Industry Insights

Using Data to Deter & Detect Fraud in the Transportation Industry

February 2015
Author:  Jeremy Clopton

Jeremy Clopton

Director

Forensics & Valuation Services

910 E. St. Louis Street, Suite 200
P.O. Box 1190
Springfield, MO 65801-1190 (65806)

Springfield
417.865.8701

Fraud in the transportation industry takes many forms, from asset misappropriation to financial statement fraud. According to the Association of Certified Fraud Examiners (ACFE) 2014 Report to the Nations on Occupational Fraud and Abuse, the average organization loses 5 percent of its revenues to occupational fraud. Factor in waste, abuse and nonoccupational fraud—fraud committed by those outside the organization—and the risk of lost revenue increases even further.

Common Schemes in Transportation

Proper application of data analysis for fraud detection and deterrence requires understanding the various types of schemes committed within the transportation industry. The figure below contains the five most common schemes in the industry, based on ACFE statistics.

Data Analytics as an Anti-Fraud Control

Many organizations wonder what they can do to prevent fraud. After all, a determined employee can be very difficult to stop. It’s unrealistic to think all frauds are preventable, but organizations can almost always do more to reduce fraud risk in key areas.

Based on the ACFE report, the most effective anti-fraud control is “proactive data monitoring and analysis” (data analysis). In fact, data analysis resulted in a near 60 percent reduction in median fraud losses and a 50 percent reduction in median scheme duration in the cases studied. Data analysis also is inherent in two of the most common detection methods:  management review and internal audit. This, combined with the growing volume of data generated by organizations each day, makes data analysis crucial. 

Applying Data Analysis

Using the definitions of the schemes described above, we can identify the largest fraud risks:  vendor management, disbursements, noncash areas (usually inventory and/or fixed assets) and payroll. Some of the most common analysis techniques in these areas include:

  • Vendor Management:  Corruption and billing schemes often occur through the vendor file. Looking for potential related parties or conflicts of interest by comparing the employee and vendor files based on key attributes, e.g., name, address, phone number or taxpayer identification number, may help identify high-risk vendors. Other beneficial analyses include a geospatial analysis of vendors to identify those in residential areas, noting vendors using a mailbox service and identifying vendors without an address, or some variation of “hold for pickup.”
  • Disbursements:  Billing and check tampering are both fraudulent disbursement schemes. In addition, corruption schemes typically involve a disbursement. Perhaps the most effective analysis technique related to disbursements is trend analysis focused on the identification of accelerating patterns of activity. Other common analytics include identifying checks issued on weekends, holidays or in round thousand-dollar increments.
  • Inventory & Fixed Assets:  Detection of noncash schemes through data can be more challenging than schemes involving cash due to the limit on available data for many noncash assets. Some of the most effective techniques involve monitoring inventory levels relative to sales, analyzing inventory shrinkage, testing existence of fixed assets and analyzing expensed fixed assets. The key to incorporating data into an analysis of noncash assets is being proactive in capturing and retaining related data elements.
  • Payroll:  Ghost employee schemes work much the same as a fictitious vendor scheme, beginning with employee setup. Analyzing new employee attributes compared to other active employees may help identify a potential ghost employee. Other useful analyses related to payroll include monitoring employee overtime trends, identifying payroll payments without tax withholdings and analyzing manual payroll checks.

While data analysis is the most effective anti-fraud control, it alone isn’t enough to deter and detect fraud. The most effective fraud prevention program combines multiple elements that, as a whole, create an environment where fraud is less likely to take root. In addition to data analysis, it’s important to explore the many other anti-fraud controls you could implement in your organization. The ACFE report offers information regarding other anti-fraud controls to help deter and detect fraud. As you assess fraud risk in your organization, be sure to evaluate all possible anti-fraud controls. Where possible, incorporate data analysis to increase the effectiveness of your anti-fraud efforts.

If you have any questions regarding data analysis for fraud detection, please contact your BKD advisor.

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