Forensics Journal - Stevenson University 2015 | Page 59

FORENSICS JOURNAL

Data Analysis and Reliability in
Predicting Fraud
Joseph T. Harris, CIA, CFE
INTRODUCTION

EXPLORING DATA ANALYSIS

Background
Global Capital Markets rely on truth, accuracy and transparency in
financial reporting. When financial statements are released by public
companies the words and figures tell a story. The story is of particular
interest to major stakeholders such as institutional investors, bankers,
creditors, employees, and regulators. The speed and efficiency of the
financial reporting system in the United States makes it convenient
for stakeholders to sift through financial reports, analyze large
volumes of data, and extract information relevant to their decisions
regarding investment opportunities, emerging industry patterns, or
global challenges impacting a specific country. However, over the
past decade, a serious problem has surfaced: companies engaging in
deceptive financial reporting. Equally disturbing is that, despite the
intense scrutiny of public audits, massive fraud occurs and remains
undetected for periods ranging from years to decades.

Corporate financial reports can portray operational success, but unless
the information is examined carefully it may prove to be an optical
illusion created by highly paid executives as a means of bolstering
their bank accounts. As companies pursue financial statement
fraud schemes, a series of effective data analysis methods may help
stakeholders to curtail these deceptive practices. Data analysis is
defined as “the process of evaluating data using analytical and logical
reasoning to examine each component of the data provided. This
form of analysis is just one of the many steps that must be completed
when conducting a research experiment. Data from various sources is
gathered, reviewed, and then analyzed to form some sort of finding or
conclusion. There are a variety of specific data analysis methods, some
of which include data mining, text analytics, business intelligence,
and data visualizations” (WebFinance). Data analysis is used by
financial professionals, such as public accountants, internal auditors,
securities analysts, bankers and industry regulators, however it is the
type of analysis performed which determines overall effectiveness.
To understand both the power and limitations of data analysis, it
is necessary to identify practical examples where its application has
succeeded in identifying financial reporting anomalies that could
indicate fraudulent activities.

In December 2008, reports of the $65 billion dollar fraud involving
Madoff Investment Securities LLC were front page news (Frontline,
2009). This was considered the longest running Ponzi scheme in U.S.
history. The mastermind behind the fraud, Bernie Madoff, misled
investors for nearly eighteen years, however, it was preceded by the
2001 $74 billion dollar Enron fraud, the 2002 $11 billion dollar
WorldCom fraud, and the 2003 $4.6 billion dollar HealthSouth
fraud. (Crawford, 2005; Freudenheim, 2004; Weld, Bergevin, &
Magrath, 2002)

EXAMPLES
DSI Index
In his internet blog, convicted fraudster and former CFO of Crazy
Eddie, Sam E. Antar, posted public statements about Nu Skin
questioning the company’s growing inventory levels in relation to
its sales trends. (Antar, July 22, 2014) Mr. Antar relied, in part,
on an analysis he performed of the company’s inventory turnover
measurement based on details cleaned from published financial
reports. After posting comments on his blog, he continued following
the company’s financial performance and provided periodic updates.
It is interesting to note the timeline between comments posted by
Mr. Antar and the subsequent public disclosures released by Nu
Skin. On July 22, 2014, an initial observation was made by Sam
Antar regarding the potential buildup of inventory at the company.
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