editor’s question
Here are several elements that are
required to implement Machine Learningbased
fraud detection at your company:
Big Data store: The first thing you
need is an architecture that can scale
to millions, even billions of data points
over time. A Big Data system should
support large and varied datasets
(both structured and unstructured) and
enable your data analytics to uncover
information, including hidden patterns,
unknown correlations and trends.
Data sources: Your processing engine
should be able to ingest data from all
available sources, including online
and offline, regardless of its format.
More data sources will result in better
correlations, context and insights.
CRAIG COOPER,
COO, GURUCUL
?Criminals and
hackers are already
using advanced
technologies,
including AI, to
harvest information
and perform fraud
at machine-level
speed.
raud is getting
F
hard to detect,
but it occurs
every day
across a variety
of industries,
causing trillions
in losses each year. While financial
services and banking are among the
hardest-hit industries, other frequent
targets include retail, healthcare,
Information Technology, government/
public administration and utilities.
Traditionally, companies have used
legacy fraud management platforms
that have limitations and result in too
many false positive alerts to investigate,
a condition that enables malicious
activities to go undetected. Typically,
these platforms produce evidence of
activity after fraud has taken place,
which is a classic example of too
little, too late.
Recent advances in a range of
technologies from Big Data to Machine
Learning have merged to build new
approaches to fraud analytics. These
can detect anomalous and outlying
behaviours and activities in real time
and provide accurate risk assessments
so that mitigations can be triggered at
machine speed.
Data linkage: The data must be
normalised in some way so it can be
linked to a specific identity. That identity
could be a cashier, a customer service
representative, a customer and so
on. Likewise, the identity could be an
entity, such as a point-of-sale device, a
desktop computer or server. Linkage is
essential to the creation of a baseline
of behaviour for each identity so that
new activities can be compared to the
baseline to look for anomalies.
A Machine Learning model: Once you
have a Big Data store, data sources and
data linkage established, you need to set
up Artificial Intelligence (AI) and Machine
Learning models that can automatically
analyse data feeds, establish baselines
and risk score activity without being
programmed. This process of learning
uses sophisticated algorithms to look
for patterns in data, adjust risk scores
and make better decisions in the future
based on data collected and analysed.
Criminals and hackers are already using
advanced technologies, including AI, to
harvest information and perform fraud
at machine-level speed. To keep pace
with attackers, organisations need to
consider enhancing legacy rules-based
fraud detection with new approaches
that use data science to process
multidimensional sources of information
in ways humans cannot.
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