Which of the following techniques would best detect on inventory fraud scheme?

An inventory fraud scheme, such as theft, often results in discrepancies that must be concealed through inventory adjustments or write-offs. By performing a data analysis that stratifies (groups) these adjustments by warehouse location, an auditor can identify outliers. A warehouse with a significantly higher volume or value of adjustments compared to other, similar locations would be a major red flag indicating potential theft or mismanagement. This analytical technique effectively pinpoints high-risk areas for further investigation, making it a powerful tool for detecting such fraud.

A. This technique is used to detect procurement fraud or circumvention of spending controls, not the theft of physical inventory.
C. This analysis is designed to detect vendor overbilling or other procurement irregularities, which is unrelated to the physical loss of inventory.
D. This is a standard test for the accounts payable function to find payment errors or fraud, not a method for detecting inventory misappropriation.

1. Albrecht, W. S., Albrecht, C. O., Albrecht, C. C., & Zimbelman, M. F. (2019). Fraud Examination (6th ed.). Cengage Learning. Chapter 10, "Data-Driven Fraud Detection," discusses using data analysis techniques like stratification and outlier investigation to identify anomalies in records, including inventory adjustments, that may signal fraud.

2. Singleton, T. W., & Singleton, A. J. (2010). Fraud Auditing and Forensic Accounting (4th ed.). John Wiley & Sons. Chapter 11, "Data Mining and Digital Analysis Tools," describes how stratifying data, such as inventory write-offs by location or employee, is a primary method for identifying potential fraud schemes.

3. Vona, L. W. (2017). The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems. John Wiley & Sons. In the chapter on the inventory management process, Vona explains that analyzing inventory adjustments and scrap transactions, especially when sorted by location or employee, is a key proactive test to detect theft.