In internal auditing, the use of Computer-Assisted Audit Techniques (CAATs) allows auditors to analyze large datasets efficiently and effectively. When it comes to fraud detection, analyzing the full population of data is often the best approach.
Detailed Explanation:
IIA Practice Guide on CAATs:
CAATs enable auditors to analyze entire datasets rather than relying on samples. This approach is particularly useful in fraud detection, where anomalies or fraudulent transactions may be rare and could be missed if only a sample is analyzed.
Full Population Analysis:
By analyzing the entire dataset, the auditor can identify patterns, anomalies, and outliers that could indicate fraudulent activity. This comprehensive approach increases the likelihood of detecting fraud.
IIA Standard 1220 - Due Professional Care:
This standard requires auditors to exercise due care, which includes considering the use of CAATs for fraud detection to ensure that all relevant data is reviewed, not just a subset.
Why Not Other Options?
Option A (Data should remain segregated): Keeping data segregated may complicate the analysis and hinder the discovery of cross-division anomalies.
Option C (Reactive approach): While tips and whistleblowing are important, a proactive approach using CAATs to analyze full populations is more effective in detecting fraud.
Option D (Random sampling): Sampling may not be sufficient to detect fraud, as it could miss infrequent but significant fraudulent transactions.