1. The Vendor column contains the names of the vendors from which the purchases were made. Use a fuzzy matching...
1. The Vendor column contains the names of the vendors from which the purchases were made. Use a fuzzy matching algorithm to find any vendors with similar names. Do you suspect any purchases were made from phantom vendors?
2. Find any vendors who are charging too much for their product compared with other vendors. In addition to average prices for each product and vendor, do you see any increasing trends that might indicate kickbacks?
3. Calculate the average product price paid by purchaser. For example, calculate the average price paid for "All Purpose Wipers" when Jose, Sally, and Daniel are purchasing. Compare these average prices. Do you see any issues to search further?
4. Verify that all purchases are included in the data set. If a purchase was left out , its ID would be removed from the sequential list of IDs. Compare each ID and ensure the column increases by one in each record.
5. Verify the values in the Quantity and Total columns. Are any missing or abnormal values present?
6. Analyze the Product Price column from each company using Benford's Law. Analyze only the first digit of the column. On average, do any of the vendors stand out? In other words, are the transactions from any vendor not matching Benford's Law?
You have recently joined the internal audit team at a large company responsible for janitorial work at many different local businesses. Because of the significant number of consumables used in janitorial work, your company has a large purchasing department. You have been asked to analyze the purchases data set for potential frauds. Download the ch06_janitorial_purchases.csv data set from the book' s Web site and import it into an analysis software package.
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