Question

what type of sampling/analytics techniques could have been used by the auditors to discover the fraud? list three techniques that the auditors should have used and explain how they would have helped.

Answer 1

Auditors could use sampling and analytics techniques like Random Sampling, Benford's Law, and Data Mining to detect fraud. These methods help in identifying unusual transactions, anomalies, and patterns indicating potential fraudulent activity.

Step-by-step explanation:

To discover fraud, auditors can employ various sampling and analytics techniques. These techniques are designed to detect irregularities in financial data that may indicate fraudulent activities. Here are three such techniques that auditors should have used:

1. Random Sampling: This technique involves selecting a random subset of data from the larger dataset. Auditors can use this approach to ensure each entry has an equal chance of being examined, reducing the risk of bias. It's particularly effective in high-volume environments where reviewing every transaction is impractical. Random sampling could help identify unusual transactions that warrant a closer look.
2. Benford's Law: This statistical analysis technique is based on the frequency distribution of leading digits in numerical data. Benford's Law states that in many naturally occurring collections of numbers, the leading digit is likely to be small. Auditors can analyze the leading digits of financial figures to detect anomalies that deviate from Benford's distribution, which might suggest manipulation or fraud.
3. Data Mining: Data mining involves using software to search for patterns in large datasets that could indicate fraudulent behavior. Techniques such as cluster analysis, anomaly detection, and regression analysis help auditors identify outliers, suspicious patterns, or relationships between variables that are unexpected. With high-powered analytics, auditors can sift through mountains of data quickly and efficiently to uncover signs of fraud.

Employing these techniques would likely enhance auditors' ability to detect fraudulent activities by analyzing more extensive and complex datasets in a detailed and sophisticated manner. By combining these approaches, auditors can triangulate data and identify fraud with a higher degree of confidence.