PCM Vol.2 - Issue 6

Thought Leaders Corner
Fighting fraud with Bayesian analytics
Effective fraud detection has many forms,
from traditional rules-based approaches to
sophisticated analytical models and machine
learning.
out from the crowd in its machine learning
capabilities – the system can learn rapidly as
frauds are tagged, constantly learning and selfcalibrating
as fraud trends change. This means
that fraud teams know that they are in the best
possible position to identify and stop fraud when
it happens.
Within the industry, the most widely used
analytical approach is to build Neural
Network models using historic
consortium data to build up a
set of patterns to later use in
detection of live card fraud.
This approach has been used
for many years with varying
success. However, recently
new approaches have become
increasingly popular and have
begun to overtake the results
being achieved with traditional
modelling techniques.
NCR has pioneered a modern
approach of fraud analytics,
choosing to base its models
on a more flexible Bayesian
analytical technique. This
methodology enables fraud
teams to build models faster
than it is traditionally possible,
while achieving extremely
good detection rates at low
false positives.
About NCR
easier.
The models form parts of the
Fractals Adaptive Classification
Engine, which provides automated, intelligent
fraud detection using a combination of Bayesian
statistical analysis and proprietary inference
techniques. By applying mathematical models to
each incoming transaction, the system identifies
suspicious activity, calculates a probability-based
fraud score that indicates the likelihood of fraud
and then triggers an action.
NCR Corporation (NYSE: NCR) is the global
leader in consumer transaction technologies,
turning everyday interactions with
businesses into exceptional experiences.
With its software, hardware, and portfolio of
services, NCR enables more than 550 million
transactions daily across retail, financial,
travel, hospitality, telecom and technology,
and small business. NCR solutions run the
everyday transactions that make your life
Unlike a Neural Network, which is nothing more
than a “black box” that produces a score,
Bayesian models produce scores that
come with a clear set of reasons
and probabilities. This “white box”
approach gives the end user much
more granular information to work
with when assessing a transaction
or speaking with a customer and, as
we know, more information means
better customer service - the key to
successful fraud operations.
Based on the risk score, pre-defined
actions are triggered and the
transactions are approved, rejected
or referred. Custom rules can then
be set for referred transactions. If
an unusually large transaction is
detected for a customer profile, the
bank can inform the customer via
SMS or e-mail about the suspicious
transaction and get confirmation
that the payment is authorized.
Comparative tests have shown that
the Bayesian models outperform
industry standard solutions
in detection rates and cause
significantly fewer false positives. Enhanced
with self-learning functions such models that
can automatically adjust to new fraud scenarios.
Fractals scores transactions in real time and can
decline a transaction during the authorization
request.
The future of fraud
The model is initially set and tuned using financial
institution’s recent historical data and fraud tags
so that it can identify the unique fraud patterns
facing each unique organisation. The result is
a bespoke and highly accurate fraud detection
model that can be set up in just a few short
weeks.
Each model in the Adaptive Classification Engine
is based on a series of mathematical algorithms
that are targeted to identify specific fraud
patterns or irregularities in the account’s holder
behaviour. However, the system really stands
The truth is that fraud isn’t going to disappear any
time soon – criminals have got too much to lose.
There will always be new breaches or techniques
to get past even the strongest security and fraud
systems. And, unfortunately, there is no solution
which is the answer to every problem.
However, a fraud system that can assess a
transaction in real time, based on a comprehensive
understanding of customers’ historic behaviour
and intelligent analysis compared to the latest
fraud trends, will arm your fraud team to stop
the highest possible levels of fraud.
005