Core Concepts of Artificial Intelligence for Accounting Professionals
AICPA & CIMA
Last updated September 6, 2026
Core Concepts of Artificial Intelligence for Accounting Professionals is a self-study course from AICPA & CIMA that sets out the vocabulary and the building blocks of AI for people who work with financial data. It separates machine learning, deep learning and neural networks from one another, then asks which kind of model suits which finance or accounting problem. The second half turns to application: analyzing financial data with AI techniques, fitting AI tools into existing accounting workflows, and judging whether an AI-driven solution genuinely improves a decision. Ethical and regulatory considerations, risk assessment and responsible deployment close it out. It is a paid course, taken online, with no prior knowledge assumed.
What you'll learn
- Telling machine learning, deep learning and neural networks apart
- Choosing a model type suited to a given finance or accounting problem
- Applying AI techniques to the analysis of financial data
- Integrating AI tools into existing accounting workflows
- Evaluating whether an AI-driven solution improves performance and decision-making
- Ethical and regulatory considerations, and what responsible deployment requires
Frequently asked questions about Core Concepts of Artificial Intelligence for Accounting Professionals
Who is Core Concepts of Artificial Intelligence for Accounting Professionals for?
Accounting and finance professionals, students and business decision makers who want a grounding in AI concepts applied to financial work, with no prior knowledge assumed; the course carries US CPE credit.
Is Core Concepts of Artificial Intelligence for Accounting Professionals free?
No — Core Concepts of Artificial Intelligence for Accounting Professionals is a paid course.
What are the prerequisites for Core Concepts of Artificial Intelligence for Accounting Professionals?
None.
Why we suggest this course
For accountants, finance professionals and the business leaders who decide what software their teams use. Much AI training is either too general to apply at work or too technical to start from; this holds a middle line — enough mechanism to tell one model type from another, framed throughout around financial data and accounting workflows. The closing material on risk and responsible deployment is pitched at the same non-technical reader.