Auditors can use data analytics to avoid the massive waste spending that often goes hand-in-hand with hiring outside vendors and contractors. This technique doesn’t require overly complicated or expensive software.

Data analytics can be used to monitor nearly any contractor or vendor in any industry, but they are particularly useful for auditing high-risk contracts in which vendors and contractors bill based on cost plus fee, time and materials, or time and expense arrangements. Three major areas are especially critical to audit when using data analytics: labor, equipment and expenses. 

When it comes to applying data analytics to labor charges, you need the following four sets of electronic data on hand:

1. The original contract, including the list of personnel staffed along with their individual job classification, labor rate and burden
2. All invoices showing what was billed to the company during the contract period
3. The vendor's job cost data indicating their incurred costs for the project
4. The payroll files indicating the names of each worker and the amount paid out to them, including fringes and benefits

In order to validate equipment charges, auditors need to have a list of the following:

1. All the equipment used
2. Each equipment identification number
3. Whether the equipment was purchased or rented
4. How it was charged to the company (hourly, daily, weekly or monthly) and at what rate
5. If there was an operator associated with it
6. How long the equipment was used
7. Whether it was located on-site or off-site
8. Whether the equipment consumed fuel (If so, how much?)

Auditors can easily use data analytics to identify many types of billing errors on expenses, including:

1. Accruals, chargebacks, adjustments and intercompany transfers
2. Vendor bonuses charged back to the company
3. Incentives charged to the company that were not earned
4. Certifications, licenses, repairs and insurance claims charged to the company and not the vendor
5. Unusual expenses that a vendor may charge back to a client, such as personal items, gifts, incentive events and team-building outings

The process of applying data mining and analytics does come with challenges, especially if you don't have all the data mentioned above. 

You can read more on this topic in our Achieving a Robust Data Analytics Program Guide and by exploring these related tools on KnowledgeLeader:

• Six Elements of Infrastructure for Data Analytics
• Customer Credit Data Analytics Audit Work Program
• Payroll Cycle Data Analytics Audit Work Program