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What’s Needed to Succeed?
Reactive fraud prevention will always
be a handicapped method to prevent
losses (and, often, embarrassing public events). Proactive fraud monitoring
using advanced analytics, including big
data, is required to adapt to the growing
threat of fraud.
What exactly is big data?
We define it by the “4 V s ”:
• Volume. Originally described as the
size of data versus processing capability,
volume today is typically measured simply by size of the data alone. This year,
“big” volume might be 25 terabytes (TB);
by next year, 250 TB. For comparison,
it’s estimated that a jet engine in a Boeing plane generates 20 TB of data for
every hour of operation; on one Atlantic
crossing, a four-engine jet can create
640 TB of data.
• Velocity. This is the frequency of
generation and capture of batch, neartime and real-time streams of data. A
world of real-time promotional offers
(where offers are generated at the moment of interaction) requires lightningfast processing and feedback loops so
that things like promotional campaigns
can match geolocations, click streams,
sentiments and purchase histories. For
instance, online-ad technology can operate at 50 to 450 milliseconds (ms) and
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high-frequency stock-trading platforms
operate at less than 60 ms for transatlantic round-trip transactions.
• Variety. Data no longer fits into neat
structures that happily reside in a traditional “database.” The proliferation in the
variety of data sources (radio-frequency
identification, sensors, social networking, mobile devices, etc.) and types (geospatial, etc.) – coupled with traditional
sources (documents, click-stream sets,
etc.) – conspire to generate a veritable
fur ball. Add unstructured data to the mix,
and things get even more complicated.
• Virality. This is the speed at which
data gets spread from person to person,
whether by voice, image or machine. Social networks and the data they generate
have created a new dimension of measurement: “going viral.” The monetization
of data assets is about understanding
factors old and new, and how they work
together – not necessarily about capturing, storing or reporting on every piece
of information passing near the orbit of a
company. It’s about knowing what matters, discarding the rest, and focusing on
the “important bits.”
To come full circle, employing analytics for proactive fraud monitoring requires:
Organizing around the data
Companies often address their big
data challenges and opportunities by
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