Finance: Internal Audit AI Agents
1. Problem
1a. Statement
Traditional internal audit processes rely on sample-based testing, reviewing only 5-10% of transactions and missing systemic issues that only emerge across entire populations. A Big Four accounting firm faced mounting pressure as clients demanded faster audits with greater coverage, while experienced auditors spent 60% of their time on repetitive documentation tasks. With SOX compliance deadlines, ITGC reviews, and accounts payable/receivable audits requiring verifiable workpapers, the firm needed AI agents capable of testing entire transaction populations while maintaining the auditability standards required for regulatory submissions.
1b. Client Profile
1c. Motivation
2. Analysis
2a. Requirements
The platform required specialized AI agents for each audit domain: SOX internal controls testing, ITGC reviews covering access management and change controls, accounts payable duplicate payment detection, and accounts receivable aging analysis. Each agent needed to process entire transaction populations rather than samples, applying rule-based checks augmented by LLM-as-a-judge patterns for nuanced policy interpretation. RAG pipelines enabled agents to retrieve relevant accounting standards, client policies, and prior year workpapers for context. Human-in-the-loop workflows routed exceptions and edge cases to senior auditors, while all outputs generated structured workpapers meeting regulatory documentation standards. The system required complete audit trails showing reasoning chains from source data through conclusions.
The platform required specialized AI agents for each audit domain: SOX internal controls testing, ITGC reviews covering access management and change controls, accounts payable duplicate payment detection, and accounts receivable aging analysis. Each agent needed to process entire transaction populations rather than samples, applying rule-based checks augmented by LLM-as-a-judge patterns for nuanced policy interpretation. RAG pipelines enabled agents to retrieve relevant accounting standards, client policies, and prior year workpapers for context. Human-in-the-loop workflows routed exceptions and edge cases to senior auditors, while all outputs generated structured workpapers meeting regulatory documentation standards. The system required complete audit trails showing reasoning chains from source data through conclusions.
2b. Constraints
3. Solution
3a. Architecture
3b. Implementation
4. Result
4a. DUBEScore™
4b. Outcomes
4c. Learnings
LLM-as-a-judge confidence thresholds required calibration per audit domain. SOX controls needed tighter thresholds than AP testing.
Workpaper templates evolved through three iterations based on QA feedback. Involve quality reviewers early in design.
Client ERP data extraction was the longest integration phase. Standardize connectors for top 5 ERP systems upfront.
Ready to Build Your AI Solution?
Let's discuss how we can deliver similar results for your organization.