5 Accounts Payable and Expense Audit Procedures You Can Perform with DataSnipper AI Agents
Auditing accounts payable and expenses often involves reviewing large volumes of documents, including purchase orders, goods receipt notes, invoices, and payment records. To verify the accuracy and completeness of transactions, auditors must perform various procedures to reconcile documents, validate information, and investigate unusual items.
Performing these tasks manually can be time-consuming, particularly for businesses processing thousands of transactions each month. Managing large datasets also increases the risk of overlooking discrepancies during the audit process.
DataSnipper's Prebuilt AI Agents are designed to automate repetitive audit tasks, from document reconciliation to identifying unusual transactions.
Instead of reviewing each document individually, auditors can provide the relevant data and supporting documents for AI Agents to perform reconciliations, consolidate results, and flag items requiring further investigation. This allows auditors to dedicate more time to risk assessment, exception investigation, and professional judgment.
Below are five accounts payable and expense audit procedures that can be supported by DataSnipper AI Agents.
1. Three-way matching in the procurement process
What does the AI Agent do?
Three-way matching is an audit procedure that checks the consistency of three documents in the procurement process: the purchase order (PO), goods receipt note (GRN), and supplier invoice.
The purpose of this procedure is to verify that goods have been ordered, received, and recorded for payment in accordance with the agreed quantities, unit prices, and contractual terms.
DataSnipper's AI Agent helps extract information from these documents and automatically reconcile the relevant data.
Benefits for the audit process:
Manufacturing, trading, and distribution companies often process large volumes of purchasing transactions. Manually matching individual invoices against purchase orders and goods receipt notes can be time-consuming.
The AI Agent reduces manual verification by identifying matching transactions and flagging discrepancies that require further investigation.
Audit results:
Once auditors provide purchasing data and supporting documents, the AI Agent reconciles quantities, unit prices, supplier information, and reference details across the documents.
The results are consolidated into a table that distinguishes successfully matched transactions from discrepancies, such as differences between received quantities and invoiced quantities, unit prices that do not match purchase orders, or missing supporting documents.
Key considerations:
For orders involving multiple deliveries, auditors should review the total quantity received and the delivery terms before concluding that a discrepancy exists.
In addition, if a business uses multiple procurement management systems, auditors should ensure that all relevant data has been provided to avoid incomplete or inaccurate reconciliation results.
2. Duplicate payment detection
What does the AI Agent do?
Duplicate payments may occur when the same invoice is processed for payment more than once or when similar payments are recorded under different transaction details.
Common scenarios include an invoice being paid twice, a supplier statement being paid even though the corresponding invoices have already been settled, or payments being recorded under suppliers with similar names.
The AI Agent helps review invoice and payment data to identify potentially duplicate transactions.
Benefits for the audit process:
When using sampling techniques, auditors may not detect duplicate payments that fall outside the selected sample.
The AI Agent enables auditors to analyze a larger volume of payment data, helping identify unusual transactions that might be difficult to detect through manual procedures.
Audit results:
The AI Agent analyzes invoice data and payment records, using information such as supplier names, invoice numbers, amounts, and transaction dates to identify potential duplicates.
Results may include exact duplicates, transactions with similar information, or multiple payments to the same supplier within a short period.
The system consolidates flagged transactions and their associated payment amounts, helping auditors prioritize higher-risk cases.
Key considerations:
Not all transactions with similar information represent duplicate payments. Some may result from adjustments to outstanding balances, reissued invoices, or installment payments under contractual arrangements.
Auditors must therefore verify the supporting documentation and the nature of each transaction before determining whether an error has occurred or assessing potential recoverable amounts.
3. Search for unrecorded liabilities
What does the AI Agent do?
One of the key objectives when auditing accounts payable is to assess the completeness of liabilities at the reporting date.
In practice, a business may have received goods or services before the end of an accounting period without recording the corresponding liability.
To identify such cases, auditors typically examine payments made after the reporting date and reconcile them against invoices, delivery documents, and outstanding payable balances.
DataSnipper's AI Agent helps automate this review process, identifying transactions that may indicate unrecorded liabilities.
Benefits for the audit process:
Searching for unrecorded liabilities often requires auditors to review numerous transactions occurring after the reporting period.
The AI Agent helps filter and identify transactions relating to the previous period, reducing the time spent searching for information and reconciling documents.
Audit results:
Auditors provide data on payments made after the reporting date, outstanding payable balances, and relevant supporting documents.
Using invoice dates, purchase order dates, goods receipt dates, or service delivery dates, the AI Agent identifies payments made after the reporting date that may relate to obligations arising in the previous period.
