Multimodal Prompting: Expanding Beyond Text

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

Chapter 10 in Prompt Engineering for Accounting and Finance, 2026, pp 341-377 from Springer

Abstract: Abstract This chapter introduces multimodal AI as an emerging capability that enables generative systems to process and integrate diverse data formats—including text, tables, images, financial statements, and structured documents—within accounting and finance workflows. It explains how combining visual, numerical, and narrative inputs enhances analytical depth beyond traditional text-only prompting. Through applications such as financial statement review, invoice processing, contract analysis, and dashboard interpretation, the chapter demonstrates how multimodal models support richer insight extraction and improved anomaly detection. It emphasizes the importance of structured prompt design when integrating visual and tabular data to reduce misinterpretation and hallucination risk. The chapter further discusses practical considerations, including data formatting, validation, and governance when working with sensitive financial information. By positioning multimodal AI as a bridge between unstructured documents and structured financial systems, this chapter expands prompt engineering beyond text and prepares professionals for increasingly integrated analytical environments.

Keywords: Multimodal AI; Document Intelligence; Financial Statement Analysis; Invoice Processing Automation; Visual Data Interpretation; AI in Accounting Systems; Cross-Format Data Integration (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-11195-1_10

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DOI: 10.1007/978-3-032-11195-1_10

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