What types of exceptions might be found during the sample testing phase of an AML audit?

During the sample testing phase of an AML audit, encountering erroneous data and data processing issues is a common situation. This phase involves analyzing selected data samples to ensure compliance with Anti-Money Laundering regulations and assessing the effectiveness of the financial institution's controls. Erroneous data can come from various sources, including human error during data entry, system malfunctions, or incorrect processing of transactions. Data processing issues might include flaws in the algorithms that flag suspicious transactions or inconsistencies in how data is aggregated and reported.

The presence of these types of exceptions signifies the integrity and reliability of the data being analyzed, which is crucial for identifying potential money laundering activities. Identifying these errors early allows for corrective actions to be taken, ensuring that the organization is adhering to regulatory requirements and maintaining robust compliance measures.

Other options focus on issues that pertain to broader operational or systemic problems within the organization rather than the specifics of the sample testing process, making them less relevant to the exceptions one might specifically find during the focused analysis of data.