Automated Population-Level Audit Assurance via AI-Based Document Intelligence

• URL: http://arxiv.org/abs/2605.05252v1
• Date: Tue, 05 May 2026 19:17:18 GMT
• Title: Automated Population-Level Audit Assurance via AI-Based Document Intelligence
• Abstract summary: Audit transaction testing validates accuracy and completeness of customer-facing statements against internal systems of record.<n>Traditional manual, sample-based review of unstructured PDF statements is labor-intensive and does not scale to millions of transactions.<n>This paper presents an automated framework for large-scale audit transaction testing using AI-based document intelligence.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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