Data Analytics Techniques for Mitigating Audit Risk

C.

The primary reason for an IS auditor to use data analytics techniques is to reduce detection risk (C).

Detection risk refers to the risk that auditors will not detect a material misstatement that exists in an assertion. This risk can be reduced by increasing the substantive testing of transactions, accounts, and disclosures. Data analytics is a technique that involves the use of computer software to analyze large volumes of data quickly and effectively. It helps auditors to identify patterns, anomalies, and relationships in the data, which can lead to the detection of fraud, errors, and other irregularities. By reducing detection risk, auditors can increase the reliability of their audit evidence and conclusions.

Technology risk (A) refers to the risk that IT systems or processes will fail to meet their intended objectives. While data analytics can help auditors identify weaknesses or failures in IT systems, reducing technology risk is not its primary purpose.

Inherent risk (B) refers to the risk that exists in an assertion regardless of the effectiveness of the internal control structure. Data analytics can help auditors identify areas of high inherent risk, but reducing inherent risk is not its primary purpose.

Control risk (D) refers to the risk that a material misstatement will not be prevented or detected by the entity's internal controls. While data analytics can help auditors test the effectiveness of internal controls, reducing control risk is not its primary purpose.