Improving Analytical Procedures: A Case of Using Disaggregate Multilocation Data

Abstract

According to SAS No. 56, Analytical Procedures, the use of disaggregate, individual location data can improve the effectiveness of analytical procedures used in multilocation audits. Using a case-study approach, we investigate whether improvements in the accuracy and precision of account balance expectations can be obtained by using disaggregate, individual location data in a large, multilocation company. Specifically, we examine two issues: (1) whether the summation of individual location expectations generates more accurate and precise expectations of company-wide account balances than expectations based on company-wide data only and (2) whether the accuracy and precision of analytical procedures is enhanced by including peer location observations of the account balance in individual location expectation models. We find that for the multilocation company examined in this case study the summation of individual location account balance expectations is not more accurate or precise than an expectation derived from aggregate models unless the individual location models include peer location observations of the account balance. When the individual location models include the same account observations from other peer locations within the company, the company-wide account balance expectations developed from disaggregate models are more accurate and precise (less variable) than expectations developed using aggregate, company-wide data only. The results from this case study indicate that when auditors are generating expectations of company-wide balances, disaggregate models incorporating peer location account observations provide account balance expectations that are both more accurate and more precise than company-wide, aggregate models. Given the limitations of a case-study approach, future research should be directed at establishing the generalizability of these findings.