Which method is commonly employed for analyzing data during an AML audit?

Data analytic techniques are commonly employed during an AML (Anti-Money Laundering) audit due to their effectiveness in processing large volumes of data and identifying patterns that may indicate suspicious activities.

In the context of an AML audit, data analytics allows auditors to apply various statistical and systematic approaches to examine trends, anomalies, and relationships within financial data. This includes the use of algorithms and software tools that can assess transactional data for signs of money laundering, such as unusual transaction sizes, frequency of transactions, or connections between parties that may not be immediately apparent through manual review.

The analytical methods used can also include techniques such as clustering, regression analysis, and anomaly detection, which help in identifying potential risks and non-compliance issues efficiently. By leveraging data analytic techniques, auditors enhance their ability to provide empirical evidence that supports their findings and recommendations. This method contrasts with the other choices that do not provide the same level of objective and quantitative analysis needed in AML audits. For instance, standard deviation calculations and qualitative assessments may contribute to the analysis but do not encompass the breadth of tools and techniques available through data analytics. As such, data analytic techniques represent the most comprehensive and effective approach in the context of AML audits.