Which of the following is the MOST effective way to identify anomalous transactions when performing a payroll fraud audit?
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A. B. C. D.B.
When performing a payroll fraud audit, it is important to identify any anomalous transactions that may indicate fraudulent activity. The most effective way to do this is through data analytics on payroll data.
Data analytics involves using specialized software to analyze large datasets, including payroll data, in order to identify patterns and trends that may be indicative of fraud. By analyzing payroll data, auditors can identify anomalous transactions that may indicate fraud, such as employees who are being paid more than their agreed-upon salary or employees who are receiving payments for work that they did not perform.
Substantive testing of payroll files may also be effective in identifying anomalies, but it can be time-consuming and may not be as effective as data analytics. Substantive testing involves reviewing payroll files in detail to identify any discrepancies or errors. This can be useful in identifying specific instances of fraud, but it may not be as effective in identifying patterns or trends.
Observation of payment processing and sample-based review of pay stubs are both useful tools in detecting fraud, but they may not be as effective as data analytics. Observation of payment processing involves observing the process of making payroll payments in order to identify any irregularities or inconsistencies. Sample-based review of pay stubs involves reviewing a sample of pay stubs in order to identify any discrepancies or errors.
Overall, data analytics on payroll data is the most effective way to identify anomalous transactions when performing a payroll fraud audit, as it allows auditors to quickly and accurately identify patterns and trends that may be indicative of fraud.