Accounting and Finance Essentials: Foundations for Effective Prompt Engineering

Sunil Kumar (), Atreya ‘Chuck’ Chakraborty () and Poojan Patel ()
Additional contact information
Sunil Kumar: Roger Williams University
Atreya ‘Chuck’ Chakraborty: University of Massachusetts System
Poojan Patel: Bryant University

Chapter 2 in Prompt Engineering for Accounting and Finance, 2026, pp 37-74 from Springer

Abstract: Abstract This chapter establishes the accounting and finance foundations necessary for effective prompt engineering in AI-assisted environments. It explains why accounting and finance present unique challenges for AI systems, including strict regulatory requirements, high-stakes precision, professional judgment, heterogeneous data, and multiple stakeholder perspectives. The chapter reviews core accounting principles—such as double-entry bookkeeping, accrual accounting, revenue recognition, financial statement interrelationships, and adjusting entries—and fundamental finance concepts, including time value of money, risk–return tradeoff, valuation methods, capital structure, liquidity and solvency, cost of capital, capital budgeting, and financial ratios. By integrating these technical foundations with applied examples, the chapter demonstrates that AI outputs in financial contexts require structured, context-rich prompting aligned with established standards. It argues that domain knowledge is not optional when using AI in accounting and finance; rather, it is essential for ensuring accuracy, compliance, and responsible decision-making. The chapter positions financial literacy as the prerequisite for reliable AI-enhanced analysis.

Keywords: Accounting Foundations; Finance Principles; Prompt Engineering in Finance; Financial Statement Analysis; Revenue Recognition; Capital Budgeting; Regulatory Compliance in Accounting (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-11195-1_2

Ordering information: This item can be ordered from
http://www.springer.com/9783032111951

DOI: 10.1007/978-3-032-11195-1_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().