AI For Finance And Accounting Professionals

Accounting and finance are among the professions most directly affected by recent advances in artificial intelligence. The information environment around corporations is being transformed, with 10-K filings, earnings calls, transaction data, and audit workpapers all becoming inputs to AI systems that can read, summarize, classify, and act.

At the same time, AI itself has become a material accounting question. How should investments in AI infrastructure be capitalized? When are circular transactions between AI companies meaningful, and when are they distortive? What does fair value look like for an AI model whose underlying technology may be obsolete in eighteen months?

This workshop builds the technical, economic, and governance fluency needed to engage substantively with the AI transformation of finance and accounting. Through conceptual instruction, paper discussions, hands-on exercises, and case studies drawn from current practice, participants develop the kind of durable understanding that allows them to evaluate AI applications, read disclosures, and assess vendor claims without relying on intermediaries.

Participants will build the technical and economic foundations needed to:

• Understand how large language models process accounting information, including tokens, embeddings, and representations
• Recognize the behavioral biases of AI models in financial decision-making
• Evaluate the AI training pipeline and the economics of build-versus-buy
• Navigate retrieval-augmented generation, knowledge graphs, and agentic AI in finance workflows
• Apply accounting principles to AI infrastructure, including capital expenditure, depreciation, and useful-life judgments
• Identify the governance, audit, and human-in-the-loop requirements for AI in financial workflows