Automated Population-Level Audit Assurance via AI-Based Document Intelligence
A new research paper published on arXiv introduces an automated framework for large-scale audit transaction testing using AI-based document intelligence. Traditional audit methods rely on manual, sample-based reviews of unstructured PDF statements, which are labor-intensive and fail to scale effectively for millions of transactions. This proposed solution leverages Snowflake Document AI to extract structured data from unstructured PDFs using a small labeled corpus of approximately 20 documents. The extracted data is then reconciled against authoritative source-of-truth datasets to identify discrepancies at scale. Results are presented through interactive dashboards and automated reports. By enabling population-level testing rather than sampling, the framework significantly improves audit coverage and supports continuous assurance objectives. The study highlights how recent advances in document intelligence and analytics-driven audit frameworks facilitate scalable, near real-time risk identification. This approach addresses critical inefficiencies in financial auditing by automating the validation of customer-facing statements against internal systems of record, offering a robust solution for modern compliance and risk management needs.
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Automated Population-Level Audit Assurance via AI-Based Document Intelligence
A new research paper published on arXiv introduces an automated framework for large-scale audit transaction testing using AI-based document intelligence. Traditional audit methods rely on manual, sample-based reviews of unstructured PDF statements, which are labor-intensive and fail to scale effectively for millions of transactions. This proposed solution leverages Snowflake Document AI to extract structured data from unstructured PDFs using a small labeled corpus of approximately 20 documents. The extracted data is then reconciled against authoritative source-of-truth datasets to identify discrepancies at scale. Results are presented through interactive dashboards and automated reports. By enabling population-level testing rather than sampling, the framework significantly improves audit coverage and supports continuous assurance objectives. The study highlights how recent advances in document intelligence and analytics-driven audit frameworks facilitate scalable, near real-time risk identification. This approach addresses critical inefficiencies in financial auditing by automating the validation of customer-facing statements against internal systems of record, offering a robust solution for modern compliance and risk management needs.
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