Why Python is valuable for auditors

You know the feeling: an Excel file full of tabs, a few thousand transactions, and the sense that one filter too many has just ruined your view. The days of auditors manually combing through data are over. More and more professionals are switching to scripting; documenting analyses in code instead of formulas. And that’s where Python shines. It’s simple, transparent, and easy to follow. In this article, you’ll learn why Python fits audit work so well and how it can make your audits smarter.

What makes Python stand out in audit work

Python was developed as an accessible programming language with a strong focus on readability and simplicity. For auditors, that means less technical hassle and more focus on what the analysis actually shows. Four features stand out:

1. Transparency and reproducibility
   In Python, every step of your analysis is captured in text. This makes it easy to verify what was done and to repeat the same procedure later. When your script is logically structured, even someone without a programming background can follow the reasoning. A colleague not involved in the audit can see exactly which filters were applied and in what order.

2. Combining data sources
   Python effortlessly integrates data from various formats and systems; from Excel files to CAMT bank data or ERP exports. That’s particularly useful in audits that require cross-checking financial data from multiple systems.

3. Detecting patterns and anomalies
   With libraries such as pandas, you can quickly filter and calculate results, for instance, to flag unusual transactions, find duplicate vendors, or compare price developments.

   The code remains compact and understandable, especially compared to SQL queries or Java scripts that often become bulky and less readable.

4. Independence and collaboration
   Python is open source and works with standard data formats. This means you can easily share scripts with colleagues or reviewers without needing identical software licenses. A well-structured script reads almost like pseudocode: clear, traceable, and ideal for collaboration within an audit team.

With Python you can quickly set up simple analyses.

Things to keep in mind

Although Python is accessible, there are a few things to consider to use it effectively in an audit context:

• Learning curve
  The basics are quick to learn, but writing clean and reusable code takes practice.

• Structure
  Especially in larger audits, it pays off to use a simple folder structure and consistent naming conventions. Also make sure to use descriptive file names like reconciliation_invoices_ledger.py.

• Understanding the data remains essential
  A good script is meaningless without proper input. You need to understand what each field represents, how transaction data is structured, and which filters are audit-relevant.

• Management and version control
  Once multiple people work on the same scripts, version control (for example via Git) becomes indispensable. It prevents analyses from diverging or being accidentally overwritten. Read more in the article on version control in auditing.

These are manageable aspects to organize properly. Anyone willing to invest a little time will find that Python becomes a powerful tool for executing structured and transparent audit tests; without the complexity of heavier programming languages or closed tools.

Try it yourself

Most analyses on The Audit Analytics website are executed in Python. See if you can follow them! Install Python and run a simple test yourself, such as an analysis on purchase prices.