Abstract
Financial reporting systems increasingly use large language models (LLMs) to extract and summarize corporate disclosures. However, most assume a single-market setting and do not address structural differences across jurisdictions. Variations in accounting taxonomies, tagging infrastructures (e.g., XBRL vs. PDF), and aggregation conventions make cross-jurisdiction reporting a semantic alignment and verification challenge. We present FinReporting, an agentic workflow for localized cross-jurisdiction financial reporting. The system builds a unified canonical ontology over Income Statement, Balance Sheet, and Cash Flow, and decomposes reporting into auditable stages including filing acquisition, extraction, canonical mapping, and anomaly logging. Rather than using LLMs as free-form generators, FinReporting deploys them as constrained verifiers under explicit decision rules and evidence grounding. Evaluated on annual filings from the US, Japan, and China, the system improves consistency and reliability under heterogeneous reporting regimes. We release an interactive demo supporting cross-market inspection and structured export of localized financial statements. Our demo is available at this https URL . The video describing our system is available at this https URL
Abstract (translated)
财务报告系统日益采用大语言模型（LLMs）来提取和汇总企业披露信息。然而，大多数系统假设单一市场环境，未解决跨司法管辖区的结构性差异。会计准则、标签基础设施（如XBRL与PDF）及汇总惯例的差异，使跨管辖区报告成为语义对齐与验证的挑战。本文提出FinReporting，一个面向本地化跨管辖区财务报告的智能体工作流。该系统针对利润表、资产负债表和现金流量表构建统一规范本体，并将报告分解为可审计阶段，包括文件获取、提取、规范映射与异常记录。FinReporting并非将LLMs用作自由生成器，而是将其作为受明确决策规则与证据约束的验证器。通过对美国、日本和中国的年度申报文件进行评估，该系统在异构报告体系下提升了 consistency 与可靠性。我们发布了支持跨市场审查与本地化财务报表结构化导出的交互式演示，演示地址见此链接。介绍本系统的视频见此链接。
URL
https://arxiv.org/abs/2604.05966
