用AI自动审计百万份文档，提升审计覆盖率和实时性。
Automated Population-Level Audit Assurance via AI-Based Document Intelligence
- 用少量标注文档训练AI，从PDF中提取结构化数据
- 在20份标注样本基础上实现大规模差异检测
- 适合需要连续审计与高覆盖的金融/合规场景
审计交易测试旨在验证面向客户的声明与内部系统记录的一致性。传统的人工抽样审查未结构化PDF文件的方式耗时且难以扩展至数百万笔交易。本文提出一种基于AI文档智能的自动化框架，用于大规模审计交易测试。该方案利用Snowflake Document AI，仅需约20份标注文档的小规模语料，即可从非结构化PDF声明中提取结构化数据，并与权威源数据进行比对，识别大规模差异。结果通过交互式仪表盘和自动化报告呈现。该框架实现了全量测试而非抽样，提升了审计覆盖率，支持持续保证目标。文档智能与分析驱动的审计框架进步，使可扩展、近实时的风险识别和持续保证成为可能。
原文摘要 · Abstract (English)
Audit transaction testing validates accuracy and completeness of customer-facing statements against internal systems of record. Traditional manual, sample-based review of unstructured PDF statements is labor-intensive and does not scale to millions of transactions. This paper presents an automated framework for large-scale audit transaction testing using AI-based document intelligence. The solution leverages Snowflake Document AI to extract structured data from unstructured PDF statements using a small labeled corpus (approximately 20 documents). Extracted data are reconciled against authoritative source-of-truth datasets to identify discrepancies at scale. Results are surfaced through interactive dashboards and automated reports. The framework enables population-level testing rather than sampling-based approaches, improving audit coverage and supporting continuous assurance objectives. Recent advances in document intelligence and analytics-driven audit frameworks enable scalable, near real-time risk identification and continuous assurance.
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