PCM Vol.2 - Issue 6

PCM
YOUR GATEWAY TO THE WORLD OF PAYMENTS
Vol 2. Issue 6
June 2016
The Balancing act
Reduce Friction &
Eliminate Fraud

Welcome to Vol.2 - issue 6
We are enthusiastic to release the 6th Issue of the Payments & Cards Network
eMagazine for 2016. Following our last issue, this month we focus on the way realtime
data can be utilized to reduce friction and eliminate fraud in the Payments
industry and further. In light of this, we present an Q&A session with Armen Najarian,
CMO at ThreatMetrix, who talks about the digital transformation in the financial
services industry.
In the Startup Spotlight we introduce you to Risk Ident, a software provider offering
anti-fraud solutions within the e-commerce, telecommunication and financial sector.
Roberto Giorgio Valerio, CEO at Risk Ident, sheds light on his company’s innovative
approach to tackle fraud, which goes beyond having advanced machine learning
components.
To provide a complete picture of the digital transformation in the financial service
industry, Luke Reynolds, Director of Fraud at FeatureSpace, clarifies what machine
learning is and how it can exactly be used to tackle online fraud.
Carrying on we feature the Michel Splichal, MRC US Program Manager, who gives
advice on how risk management departments can foster an excellent customer
experience. And on top of this Jose Gonzales-Alonso, Head of European professional
services for Payments solutions at NCR, explains the advantages of using Bayesian
analytics to fight fraud more effectively.
Finally, an overview of the hottest job openings we have at the moment. Feel like you
need a change or looking for a job opportunity? Get in touch directly by clicking on
the jobs. For the ones who like to network or simply want to stay up-to-date with their
peers - check out our premium event partners and make use of the discounts we
have on offer before they run out!
For any questions, suggestions, or concerns, please address them to the editors:
Amir Abdin - amir@paymentsandcardsnetwork.com
Duc Dang - duc@paymentsandcardsnetwork.com
Joanna Bak - joanna@paymentsandcardsnetwork.com
The Payments & Cards Network team wishes you good reading!
002

Contents
thoughtleaders
spotlight
7 16
10
13
STORIES
4
7
10
13
Fractals - The future of fraud detection
Jose Gonzales-Alonso dives deep into new approaches and techniques of
fraud detection.
How is Machine Learning helping to tackle online
fraud
Luke Reynolds explains what the popular term of machine learning and how it
can be utilized to fight fraud.
Ways for Risk Management team to provide a
winnnig customer experience
Michel Splichal shares a number of strategies a risk department can
implement to sustain a excellent customer experience.
Understanding the total costs of friction
Armen Najarian highlights the importance of preserving the overall customer
experience while improving to detect & fight fraud.
16
19
20
Spotlight: Risk Ident
We speak to Risk Ident, a very innovative startup taking
fraud prevention to the next level.
Hot Jobs
Hottest jobs in the industry! Get in touch directly to get
more insight.
Events
Here we showcase the most exciting upcoming events
related to FinTech, Payments and Cards throughout
the world!
003

Thought Leaders Corner
by Jose Gonzalez-Alonso
Fractals - The Future of Fraud Detection
Fraud exists in many forms across the financial
industry and its losses are not getting any smaller.
Figures from the Nilson Report revealed that
in 2014 the global loss of payment card fraud
reached over $16 billion which is an increase of
19% in comparison to the previous year, 2013 .
Criminals operate in highly organised gangs,
targeting the weakest links in the payments
“chain”. It may be a data breach, skimming
devices, phone scams, fake websites, intercepting
mail, phishing online bank accounts or hacking.
There are many, ever-changing, ways to get past
the security.
At the same time as criminals have changed their
methods and techniques, the fraud detection
technology also has evolved. Nowadays, financial
institutions have more tools and techniques
available to fight fraud. For instance big data
analytics, which enables analysing huge data sets
and identify anything out of the ordinary. Despite
this fact, fraud is not going away.
The customer at the heart of fraud detection
It is vital for financial institutions to accurately
identify and proactively stop fraudulent
transactions in order to protect their customers
and keep the losses to a minimum. After all,
financial institutions are the main defender of
the customers and most clients expect their
banks to track and stop fraud. But things aren’t
as simple as that. Today’s customers also expect
their banks to always get it right, meaning that
the customers have a low tolerance for a “false
positive” situation in which the financial institution
blocks a genuine transaction in the mistaken
belief that it is fraudulent.
This means that banks have to find the perfect
balance between a system that is so “strict” that
it blocks genuine transactions, having a negative
impact on customer experience, and taking an
approach that is more passive and that might
overlook genuine fraud.
The answer lies in a combination of in-depth
knowledge of the customer and a fraud detection
system that is intelligent enough to learn and
adapt as fraud changes.
It is also crucial to know the customer well enough
to immediately see if any of their activity is outside
of their normal behaviour. This means that the
financial institutions need to look at the full
enterprise view of each individual to understand
their historic activity across all channels (both
financial and non-financial), taking into account
the geographic and demographic profile as well
as other influencers such as time of year.
Fraud teams also need to consider factors such
as channel. Figures from LexisNexis suggest that
in 2014 m-commerce accounted more than one
fifth of fraudulent transactions in the US, despite
only making up 14% of transaction volumes.
However, a transaction between a customer with
an IP address that you know and a regularly used
online merchant is presumably genuine.
004

