This course explores how Generative AI, particularly Large Language Models (LLMs), can transform governmental reports and accounting practices. You will learn how AI can optimize financial data extraction, improve decision-making, and enhance the efficiency of accounting processes. The course addresses key questions such as:
Generative AI & Governmental Financial Reporting
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Earn a Certificate
With paid plans
What you'll learn
Understand the role of AI and LLMs in modern accounting practices.
Utilize LLMs to extract structured financial data from unstructured governmental reports.
Evaluate the accuracy and efficiency of AI-enabled data extraction frameworks.
Skills you'll gain
- Accounting
- Application Programming Interface (API)
- Robotic Process Automation
- Decision Making
- Generative AI
- Automation
- Unstructured Data
- Governmental Accounting
- Data Processing
- Accounting Systems
- User Interface (UI)
- Artificial Intelligence
- Financial Data
- Scalability
- Financial Reporting
- Artificial Intelligence and Machine Learning (AI/ML)
Details to know
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Recently updated!
May 2025
Assessments
9 assignments
Taught in English
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There are 4 modules in this course
By the end of Module 1, learners will gain a foundational understanding of AI and machine learning and their relevance to accounting. They will be able to describe Large Language Models (LLMs) and their applications in the field while recognizing both the benefits and challenges of integrating LLMs into accounting practices. Additionally, they will understand the importance of prompt engineering in shaping LLM outputs and appreciate how technological advancements have made LLMs more accessible to non-technical users.
What's included
4 videos2 readings2 assignments1 discussion prompt
By the end of Module 2, learners will understand various methods for implementing LLMs in accounting, including UI, API, UI-RPA, and API-RPA, and be able to evaluate their advantages and limitations. They will develop the ability to choose the most suitable implementation approach for different accounting tasks while considering key integration factors. Additionally, they will gain insights into practical considerations and make informed decisions about LLM adoption based on organizational needs and available resources.
What's included
5 videos1 reading2 assignments1 discussion prompt
By the end of Module 3, learners will understand the challenges of extracting financial data from unstructured sources and explore the components and workflow of an LLM-enabled data extraction framework. They will learn how to apply prompt engineering techniques to enhance extraction accuracy and recognize how the framework can be adapted for various financial documents. Additionally, they will appreciate the efficiency and accuracy benefits that LLMs bring to financial data extraction.
What's included
6 videos1 reading2 assignments1 discussion prompt
By the end of Module 4, learners will be able to evaluate the accuracy and efficiency of an LLM-enabled data extraction framework and interpret its results across different financial documents. They will identify common extraction errors and apply strategies to address them while refining prompts to enhance performance. Additionally, they will explore considerations for scaling the framework to handle larger datasets and different LLMs effectively.
What's included
3 videos1 reading3 assignments1 discussion prompt
Instructor
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