Document AI - Financial Report Extraction

Introduction

In this article, we will explore how Artificial Intelligence (AI) can be employed to analyze documents and extract pertinent information, with a specific focus on financial reporting. Following our favorite companies can sometimes be overwhelming, especially when trying to keep up with their performance metrics, growth prospects, and user acquisition trends. One reliable source of information is the legally mandated financial results that publicly traded companies are required to release quarterly.

These are typically presented as PDFs, which can be somewhat monotonous to sift through. Nevertheless, essential insights can be gleaned from these documents. The challenge lies in efficiently extracting the relevant data from the myriad of text and tables that financial reports contain. In tackling this issue, I created a small-scale solution leveraging AI to autonomously extract key data points we care about.

Architecture Overview

My solution comprises a basic web application featuring an upload component. Users can upload PDFs to an AWS S3 bucket, which is advantageous for scalability and cloud-based execution. This architecture prevents dependency on the local machine’s performance. The S3 bucket is tied to an AWS EC2 instance and uses Amazon's Simple Notification Service (SNS) for notifications upon file uploads.

Once a new file is uploaded, a Python script hosted on the EC2 instance listens for these notifications. Upon receiving one, it initiates the data extraction process. For text extraction, the Python library PyMuPDF (formerly known as fitz) is employed. Following text extraction, the data is sent to OpenAI for further analysis and condensing of the critical information we need.

Data Extraction Process

Here’s how the data extraction script works:

1. Monitoring File Uploads: The script awaits notifications of new uploads via SNS, capturing the object key and bucket name.
2. Downloading the PDF: After receiving the notification, it downloads the new PDF.
3. Extracting Tables and Text: The script retrieves all tables from the newly downloaded PDF and collects text from each cell. This is a critical step, as tables often contain relevant data that can be tricky to extract.
4. Consolidating Data: To avoid duplicates in extracted data, the script consolidates the text, formatting it in Markdown—a structure that aids AI in comprehending the information.
5. Sending Data to AI: Using the LangChain library, the script constructs a specific prompt that instructs OpenAI to extract the information we need in a pre-defined format.
6. Uploading Results: Finally, the extracted data is saved into a CSV file and uploaded back to the S3 bucket for easy access.

Data Points of Interest

For my implementation, I focused on extracting five key data points from the financial reports:

• Total stockholder equity
• Revenue year-over-year
• Daily active users
• Biggest risks for the company
• Biggest opportunities outlined in the report

These metrics are essential for investors as they provide a clear snapshot of a company's health and potential future performance.

Conclusion

The final step involves verifying whether the extracted information is accurate by cross-referencing it with the original PDF. For instance, confirming the stockholder equity values and revenue figures further validates the efficacy of the AI solution.

This exploration demonstrates how AI can effectively sift through dense documents, saving time and enhancing our ability to make informed investment decisions.

Thank you for your attention. I hope you found this article helpful, and I welcome your feedback!

Keywords

• Artificial Intelligence
• Document Analysis
• Financial Reports
• Data Extraction
• AWS
• EC2 Instance
• S3 Bucket
• OpenAI
• LangChain
• Markdown
• Stockholder Equity
• Revenue
• Active Users
• Company Risks
• Opportunities

FAQ

What is the purpose of using AI for financial report extraction?
AI streamlines the data extraction process, allowing for quick and efficient analysis of financial documents, enabling investors to access key metrics without sifting through extensive reports.

How does the system determine which data points to extract?
In this implementation, a set of relevant data points including total stockholder equity, revenue, and user metrics are predefined for extraction. This method could be expanded with more dynamic options for user-defined metrics.

What role does the S3 bucket play in the architecture?
The S3 bucket serves as a cloud storage solution for uploaded PDF files, facilitating scalable and continuous operations without performance constraints imposed by local machines.

What libraries are used for text extraction and processing?
The primary library for text extraction is PyMuPDF for handling PDFs, while LangChain is utilized to format prompts and communicate with the OpenAI API.

How is the accuracy of the extracted data verified?
After extraction, the results are compared against the original PDF document to ensure they accurately reflect the data presented in the financial report.