Fraud Tip Friday: Small Dollars, Big Problems

Background

Last week, a Transit supervisor and five others were charged in connection with a scheme to steal over $2 million from the agency. The kicker:  the payments were under the limit of $5,000 – roughly speaking, that’s over 400 payments! See Story below.

I thought this was an interesting article to share, because it’s been my experience that some fraud examiners tend to only look at big-ticket items. This case is a reminder to look at the frequency and legitimacy of the payee on smaller dollar amounts as well, since fraudsters may take advantage of materiality or control thresholds set by the company.

Fraud

Fraudulent transactions, by nature, do not occur randomly.

When a fraud examiner receives an allegation or tip, is it appropriate to review every single file and transaction related in any way to the suspect(s)? The answer is sometimes; however, these efforts might not always yield results. With the significant increase in, and globalization of, transactional data, it may be impossible to review every single transaction.

So what can the fraud examiner do?  Use data analytics, sampling techniques, a combination of the two?

Standing Out From The Crowd With Smiling Sphere

Sampling

Some fraud examiners may consider using sampling.  Because the discovery of anomalies has been a very important part of financial statement, internal control, and compliance audits, it is not surprising that sampling has become a standard auditing procedure. It is an effective analysis procedure for finding routine anomalies spread throughout a data set.  In contrast, sampling alone is usually a poor analysis technique when looking for fraud or other unethical behavior, or sometimes that needle in a haystack. If you sample at a 5 percent rate, you effectively take a 95 percent chance that you will miss the few fraudulent transactions! Fraud examiners must take a different approach; they should normally complete full-population analysis to ensure that the “needles” are found.

Fortunately, almost all data in today’s audits and fraud investigations are electronic, or can be converted to an electronic format, so that computers can analyze full populations almost as fast as they can analyze samples. Certainly, some tasks will always require sampling. But the majority of tasks can be analyzed at a full-population level without significant increases in cost or time. When given a task to complete—whether as part of an audit or a full fraud investigation—the benefits and costs of applying data analytics to the full-population should be considered. Given the right tools and techniques, data analytics is often the better option.

Data Analytics and Forensic Data Analytics

The term “Data Analytics” is defined as the ability to collect and use data to generate insights that inform decision-making.  We define “Forensic Data Analytics” in the context of managing fraud and bribery risk as the ability to collect and use electronically stored information, both structured and unstructured data sources, to identify potentially improper payments, patterns of behavior, and trends.

Forensic data analytics encompasses integrating continuous auditing tools, analyzing data in real-time, and allowing for immediate action when suspicious or fraudulent payments are detected.

The Courts

Recent court decisions have looked at the role of statistical sampling as an evidentiary issue, not a per se legal issue. But even as a number of courts have allowed statistical sampling as a form of evidence, they also have provided useful commentary that may guide how parties approach statistical sampling in their cases.

Here are a few cases for consideration:

  • Tyson Foods Inc. v. Bouaphakeo
  • United States ex rel. Ruckh v. Genoa Healthcare
  • United States ex rel. Wall v. Vista Hospice Care, Inc.

CAUTION: A competent defense attorney, at a minimum, will more likely than not point out every flaw in the sample and in the methodology of determining liability  and calculating damages to try to convince the court it is not reliable. They will also point out that you cannot establish falsity through extrapolation – especially in cases where judgment comes into play.

In Conclusion

There were some classic “red flags” in this case. One of the accused was a supervisor; one was former employee of the agency, and shares the same last name with three others, his relatives.

The invoices were for landscaping and maintenance, that is, services, which are more often the item on a fraudulent invoice than are tangible goods.  Did data analytics or sampling catch any of these factors in its nets?

Remember that because of the nature of fraud, sampling alone can undermine an otherwise well-planned fraud investigation.

Kudos to the Transit Internal Auditors for identifying the payments.  However, why did it take three years to investigate? Was that a breakdown of the controls of the controls?

I encourage all of you to stay up to date with current fraud events. While you are reading, always try to analyze the perpetrator(s) and the crime – determine the scheme(s), concealment strategy, and conversion tactics and why the behavior was not deterred or caught sooner. The Advanced Meta Model of Fraud (below) is a great guide.

advanced meta model of fraud

I welcome your thoughts, comments, and suggestions.

Best!

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 Jonathan T. Marks, CPA, CFF, CFE

Story

A NJ Transit facilities supervisor and five others, including a former agency employee, have been charged with running a scheme that siphoned millions from the transit agency, the Essex County Prosecutor’s Office announced Thursday.

Authorities say a facilities superintendent, was approving bill payments, numerous ones, of a few thousand dollars to four companies for services like landscaping and maintenance.

Prosecutors allege the companies that had been set up by five others, including a former NJ Transit bus mechanic, for the sole purpose of collecting the NJ Transit payments.

The submitted and paid bills were fraud, prosecutors say.

The facilities superintendent was receiving kickbacks from the companies, which were controlled by his co-defendants. He’s charged with official misconduct.

Although the individual payments were all less than a $5,000 agency transaction limit, they added up to more up to $2.1 million, prosecutors allege. Authorities did not say how long they believe the scheme had been operating.

In October 2015, internal auditors noticed the payments and reported them to NJ Transit’s Fraud Investigation Unit.

NJ Transit detectives and the Prosecutor’s Office’s official Corruption Unit with leading the investigation.

See original here.

 

Attribution:
ACFE
W. Steve Albrecht
Kevin McColgan, Baker Tilly
Ali Rampurawala, Baker Tilly
Alexa Mahnken, Baker Tilly
Steven Bragg
Healthlawyers.org
North Jersey Record