PCM Vol.2 - Issue 6

Thought Leaders Corner
How is machine learning helping to tackle online fraud?
It’s widely known in the financial services sector that
criminals look for the weakest link in transactional
systems for attempting fraud attacks. I was at the centre
of UK banking when we rolled out Chip-and-PIN in the
mid-2000s and saw the impact of this change on online
fraud. With EMV rolling-out to the US last year, we’re
already seeing online fraud spike – by 11% according
to PYMNTS.com – as criminals perceive this channel
as offering the biggest fraud ‘rewards’.To try and beat
the criminals, financial services institutions need to
understand all the data they’re collecting about their
individual customers – and use this data for informed
business decision-making. One term that we’re hearing
used a lot to tackle analysing big data is ‘machine
learning’. So what exactly is machine learning, and how
is it being used to tackle online fraud?
What is machine learning?
In many industries – banking and payments included
– machine learning has become a popular term. We’re
particularly hearing it a lot in business areas that are already
using advanced analytics methods to understand customer
and business data.
The promise of machine learning is that it can automate
analysis processes – making it easier for financial services
institutions to make decisions about their customers faster,
and with more accuracy, than relying on human analysis alone.
The kicker with machine learning is that the criminals are as
sophisticated in these techniques as those of us who are trying
to fight them off. It’s really tough to stay ahead, and we know
that criminals look for the easiest targets when committing
fraud. We need to beat them at their own game.
Advanced computer science
Machine learning is an area of computer science that studies
how to make computers learn and improve with experience,
with minimal human intervention. For example, being able
to identify trends or patterns in large, complex quantities of
data, with speed and accuracy – and then automatically adapt
statistical models to incorporate these patterns and trends.
This process enables computers to find insights from the data
without being told where to look – this is what makes machine
learning a branch of artificial intelligence.
Fraud detection – detecting anomalies with machine
learning
So what does this mean for fraud detection and customer
management? Well, the flip side of spotting and understanding
data trends at high speed is that machine learning systems
can also accurately and efficiently detect anomalies in these
trends.
This is crucial for fraud prevention. Human analysts are
capable of looking at data to spot trends and anomalies, but
do so very slowly. It takes us a long time to see the important
changes in the data, and manually make adjustments to the
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