Which method is commonly used by fraud examiners to analyze large amounts of data for irregular patterns?

Data mining is a crucial method utilized by fraud examiners to analyze extensive datasets for unusual or irregular patterns that may indicate fraudulent activity. This technique involves using sophisticated algorithms and statistical tools to sift through large volumes of data. By identifying correlations, trends, and anomalies within the data, fraud examiners can hone in on potential issues that merit further investigation.

The effectiveness of data mining lies in its ability to process complex and vast datasets that would be impractical to examine manually. It enables the detection of subtle patterns that might not be immediately apparent, allowing examiners to uncover hidden fraud schemes or operational inefficiencies.

In contrast, while random sampling, physical inspection, and auditing are valuable techniques in their own right, they do not provide the same level of analytical depth for handling large amounts of data. Random sampling may miss critical anomalies present in the data set, physical inspection is limited to tangible examination and cannot analyze data at scale, and auditing primarily focuses on verifying compliance with regulations and standards rather than pattern recognition within large datasets. Thus, data mining stands out as the most effective method for such analyses within the realm of fraud examination.