Generative AI & Governmental Financial Reporting

Rutgers the State University of New Jersey

Generative AI & Governmental Financial Reporting

Instructor: Huaxia Li

Access provided by Masterflex LLC, Part of Avantor

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Details to know

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Assessments

9 assignments

Taught in English

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There are 4 modules in this course

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:

• How can LLMs be used to process and analyze financial reports? • What are the challenges of implementing AI in accounting? • How can AI-driven frameworks improve accuracy and efficiency in financial reporting? By the end of the course, you’ll understand how AI-powered tools can automate data extraction, integrate workflows, and improve financial decision-making. This course is designed for: • Accounting and finance professionals looking to integrate AI into their workflows. • Governmental financial analysts and auditors handling large datasets. • AI and data science professionals interested in applications of LLMs in financial reporting. • Students and researchers in accounting, finance, or AI-related fields. Learners with any background are welcome. However, Basic knowledge of accounting principles and financial reporting, familiarity with AI concepts and programming (e.g., Python) are recommended.

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

4 videos Total 17 minutes

Welcome, Agenda and Introducation 4 minutes
The Development of GPT Models 4 minutes
Large Language Models and Accounting 4 minutes
Skill Gaps, Prompt Engineering and Evolution 5 minutes

2 readings Total 25 minutes

Course Syllabus 10 minutes
AI in Accounting 15 minutes

2 assignments Total 24 minutes

Introduction to LLMs 12 minutes
LLMs and Accounting 12 minutes

1 discussion prompt Total 20 minutes

LLM Opportunity for An Accountant 20 minutes

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

5 videos Total 23 minutes

UI Method 5 minutes
API Method 4 minutes
Robotic Process Automation (RPA) 6 minutes
API-RPA Integration 6 minutes
Choosing the Right Method 2 minutes

1 reading Total 30 minutes

Implementing LLMs Efficiently and Successfully 30 minutes

2 assignments Total 24 minutes

UI and API Methods 12 minutes
Robotic Process Automation (RPA) 12 minutes

1 discussion prompt Total 20 minutes

Implementation Methods and Long-term Outlook 20 minutes

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

6 videos Total 25 minutes

Introduction to Unstructured Data 6 minutes
Data Preparation 3 minutes
Prompt Engineering Techniques 5 minutes
Batch Querying and Database Construction 3 minutes
An Example - Extracting Annual Comprehensive Financial Reports 3 minutes
An Example - Extracting ESG Report Data 5 minutes

1 reading Total 30 minutes

Use Case of AI in Accounting 30 minutes

2 assignments Total 28 minutes

Unstructured Data Preparation 16 minutes
Prompt Engineering 12 minutes

1 discussion prompt Total 20 minutes

Prompt Design 20 minutes

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.