These transactions are consolidated into a list for further verification and assessment by auditors.
Key considerations:
The review period should be determined based on the business's payment cycle and risk profile, such as 30–60 days after the reporting date.
Where invoice dates and goods receipt dates fall in different accounting periods, auditors should examine the transaction terms and the timing of the underlying obligation before reaching a conclusion.
Significant recurring expenses, such as rent, utilities, and service fees, should also be reviewed to assess the possibility of unrecorded accrued expenses.
4. Supplier statement reconciliation
What does the AI Agent do?
Supplier statement reconciliation is an audit procedure that compares a company's accounting records with outstanding balances reported by its suppliers.
Differences between these two sources may arise from unrecorded invoices, payments that have not yet been updated, timing differences, or unresolved disputes.
The AI Agent helps reconcile supplier statements with the accounts payable ledger to identify discrepancies requiring further investigation.
Benefits for the audit process:
For businesses with numerous suppliers or frequent purchasing transactions, manual reconciliation can be time-consuming.
The AI Agent enables auditors to identify matched items more quickly and focus their attention on unexplained discrepancies.
Audit results:
After receiving supplier statements and the corresponding accounts payable data, the AI Agent performs line-by-line reconciliation of invoices, credit notes, and payments.
The results are presented in a reconciliation table, classifying matched transactions, items appearing on supplier statements but not in the accounting records, and vice versa.
The system also helps identify differences that may result from transaction timing.
Key considerations:
To improve the reliability of audit evidence, auditors should prioritize obtaining supplier statements directly from suppliers where appropriate.
They should also distinguish temporary timing differences from actual accounting errors.
If a significant supplier account contains numerous unreconciled items, auditors should consider whether this indicates weaknesses in the company's accounts payable management and internal control processes.
5. Corporate card expense verification
What does the AI Agent do?
Many businesses use corporate payment cards to cover business travel, entertainment, purchasing, and other operating expenses.
These transactions are typically low to medium in value but occur frequently, making individual expense verification time-consuming.
The AI Agent helps reconcile corporate card transactions with invoices, receipts, and approval records to identify expenses with missing documentation, unauthorized transactions, or amount discrepancies.
Benefits for the audit process:
Corporate card policy violations may be spread across numerous transactions, including personal expenses charged to corporate cards, spending above authorized limits, or transactions without valid supporting documentation.
Using an AI Agent to review transaction data allows auditors to examine a broader range of expenses and identify unusual patterns requiring investigation.
Audit results:
Auditors provide corporate card transaction data, invoices, receipts, and related approval information.
The AI Agent matches transactions based on amounts, dates, and suppliers, while checking compliance with spending limits and approval procedures using the available data.
Results may include fully reconciled transactions, expenses with missing invoices or receipts, unauthorized expenses, and discrepancies between card statements and supporting documents.
Key considerations:
Auditors should distinguish between expenses with missing supporting documents and expenses that have not been authorized, as these may indicate different internal control issues.
Priority should also be given to high-value or unusual transactions and cardholders with multiple flagged transactions.
For expenses that may be personal in nature, auditors should further examine the transaction purpose and the company's expense policies before reaching a conclusion.
6. Getting started with AI Agents in auditing
The five AI Agents introduced above are part of the DataSnipper Prebuilt Agents library, which is designed to support specific audit procedures. Rather than developing automated workflows from scratch, auditors can select a suitable Agent, provide the required input data, and perform the procedure following the system's guidance.
These Agents can also be customized through Agent Builder to better align with an audit firm's methodology, engagement scope, and client data characteristics.
According to DataSnipper's original article, the Prebuilt Agents library includes 25 audit procedures covering various areas, such as revenue, accounts payable, fixed assets, cash, inventory, payroll, leases, and borrowings.
However, the effectiveness of AI Agents depends on the quality of input data, the completeness of supporting documentation, and the appropriate configuration of verification rules.
AI Agents support audit procedures but do not replace auditors' professional judgment. Flagged transactions still need to be verified, their underlying causes assessed, and their potential impact evaluated before conclusions are reached.
Adopting AI Agents therefore not only helps reduce repetitive reconciliation tasks but also enables auditors to devote more time to risk analysis, control assessment, and matters requiring professional judgment.
Learn more about DataSnipper here.
EGP Vietnam
DataSnipper consulting and implementation partner in Vietnam
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Thai Nguyen
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Source: DataSnipper, 5 AP Audit Procedures External Auditors Run With a Prebuilt Agent.
