AnalysisBank：用于财务报告生成的专家分析模式库
AnalysisBank: An Expert Analysis Pattern Library for Financial Report Generation
- National University of Singapore（新加坡国立大学）
- Singapore Management University（新加坡管理大学）
- Asian Institute of Digital Finance（亚洲数字金融研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出AnalysisBank这一专家分析模式库，用于在分析层面生成财务报告，经实验验证其可提升数据驱动新洞察比例，且该方法可迁移至科学写作领域。
AI中文摘要：
我们认为，财务报告生成应在分析层面而非结构层面开展，从数据衍生的洞察而非高级主题或章节中组合内容。为此，我们提出AnalysisBank，它将专家报告提炼为可复用的分析（Analysis）库，每个分析包含数据信号、分析动作及它所源自的专家文本片段。在推理阶段，AnalysisBank将输入信号与库条目匹配，并应用检索到的动作来组合报告。对从550份专家报告中提炼的分析的研究显示，存在47-52种信号类型的重尾分布，涵盖13种动作类型。在两个财务基准上，使用四个大语言模型（LLM）主干的情况下，AnalysisBank使基于数据的新洞察的比例较结构层面基线提高了1.7-3.7倍。向科学写作的迁移表明，该区分不仅适用于金融领域。代码及提炼的分析库可在此httpsURL获取。
英文摘要：
We argue that financial report generation should operate at the analytical rather than structural level, composing content from data-derived insights rather than high-level topics or sections. To this end, we propose AnalysisBank, which distills expert reports into a reusable library of Analyses, each pairing a data signal, an analytical move, and the expert span it was derived from. At inference time, AnalysisBank matches input signals to library entries and applies the retrieved moves to compose the report. A study of Analyses distilled from 550 expert reports reveals a heavy-tailed distribution of 47-52 signal types spanning 13 move types. On two financial benchmarks across four LLM backbones, AnalysisBank increases the proportion of novel, data-grounded insights by 1.7-3.7x over structural-level baselines. Transfer to scientific writing suggests that the distinction generalizes beyond finance. Code and the distilled Analysis library are available at https://github.com/yajingyang/AnalysisBank.
