Ins ide story
Fighting fraud with analytics
A day doesn’t seem to go by without a
new report of fraud via stolen identity and
misappropriated credit card numbers, Internet and phone scams, good, old-fashioned
employee embezzling and officialdom corruption, you name it. Is the world really
crawling with fraudsters? Perhaps so.
According to the Report to the Nations
on Occupational Fraud and Abuse – a
2014 global fraud study – the typical organization loses 5 percent of revenues each
year to fraud, which, if applied to 2013 estimated gross world product, translates to
a potential projected global fraud loss of
nearly $3.7 trillion. That’s some serious
malfeasance. The Report also reports that
22 percent of fraud cases result in losses of
at least $1 million, and many of the victims
– individuals and organizations, large and
small – never fully recover or, in the case
of some companies, go out of business.
Two articles in this issue of Analytics
take a closer look at the enormous worldwide problem of fraud and explain how big
data, analytics and pattern recognition are
effective tools in curbing the $3.7 trillion
crime.
In their article “Employing big data and
analytics to reduce fraud,” Drew Carter
and Stephanie Anderson of AlixPartners
point out that fraud doesn’t play favorites; it’s a multi-industry problem, noting
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that retail, transportation, manufacturing
and telecom are all prone to fraud, along,
of course, with the banking and financial
sectors. Carter and Anderson go on to
spell out the keys for employing analytics
for proactive fraud monitoring.
Warns Carter and Anderson: “Sinister schemes one can’t even imagine are
happening because no one knows to look
for them. Once they are uncovered and
observed, their patterns can be “built into”
rules-engines.”
Meanwhile, in his article “Real-time
fraud detection in the cloud,” Saurabh
Tandon of Mu Sigma explores real-time
fraud detection in the cloud, and how his
company built a fraud detection framework
that had up to 250 unique variables pertaining to the demographic and financial
history of the financial client’s customers.
Writes Tandon: “A cloud-based ecosystem can enable users to build an application that detects, in real time, fraudulent
customers based on their demographic
information and prior financial history.”
Analytics alone can’t stop the worldwide crime spree, but it’s clearly entered
the anti-fraud fight, and more and more
organizations have seen it packs a powerful punch.
– Peter Horner, editor
peter.horner@ mail.informs.org
w w w. i n f o r m s . o r g