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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.05252 |
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| _version_ | 1866910195153633280 |
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| author | Vasudevan, Santosh Natarajan, Velu |
| author_facet | Vasudevan, Santosh Natarajan, Velu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05252 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Population-Level Audit Assurance via AI-Based Document Intelligence Vasudevan, Santosh Natarajan, Velu Software Engineering Artificial Intelligence 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. |
| title | Automated Population-Level Audit Assurance via AI-Based Document Intelligence |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2605.05252 |