The Future of Financial Analytics

If you’re a banker, investor or a financial analyst, you often need financial statements of multiple companies to decide on loans or investments. The problem? These statements are usually scanned PDFs uploaded on regulatory websites like the Ministry of Corporate Affairs (MCA) or company filings.

Extract of Scanned PDF Financial Statements:

Traditionally, extracting meaningful insights from such scanned documents requires manual data entry, which is both time-consuming and prone to errors.

What if we could automate this? This case study walks you through how we can use Python’s, OCR, API’s, and Power BI’s Power Query to make financial analysis seamless.

Step 1: Extracting Financial Data from Scanned PDFs

The first step is to extract text from scanned financial statements. Since these PDFs are just images, we need Optical Character Recognition (OCR) to convert them into readable text.

Using Python for OCR

We use Tesseract OCR, an open-source tool, to extract text from these scanned statements.

While OCR does the job, the extracted data is still unstructured and messy. This is where AI steps in.

Step 2: Organizing Financial Data Using AI

The raw text needs to be converted into a structured financial statement. We use OpenAI’s API to:

1. Identify financial terms like Revenue, Expenses, Liabilities, and Equity.
2. Standardize variations (e.g., “Total Sales” → “Revenue”, “Net Profit” → “Net Income”).
3. Format the extracted data into structured financial statements (Balance Sheet, Profit & Loss Statement, and Cash Flow Statement).

Now, we have financial data in a structured format, ready for analysis.

Step 3: Automating Financial Data in Power BI

Once the data is structured, we import it into Power BI using Power Query. Here’s what happens next:

1. Load AI-processed financial statements into Power Query.
2. Use AI to map financial terms into a standardized format.
3. Example: If a statement has “Net Sales” or “Total Sale,” AI converts it into “Revenue.”

c.Ensure all new financial statements follow the same structure, making updates effortless.

Step 4: Automating Dashboards and Financial Analysis

With a standardized format in Power BI, we can:

1. Create real-time dashboards that update automatically as new financial statements are added.
2. Perform trend analysis, ratio analysis, and KPI tracking.
3. Compare financial performance across companies, sectors, or time periods.
4. Eliminate manual errors and improve efficiency in financial reporting.

Conclusion: The Future of Financial Analysis

By integrating Python’s OCR, API, and Power BI, we have automated financial statement analysis. What once took hours or days can now be done in minutes—without human intervention.

This is financial analysis on autopilot, making life easier for bankers, investors, and financial professionals.