An IS auditor is performing a routine procedure to test for the possible existence of fraudulent transactions.
Given there is no reason to suspect the existence of fraudulent transactions, which of the following data analytics techniques should be employed?
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A. B. C. D.C.
When an IS auditor is performing a routine procedure to test for the possible existence of fraudulent transactions, the auditor must be careful to use appropriate data analytics techniques. The choice of technique depends on the auditor's objective, data available, and the auditor's knowledge of the organization's operations.
Given there is no reason to suspect the existence of fraudulent transactions, the auditor should use an analytical technique that is more suitable for general detection of potential fraud. The most appropriate data analytics technique in this case would be C. Anomaly detection analysis.
Anomaly detection analysis is a technique that is commonly used to identify outliers or anomalies in data. This technique is suitable for detecting potential fraud because it can help identify transactions that are significantly different from the expected behavior of the organization.
Association analysis, on the other hand, is a technique used to discover relationships between variables in large datasets. Classification analysis is used to identify the category of an item or transaction, and Regression analysis is used to estimate the relationship between variables. While these techniques may be useful in other situations, they are not specifically designed to detect fraudulent transactions.
In summary, when there is no reason to suspect the existence of fraudulent transactions, the most appropriate data analytics technique to use would be anomaly detection analysis, as it is better suited for general detection of potential fraud.