Prompt Engineering for Accounting and Finance

This textbook is the first resource specifically designed to equip business students in accounting and finance with a systematic and practical understanding of artificial intelligence (AI). Recognizing the current gap where business students often lack structured guidance on applying AI to their specific professional challenges, this book provides a comprehensive, step-by-step framework to effectively integrate AI into accounting and finance workflows. The textbook thoroughly addresses key areas, including foundational principles of prompt engineering, advanced techniques such as zero-shot, few-shot, chain-of-thought, and multimodal prompting, as well as strategies for handling structured data and managing task-oriented AI workflows. Emphasis is placed on robust evaluation practices, incorporating quantitative and qualitative assessments, alongside essential ethical considerations such as bias reduction, misinformation prevention, model transparency, and data confidentiality. By offering clearly defined frameworks, best practices, and detailed instructional guidance, this book serves as an indispensable resource for developing dedicated AI-focused courses in business schools. It uniquely empowers students to confidently harness AI tools within their professional contexts, ensuring they uphold rigorous ethical standards while achieving accuracy and reliability in financial and accounting tasks. As the singular resource of its kind, this textbook directly addresses the pressing educational need to prepare business students effectively for an increasingly AI-driven professional landscape.

Provides a comprehensive, step-by-step framework to effectively integrate AI into accounting and finance workflows Addresses strategies for handling structured data and managing task-oriented AI workflows Includes case studies, worked examples, evaluation rubrics, slide decks, and online exercises

Autorentext

Sunil Kumar PhD, CPA, is an Assistant Professor of Accounting at Roger Williams University. His work focuses on integrating AI, automation, and computational techniques into modern accounting and financial analysis, reflected in his papers on AI-based ESG signals, AI-focused CPA education, and algorithm assurance. He is the author of Python for Accounting and Finance, a widely used research tool for empirical and data-intensive work in accounting and finance.

Atreya “Chuck” Chakraborty is Professor of Finance at the University of Massachusetts Boston, where he directs the PhD program. His research focuses on corporate finance, financial strategy, governance, and value creation, with publications in leading journals including the Journal of Financial and Quantitative Analysis, Financial Management, and the Journal of Corporate Finance.

Poojan Patel is a Visiting Assistant Professor of Finance at Bryant University and a PhD scholar at UMass Boston. His research spans generative AI, prompt engineering, corporate disclosures, and AI applications in finance. He has presented at leading conferences, advancing work on AI and its application in finance.

Inhalt

CHAPTER 1:INTRODUCTION TO AI IN ACCOUNTING AND FINANCE.- CHAPTER 2: ACCOUNTING AND FINANCE ESSENTIALS.- CHAPTER 3:FUNDAMENTALS OF PROMPT ENGINEERING.- CHAPTER 4:FOUNDATIONS OF PROMPTING STRATEGIES.- CHAPTER 5:ENHANCING AI REASONING.- CHAPTER 6:KNOWLEDGE-DRIVEN PROMPTING.-CHAPTER 7: TASK-ORIENTED PROMPTING.- CHAPTER 8:ADAPTIVE AI.- CHAPTER 9:AUTOMATING AND OPTIMIZING PROMPTS.- CHAPTER 10:MULTIMODAL PROMPTING.- CHAPTER 11:STRUCTURAL PROMPTING.- CHAPTER 12: EVALUATING AI-GENERATED SOLUTIONS.- CHAPTER 13:ETHICAL AND PRACTICAL CONSIDERATIONS.