Detecting Fraud with Analytical Procedures: Common Audit Deficiencies and How to Improve

By Jaclyn M. Veno, CPA (licensed in NC and SC)

Auditors have long struggled with detecting fraud. According to the 2024 Association of Certified Fraud Examiners (ACFE) Report to the Nations, only 3% of occupational fraud  cases are initially detected by external auditors. Even more  startling is the fact that 5% of fraud is discovered by accident. In other words, fraud is statistically more likely to be uncovered unintentionally than by the procedures of a trained audit team. This is a troubling reality that raises important questions: Why is fraud so difficult to detect through audit procedures, and what role do analytical procedures play in addressing this challenge? 

Under Generally Accepted Auditing Standards (GAAS), AU-C Section 520 defines analytical procedures as the evaluations of financial information through analysis of plausible relationships among both financial and nonfinancial data. These procedures also encompass investigation – when necessary, of fluctuations or relationships that appear inconsistent with other relevant information or deviate from expected values by a significant amount.

Analytical procedures are required at three points in the audit process: during the planning and risk assessment phase, substantive testing and the final review phase. Preliminary analytics help identify unusual trends early in the engagement while substantive analytics may serve as evidence in support of specific account balances. Final analytical procedures are used to form an overall conclusion from the financial statements.

Methodology 

The methods used to perform analytical procedures vary widely, ranging from straightforward comparisons to complex analyses using advanced statistical techniques. These procedures can be applied to consolidated financial statements, individual components or specific elements of financial information. The most common forms of analytical procedures include trend analysis, ratio analysis, reasonableness testing and regression analysis.

1.     Trend analysis involves evaluating how account balances change over time, such as reviewing several periods of sales revenue for unusual fluctuations.
2.      Ratio analysis focuses on comparing relationships between financial accounts or between financial and nonfinancial data, such as comparing gross margin percentages across periods or industry benchmarks.
3.      Reasonableness testing involves developing expectations for account balances based on both financial and nonfinancial inputs. For example, the number of employees might be used to estimate expected payroll or accrued vacation balances.
4.      Regression analysis is a more advanced technique, employing statistical modeling to quantify expectations with measurable precision. This approach is most effective when using disaggregated data from systems with strong internal controls.

Despite the technical value and flexibility of these procedures, audits often fall short in their application particularly in areas where fraud risk is higher, such as revenue recognition.

Common Deficiencies

There are several common deficiencies in the performance of analytical procedures that reduce their effectiveness and in turn diminish their usefulness in detecting fraud.

One of the most frequently observed deficiencies is the inappropriate use of analytical procedures as the sole form of substantive testing – especially in relation to revenue. Relying exclusively on analytics to test revenue can be risky because revenue is often complex and highly susceptible to manipulation. Analytical procedures provide an overview of trends and relationships but may not reveal detailed transaction-level discrepancies that other substantive tests – like tests of details, are designed to uncover. As a result, auditors who do not incorporate different forms of testing risk missing something.

Another common issue is the use of imprecise or vague expectations. For instance, when auditors base their expectations simply on the assumption that account balances will remain consistent with the prior year they risk weakening the reliability of their analysis. A general expectation without a well-supported quantitative basis does little to help identify anomalies. Not only does this approach fail to identify significant discrepancies, but it can also lead to unnecessary explanations for immaterial variances which  would result in the waste of valuable audit resources.

A third deficiency relates to the failure to test the completeness and accuracy of the data used to perform analytical procedures. The conclusions drawn from analytics are only as strong as the underlying data. If auditors rely on unverified, incomplete or inaccurate data when designing analytical procedures they may reach incorrect conclusions. For example, using unsupported budget data or management-prepared schedules without verifying them against the general ledger undermines the reliability of the analysis. To address this, auditors should perform recalculations, agree figures to underlying support, and reconcile data with source systems or the trial balance.

Additionally, auditors sometimes fail to properly investigate unexpected differences that arise during analytical procedures. When fluctuations or deviations exceed the auditor’s threshold for acceptable variance and remain unexplained the auditor must evaluate the nature and cause of the difference. Failure to do so may lead to missed red flags or unaddressed risks of material misstatement. Investigations should be documented and based on a clear audit response. Dismissing a variance without sufficient inquiry or follow-up jeopardizes the integrity of the audit.

The final common deficiency is a lack of corroborating evidence for management’s explanations. Auditors may document that management has provided an explanation for a difference, but without additional audit procedures this explanation remains unverified. Simply stating that “management said the difference is due to seasonality” or “management expects growth in this area” does not meet audit standards. Instead, auditors should critically assess management’s explanation, quantify the impact of the difference and determine its significance. Where appropriate auditors should obtain corroborating evidence such as industry data, external confirmations or more detailed testing of the underlying accounts. Inquiry alone is rarely sufficient. Additional procedures such as recalculation, inspection or reperformance should be used to validate the explanation.

How Auditors Can Strengthen Procedures

To strengthen the use of analytical procedures and increase their potential in detecting fraud, auditors must be deliberate in determining whether such procedures are appropriate for the assertion being tested. The effectiveness and efficiency of analytical procedures in identifying misstatements depend on several factors.

One is the nature of the assertion itself. Certain assertions are often better suited to analytical procedures than others particularly when there are strong predictable relationships present.

Another important factor is the plausibility and predictability of the relationships used in the analysis. For example, income statement accounts which represent activity over a period of time tend to be more predictable than balance sheet accounts which reflect a snapshot in time.

Auditors should also consider the availability and reliability of the data used to develop expectations. Information derived from IT systems with effective IT controls is more reliable especially when it is subject to other audit procedures or comes from independent third parties. A good example is interest expense: if the related debt balances have been confirmed and agreed upon supporting documentation, the expectation for interest expense becomes more precise and reliable.

Lastly, the precision of the auditor’s expectation plays a critical role in the quality of the procedure. More  precise expectations enable the auditor  to establish tighter thresholds for identifying variances and responding appropriately. Vague or loosely defined expectations may result in meaningless comparisons that fail to uncover fraud or material errors.

In conclusion, while analytical procedures are a powerful tool under GAAS, they are frequently underutilized or improperly executed. To maximize their effectiveness auditors must go beyond surface-level comparisons and apply professional judgment, develop high-quality expectations, verify the reliability of data and diligently investigate variances. When performed correctly, they can help auditors detect misstatements and identify fraud risks earlier in the engagement.
 

With an extensive background in accounting, auditing and adult learning theory, Jaclyn Veno oversees the development of Level Training programs for Galasso Learning Solutions,  one of the ASCPA’s preferred CPE providers. To see a list of their ASCPA course offerings, go to www.ascpa.com/galasso.