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

ALWAYS ONE STEP
AHEAD OF FRAUDSTERS ...
Risk Ident is a software development company that offers
anti-fraud solutions to European companies within the
e-commerce, telecommunication and financial sector. We are
experts in data analytics and machine learning. Let us show
you how to improve your fraud prevention step by step!
www.riskident.com

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
007

Thought Leaders Corner
Merchants also feel the impact of these inefficient fraud
protection methods. They get hit hard by the resulting
chargeback costs and fraud losses – not to mention to loss of
revenue from declined transactions (false positives).
Blocking genuine customers can’t be a cost of stopping
fraud
When good customers are blocked in an attempt to catch
fraud it’s a problem for both customers and merchants.
Recent research from MasterCard revealed that it’s often the
most affluent customers that get blocked. For example, those
who are travelling more frequently or making purchases with
a larger transaction value. If these customers get blocked by
outdated, inefficient fraud systems, there is a significant loss
of both revenue and reputation for the financial services
provider, and their merchants.
Luke Reynolds,
Director of Fraud at Featurespace
Luke is responsible for Featurespace’s fraud clients in
Financial Services and Insurance. Prior to Featurespace,
Luke worked in the Financial Services sector for 20 years,
including as Callcredit’s Commercial Director of Fraud and
ID, a variety of roles in Lloyds Banking Group, the UK Card
Association and NatWest.
systems that rely on this data.
Machine learning systems can do this much faster – looking
at complex data sets and offering accurate predictions
on behaviour, and a deep understanding of, for example,
customer behaviour when making transactions online.
Changing payments push-up online fraud
Understanding individual behaviour patterns – and detecting
anomalies – is especially important in the payments world,
where processes are changing fast and protecting merchants
and customers involves analysing vast amounts of data.
As soon as financial institutions and their merchants get up to
speed with a payments change – such as Chip technology or
contactless cards – another change seems to be coming.
Reducing customer friction: advanced fraud management
needed
So, it’s clear that financial institutions are under pressure to be
providing a seamless, secure experience. Modern customers
want fast, efficient methods of payment and don’t want to be
blocked when making a genuine transaction.
This brings us back to the promise of machine learning.
Organisations need fraud systems that can interpret vast
volumes and complex aspects of customer behaviour –
quickly and efficiently – while reducing customer friction with
a seamless experience.
What I’ve learnt from over 20 years battling fraud is that
financial institutions need fraud systems which keep one step
ahead by understanding every individual customer’s behaviour
in real-time – protecting them from fraud, while providing a
frictionless experience. This is where machine learning steps
in.
How are sophisticated machine learning systems better
than humans?
Preventing fraud with advanced deep machine learning
systems is enabling organisations to balance robust fraud
Don’t let fraud prevention drop in priority
Having worked for over twenty years’ in risk and security in
the banking sector, it’s been my experience that as financial
institutions and their merchants get to grips with new payment
processes, fraud prevention drops down the priority list.
This means that changes to payments ultimately negatively
impact the customer. What should be a positive move
for convenience, in reality causes a knock-on impact on
increasing the risk of fraud attack. To try and counter this risk,
organisations often increase the dial on their fraud protection
limits – meaning more genuine customers feel the friction of
having transactions incorrectly blocked in an attempt to catch
fraud.
008

