Professor Elam

 

Two articles by Jo Craven McGinty in December issues of the Wall Street Journal describe one of several data analysis tools used by forensic accountants to catch fraud.
 
When a team of forensic accountants began sifting through refunds issued by a national call center, something didn’t add up:  There were too many fours in the data.  And it was up to the accountants to figure out why.  Until recently, such a subtle anomaly might have slipped by unnoticed.  But with employee fraud costing the country an estimated $300 billion a year, forensic accountants are increasingly wielding mathematical weapons to catch cheats.
 
“The future of forensic accounting lies in data analytics,” said Timothy Hedley, a fraud expert at KPMG, the firm that did the call-center audit.  In the curious case of the call centers, several hundred operators across the country were authorized to issue refunds up to $50; anything larger required the permission of a supervisor.  Each operator had processed more than 10,000 refunds over several years.  With so much money going out the door, there was opportunity for theft, and KPMG decided to check the validity of the payments with a test called Benford’s Law.
 
According to Benford’s Law—named for a Depression-era physicist who calculated the expected frequency of digits in lists of numbers—more numbers start with one than any other digit, followed by those that begin with two, then three and so on.  “The low digits are expected to occur far more frequently than the high digits,” said Mark J. Nigrini, author of Benford’s Law:  Applications for Forensic Accounting, Auditing, and Fraud Detection and an accounting professor at West Virginia University.  “It’s counterintuitive.”
 
Most people expect digits to occur at about the same frequency.  But according to Benford’s Law, ones should account for 30% of leading digits, and each successive number should represent a progressively smaller proportion, with nines coming last, at under 5%.
 
In their call-center probe, Mr. Hedley and his colleagues stripped off the first digits of the refunds issued by each operator, calculated the frequencies and compared them with the expected distribution.  “For certain people answering the phones, the refunds did not follow Benford’s Law,” Mr. Hedley said.  “In the ‘four’ category, there was a huge spike.  It led us to think they were giving out lots of refunds just below the $50 threshold.”  Bingo.
 
The accountants identified a handful of operators—fewer than a dozen—who had issued fraudulent refunds to themselves, friends and family totaling several hundred thousand dollars.  That’s a lot of $40 refunds. But before running the Benford analysis, neither the company nor its auditors had evidence of a problem.
 
Getting the accounting profession to adopt Benford’s Law and similar tests has been a slow process, but Mr. Nigrini has spent two decades inculcating Benford’s Law in the accounting and auditing community, promoting it through articles, books and lectures.
 
Benford’s Law isn’t a magic bullet.  It’s only one approach.  It isn’t appropriate for all data sets.  And when it is a good tool for the job, it simply identifies anomalies in data, which must be explained with further investigation.  In many cases, there are reasonable explanations for incongruities.
 
“I think people use Benford’s Law incorrectly,” said Mark Nigrini, the author of Benford’s Law: Applications for Forensic Accounting, Auditing, and Fraud Detection and an accounting professor at West Virginia University.  “They expect it to be like a lie detector.  Take data and run it against Benford’s Law, and if it doesn’t fit the pattern, it’s automatically fraud.  Nothing could be further from truth.”
 
Benford’s isn’t appropriate for all datasets, and when it is a good tool for the job, it simply identifies anomalies that must then be investigated.  Sometimes, the anomalies do reveal fraud.  But in many cases, there are reasonable explanations for the incongruities.
 
Hill & Ford's team of highly skilled forensic accountants employ a variety of data analysis techniques to review data.  Their experience and training help them to determine if the anomalies found are truly fraud or if there are reasonable explanations.  If you or your client are concerned about a possible fraud situation, contact us at (210)340-8341 to discuss our services.  We’ll help you get to the bottom of the issue.

 

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