figh tin g frau d
That, in turn, determines the kinds of
data required and the kinds of analysis
needed to find the insights lurking in the
data. For anti-fraud efforts, the business
can guide data needs by identifying:
• Already-known fraud scenarios –
this will provide an initial data set to begin monitoring. It will also provide a basis
for monitoring algorithms.
• Building up sensitivities to unknown
scenarios – while, of course, unknown
risks are by definition unknown, companies can identify areas where the effect
of fraud would be especially negative,
such as an increase in product prices
paid by certain customers, which may indicate procurement kickbacks or provide
funding for covering up other undesirable
behaviors, such as bribing government
officials to obtain government contracts,
permits and licensing or to overlook illegal or non-compliant activities.
Data analytics can help in monitoring
these scenarios once desired business
processes are defined and reporting
dashboards are developed.
Sophisticated Analytics
Analytics, in this environment, does
not mean just a spreadsheet. It means
such things as advanced methods of
pattern identification, to be designed and
operated by experienced analytics and
fraud professionals. Pattern recognition
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is a science of its own, but it is hardly new.
For instance, the Fibonacci sequence
was made famous by Italian mathematician Leonardo Bonacci, aka “Fibonacci,”
in his 1202 book, “Liber Abaci.”
Advanced practitioners today are using pattern recognition methods to establish relationships in fields as diverse as
baseball and healthcare. Analytics have
even reached the level of sophistication to create original works of art. Dave
Cope, a musician and computer scientist,
has developed a program called “Emily
Howell” that can create original works
of music seen by many critics as being
on par with that of the world’s greatest
musicians.
When tackled by experienced professionals, these efforts should deliver:
• Accurate insights – the “confusion
matrix” is a standard tool to measure accuracy. It is used to identify type 1 (I said
you were a safe transaction, but you were
actually fraudulent) and type 2 (I said you
were fraudulent, but you were actually a
safe transaction) errors. The best monitoring provides a balance of missing only
a few bad scenarios, but not calling too
many scenarios into question.
• Timely insights – as required by the
nature of the business.
• Simple access – delivery of fraud
warnings in clear, easy-to-understand
language and processes.
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