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Navigating the Complex World of Tax Footnotes: A GPT-4-Driven Case Study in Financial Statements
Journal article   Open access   Peer reviewed

Navigating the Complex World of Tax Footnotes: A GPT-4-Driven Case Study in Financial Statements

Tony L. J. Lin and Anthony P. Curatola
Journal of emerging technologies in accounting, pp 1-9
01 Apr 2026
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.2308/JETA-2023-064View
Published, Version of Record (VoR) Open

Abstract

Business & Economics Business, Finance Social Sciences
This study examines how students develop professional judgment by analyzing the "Income Taxes" footnotes of Apple Inc. and Amazon.com Inc. The design guides a four-step workflow: (1) compute effective tax rates; (2) identify qualitative drivers: unrecognized tax benefits, deferred tax assets/liabilities/, valuation allowances, and cross-border/legal exposure; (3) apply a deterministic large-language-model prompt using context engineering; and (4) reconcile human and model conclusions to deliver a concise evidence-based comparative conclusion. Structured worksheets and reflections promote reproducibility and professional skepticism. The paper further provides validated reference analyses, additional guidance, and discusses retrieval-augmented generation (RAG) as an optional pathway for source validation and auditability. By linking computational tools with evidence-based reasoning, the study provides a rigorous, ready-to-use framework that accelerates the first-pass reading of dense disclosures while maintaining a central role for human judgment.

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