Automating Routine Accounting Tasks with AI (10 CPD units)
A practical course for accountants on using AI to automate data entry, reconciliation, and invoice processing, with hands-on Python exercises and the ethics of responsible automation.
About This Course
This course is designed to help professional accountants understand and leverage AI technologies to automate time-consuming and repetitive accounting tasks. It covers practical applications of AI, such as automating data entry, reconciliation, invoice processing, and approval workflows, while also addressing the ethical considerations of AI automation.
You will start with the foundations: how AI, machine learning, and natural language processing work, and how tools such as QuickBooks and Xero already use machine learning to match and categorise transactions. You will then put the theory into practice.
In the hands-on sections, you will use Google Colab and Jupyter Notebook to build a machine learning model that classifies financial transactions from their descriptions, and you will use Optical Character Recognition with Tesseract to extract invoice numbers, dates, customer names, and totals from invoice images and export the results to Excel.
The course closes with the ethics of AI automation, covering fairness, privacy by design, data minimisation, transparency, and the AICPA Audit Data Standards, alongside real-world case studies including JPMorgan Chase, American Express, and KPMG.
The course aims to enhance the efficiency and accuracy of accounting processes, making it highly relevant for professional development, including for ACCA, CPA Canada, and CPA Australia members. After completing this course successfully, you will earn 10 CPD units (equivalent to 10 hours).
What You Will Learn
- Explain how artificial intelligence, machine learning, and natural language processing apply to routine accounting and auditing tasks.
- Identify where AI can automate work such as data entry, reconciliation, invoice processing, and approval workflows.
- Build a machine learning model in Google Colab that classifies financial transactions from their descriptions.
- Use Optical Character Recognition with Tesseract to extract key fields from invoice images and export structured data to Excel.
- Set up and work within Google Colab and Jupyter Notebook (via Anaconda) using common Python libraries such as Pandas, NumPy, and Scikit-Learn.
- Apply ethical principles to AI automation, including fairness, privacy by design, data minimisation, and the AICPA Audit Data Standards.
- Evaluate real-world AI applications in finance through case studies such as JPMorgan Chase, American Express, and KPMG.
Who This Course Is For
- Professional accountants who want to automate repetitive, manual tasks using AI.
- Members and students of ACCA, CPA Canada, and CPA Australia seeking relevant CPD.
- Finance and audit professionals curious about practical, hands-on applications of machine learning.
- Accountants new to Python who want a guided, step-by-step introduction to automation tools.
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