AnalysisBank: An Expert Analysis Pattern Library for Financial Report Generation

2026-09-01

AI Summary

本文提出AnalysisBank，通过从专家报告中提炼分析模式库来生成财务报告，提高了数据驱动的洞见比例。

Abstract

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.

Problem

• financial report generation
• analytical level
• data-derived insights

Innovation

• AnalysisBank
• data-derived insights
• analytical move
• financial report generation