Traditional data quality checks often rely on manual sampling, which can miss rare but significant errors. AI- driven audit tools can analyze 100% of a population to " identify outliers " that represent data errors, anomalies, or potential fraud. According to the AAIA™ framework, identifying these outliers is crucial because " dirty data " can fundamentally skew AI model predictions and audit conclusions. By automating the detection of data inconsistencies, auditors can focus their manual efforts on investigating high-risk items, thereby increasing both the accuracy of the audit and the overall reliability of the data used for model training.