Automated Population-Level Audit Assurance via AI-Based Document Intelligence
Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Vasudevan, Santosh, Natarajan, Velu
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7160599308
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