AI-Based Framework for Automated Audit Transaction Testing
A study published on arXiv (2605.05252) presents an automated system designed for extensive audit transaction testing through AI-driven document intelligence. This innovative framework utilizes Snowflake Document AI to convert unstructured PDF statements into structured data, having been trained on a limited set of around 20 labeled documents. The system reconciles the extracted data with trusted source datasets to detect discrepancies on a large scale. Findings are showcased via interactive dashboards and automated reporting. By facilitating population-level testing rather than relying on traditional sampling methods, this approach enhances audit coverage and aligns with continuous assurance goals. The paper emphasizes recent developments in document intelligence that enable this method's implementation, addressing the challenges of manual reviews of unstructured PDF statements, which struggle with millions of transactions.
Key facts
- arXiv paper 2605.05252 presents an automated framework for audit transaction testing.
- The framework uses Snowflake Document AI for data extraction from unstructured PDFs.
- Training data consists of approximately 20 labeled documents.
- Extracted data is reconciled against source-of-truth datasets.
- Results are surfaced through interactive dashboards and automated reports.
- The approach enables population-level testing rather than sampling.
- It improves audit coverage and supports continuous assurance.
- Traditional manual review is labor-intensive and does not scale to millions of transactions.
Entities
Institutions
- arXiv
- Snowflake