Accounting Fraud Detection Discussion Questions


In Chapter 6, we explored the concept of data-driven fraud detection, which is a more proactive approach to fighting fraud and detecting fraud earlier. To help develop an appreciation of this approach and to gain exposure to some of the tools that enable this approach, here are several exercises that will allow you to experiment and explore these tools. For these exercises, you must  use IDEA, which is also being introduced this week for you to learn and use in your Group Project Case Study (for more information about IDEA, see the Group Project Case Study Guidelines and the invitation to the IDEA software and workbook.
Please complete and submit the following exercises:
Chapter 6, Case Studies 2 

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Case Study 2
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.
♡ Questions
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
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?

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