Improving Zero-Shot Financial Intelligence in Accounting and Tax Systems Through Multimodal Financial Data Alignment

• Eshal Nasir

  Author

• Laiba Qaisar

  Author

Zero-shot financial intelligence aims to extend the capabilities of accounting and tax systems to perform new financial analysis, compliance verification, and reporting tasks without requiring extensive task-specific training. This paper proposes a novel approach to improving zero-shot learning in intelligent accounting and tax systems through multimodal financial data alignment. By enhancing the relationship between visual financial documents, including invoices, receipts, tax forms, and financial statements, and textual accounting records, the proposed framework improves the system's ability to generalize across previously unseen financial tasks and regulatory scenarios. The approach utilizes advanced machine learning techniques, multimodal representation learning, and intelligent financial knowledge integration to strengthen automated bookkeeping, tax compliance monitoring, fraud detection, audit support, and financial reporting processes. Experimental evaluations demonstrate that multimodal financial data alignment significantly improves accuracy, adaptability, and robustness when processing unfamiliar document formats and emerging compliance requirements. The results indicate that zero-shot financial intelligence can reduce manual intervention, increase operational efficiency, and enhance decision-making capabilities in modern accounting and tax management systems.

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