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.
