll use libraries such as pandas for data manipulation and matplotlib for visualization. First, let's import the necessary libraries and convert our data into a pandas DataFrame.

Creating a DataFrame Here's the Python code to create a DataFrame from our extracted data:

import pandas as pd
import matplotlib.pyplot as plt

# Convert the dictionary to a DataFrame
df = pd.DataFrame.from_dict(financial_data, orient='index')

# Display the DataFrame
print(df)

This code will create a structured DataFrame that we can use for further analysis.

Basic Financial Ratio Analysis Now that we have our data in a DataFrame, we can calculate some basic financial ratios. We'll focus on liquidity ratios, solvency ratios, and profitability ratios.

Calculating Liquidity Ratios Let's calculate the Current Ratio and Quick Ratio:

# Current Ratio
df['Current Ratio'] = df['Current Assets'] / df['Current Liabilities']

# Quick Ratio (assuming 50% of Current Assets are inventory)
df['Quick Ratio'] = (df['Current Assets'] * 0.5) / df['Current Liabilities']

print(df[['Current Ratio', 'Quick Ratio']])

Calculating Solvency Ratios Now we'll calculate the Debt-to-Equity Ratio and Debt-to-Assets Ratio:

# Debt-to-Equity Ratio
df['Debt-to-Equity Ratio'] = df['Total Liabilities'] / df['Total Equity']

# Debt-to-Assets Ratio
df['Debt-to-Assets Ratio'] = df['Total Liabilities'] / df['Total Assets']

print(df[['Debt-to-Equity Ratio', 'Debt-to-Assets Ratio']])

Visualizing Financial Trends To better understand the financial trends, we can create visualizations using matplotlib. Here's an example of how to create a bar chart comparing Total Assets, Total Liabilities, and Total Equity over the years:

# Create a bar chart
df[['Total Assets', 'Total Liabilities', 'Total Equity']].plot(kind='bar', figsize=(10, 6))
plt.title('Financial Overview')
plt.xlabel('Year')
plt.ylabel('Amount')
plt.legend(loc='upper left')
plt.show()

Advanced Analysis - DuPont Analysis For more advanced analysis, we can perform a DuPont analysis, which breaks down Return on Equity (ROE) into its component parts. The DuPont formula is:

ROE = (Net Income / Sales) * (Sales / Total Assets) * (Total Assets / Equity)

This formula requires additional data not present in our balance sheet, so we'll need to ask the AI for more information.

Requesting Additional Data To perform the DuPont analysis, we need to request additional information from the AI. Here's a prompt to use:

Based on the financial data you extracted earlier, please provide the following additional information for both 2022 and 2023:
1. Net Income
2. Sales

Format the data as a Python dictionary, similar to the previous output.

Performing DuPont Analysis Once we have the additional data, we can perform the DuPont analysis:

# Assuming we've received the additional data and added it to our DataFrame
df['Net Profit Margin'] = df['Net Income'] / df['Sales']
df['Asset Turnover'] = df['Sales'] / df['Total Assets']
df['Equity Multiplier'] = df['Total Assets'] / df['Total Equity']
df['ROE'] = df['Net Profit Margin'] * df['Asset Turnover'] * df['Equity Multiplier']

print(df[['Net Profit Margin', 'Asset Turnover', 'Equity Multiplier', 'ROE']])

Interpreting the Results The final step in our workflow is interpreting the results of our analysis. This involves examining the calculated ratios and trends to draw meaningful conclusions about the company's financial health, efficiency, and profitability.

Conclusion and Next Steps This workflow demonstrates how AI and Python can be combined to streamline and enhance financial analysis. Future improvements could include automating the data input process, incorporating more advanced financial models, and creating a user-friendly interface for non-technical users.

Additional References

1. "Financial Statement Analysis" by Martin Fridson and Fernando Alvarez
2. "Python for Finance" by Yves Hilpisch
3. OpenAI's ChatGPT documentation: https://openai.com/chatgpt
4. pandas documentation: https://pandas.pydata.org/docs/
5. matplotlib documentation: https://matplotlib.org/stable/contents.html