To identify very small control and transaction anomalies, internal auditors should analyze the entire dataset rather than a sample. Full population analysis increases the likelihood of detecting:
* Unusual transaction patterns, including fraud, errors, and control weaknesses.
* Rare or subtle anomalies that might be missed in sampling-based audits.
* Machine-learning-based fraud detection and exception analysis.
* A. Analysis of the full population of existing data. (Correct)
* This approach ensures complete coverage, reduces sampling risk, and detects rare anomalies.
* Modern data analytics tools allow auditors to analyze entire datasets efficiently.
* B. Verification of the completeness and integrity of existing data. (Incorrect)
* While data integrity checks ensure reliable data, they do not actively identify anomalies or suspicious patterns.
* C. Continuous monitoring on a repetitive basis. (Incorrect, but relevant)
* Continuous monitoring is useful for ongoing fraud detection, but it does not guarantee full anomaly detection unless it covers all transactions.
* Full population analysis is more comprehensive for identifying small anomalies.
* D. Analysis of the databases of partners, such as suppliers. (Incorrect)
* While analyzing external data sources can uncover vendor fraud, it does not address internal control or transaction anomalies within the organization.
* IIA GTAG 3 - Continuous Auditing recommends full population analysis as a best practice for anomaly detection.
* IIA Standard 1220 - Due Professional Care requires auditors to use advanced analytical techniques to detect control weaknesses.
* COSO Framework - Fraud Risk Management Guide suggests full transaction data analysis for effective fraud detection.
Explanation of Answer Choices:IIA References:Thus, the correct answer is A. Analysis of the full population of existing data.