Automated Population-Level Audit Assurance via AI-Based Document Intelligence

A new arXiv paper from Santosh Vasudevan and Velu Natarajan presents an automated framework for population-level audit assurance using AI-based document intelligence. The solution leverages Snowflake Document AI to extract structured data from unstructured PDF statements — the kind of customer-facing documents that auditors traditionally review manually with small samples. Remarkably, the system requires only about 20 labeled documents to train, then scales to reconcile millions of transactions against authoritative source-of-truth datasets. This enables auditors to test every single transaction rather than relying on statistically sampled subsets, dramatically increasing coverage and risk detection.

The framework outputs results through interactive dashboards and automated reports, allowing near real-time identification of discrepancies. By moving beyond traditional manual, sample-based reviews, the approach aligns with continuous assurance goals — where risks are monitored and addressed as they occur rather than retrospectively. The authors demonstrate that recent advances in document intelligence and analytics-driven audit frameworks now make scalable, population-level audit testing practical. For professionals in finance, compliance, and accounting, this could transform how regulatory audits and internal controls are conducted, reducing labor costs while improving accuracy and timeliness.