Automated Population-Level Audit Assurance via AI-Based Document Intelligence
For auditors and financial institutions, this framework addresses the scalability bottleneck of manual, sample-based audit testing by enabling automated, population-level reconciliation.
The paper presents an AI-based framework for automated audit transaction testing that extracts structured data from unstructured PDF statements using Snowflake Document AI with a small labeled corpus, enabling population-level testing instead of sampling. This improves audit coverage and supports continuous assurance.
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.