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

TL;DR

This paper introduces an AI-driven framework for automating large-scale audit transaction testing, replacing manual PDF reviews with scalable, real-time discrepancy detection using document intelligence.

Contribution

It presents a novel automated approach leveraging Snowflake Document AI for extracting and reconciling data from unstructured PDFs at scale.

Findings

Enabled population-level audit testing rather than sampling

Improved audit coverage and continuous assurance capabilities

Demonstrated scalable, near real-time risk identification

Abstract

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.