Thought Leaders Corner
checks with a frictionless customer experience. Machine
learning systems automate the process of viewing events
in context, building a deep understanding of every single
customer. By monitoring every event and transaction taking
place in real-time and from multiple channels, fraud attacks
stand out and genuine customers are easy to recognise – all
from an automated fraud prevention system. It’s an approach
being adopted by TSYS, the largest payment processor in
the United States. TSYS wanted to strengthen its position in
faster payments using machine learning to provide clients
with actionable insights in real-time. They’re implementing
an advanced machine learning engine to protect their clients
from fraud while providing a seamless customer experience.
capability to predict behaviour one step before the criminals
think of new ways to behave.
It’s up to organisations to embrace new fraud systems to gain
a vital competitive edge in protecting their customers and
their reputations.
Staying one step ahead
Advanced machine learning systems are the way forward
for financial institutions to stay ahead of the criminals. It’s
important to take action now to reduce fraud while providing
frictionless customer experience and protecting revenue.
Fraud attacks globally are going to increase online and via
mobile channels – you don’t need a computer science degree
or a PhD in machine learning or statistics to predict that.
My advice to companies tackling fraud is to make sure that
your fraud prevention software is built by guys who have the
Featurespace
www.featurespace.co.uk
Featurespace is the world-leader in Adaptive Behavioural Analytics and creator of the ARIC engine, a
machine learning software platform developed out of Cambridge University, which understands individual
behaviours in real-time for enhanced fraud detection decision-making capabilities. Customers include
TSYS, Callcredit, Betfair, KPMG, Vocalink/Zapp, Camelot, and William Hill.
009

Thought Leaders Corner
Ways for Risk Management Departments to
Provide a Winning Customer Experience
by Mike Splichal, MRC US Program Manager
It is generally calculated that businesses
must spend between four and ten times
more to acquire a new customer than
to keep an existing one [1]. With that
in mind, we would like to share several
strategies risk departments can employ
to help deliver a strong customer
experience, an important component of
customer loyalty.
Remove direct links from the body
of your department's emails -- If
customers need to take a particular
action, such as resetting their password,
or should visit a specific resource, such
as a help page, give customers the
steps or the path instead of providing
a direct link. Much has been done to try
to educate consumers on the peril of
clicking URLs in emails; including links
can become both a customer trust issue
and drive additional contacts to your
department -- or to Customer Service
-- questioning the validity of the email.
Involve your social media points of
contact -- More and more customers
are choosing to interact with companies
using social media. Whether Customer
Service, Marketing or another team
monitors your company’s social media
accounts, ensure the relevant POCs
know who to engage for any fraud,
risk or security issues received via
Facebook, Twitter or other channels. If
your company does not already have
an internally accessible contact matrix,
creating one and reviewing it at least
quarterly is a good place to start.
Strengthen your CS relationship
-- Meet periodically with appropriate
personnel in Customer Service
leadership to discuss any important
fraud/risk trends, projects or events
which may lead to CS contacts. Also,
coordinate with CS on regular updates
to their account security training so
CS associates keep current on topics
related to fraud and risk, such as social
engineering.
Keep communication concise and
personalized -- Unless the customer's
scenario involves a legal issue, risk
investigators should refrain from using
generic templates that come across
as "blurbs" or contain extraneous
information irrelevant to the situation at
010

Thought Leaders Corner
Work towards a continuous
feedback loop on rules/models -- In
partnership with the analysts, data
scientists or solution provider(s) who
maintain your rule sets and/or machine
learning models, continually assess
false positives and dive deep at the
first sign of an anomaly. Transactions
inappropriately queued for manual
review can delay orders unnecessarily;
transactions cancelled erroneously are
especially problematic and may result
in an escalated customer service case.
Operational input can play an important
role in fine-tuning rules and models,
leaving good transactions out of manual
review queues and letting investigators
focus on those transactions which truly
are risky.
Network with other risk professionals
-- By joining a professional organization
like the Merchant Risk Council, key
fraud and payments personnel
can gain valuable insights, discuss
emergent threats and trends, and
share best practices with other industry
professionals. The MRC offers numerous
opportunities to connect and learn: in
person, via four annual conferences
and multiple smaller networking
events; and virtually, through MRC
Communities, a portal which enables
quick communication and timely
feedback between fraud and payments
professionals around the globe.
Conclusion
Mike Splichal,
MRC US Program Manager
Mike coordinates content for committees,
presentation archives and community forums.
He also develops member training and
certification programs. and NatWest.
hand. Your customers' time is valuable, so
limit emails to two or three paragraphs if
possible, and consider using help pages
to display more detailed information, if
needed, about policies and procedures.
Shorter emails also display more easily
on mobile devices, perfect for customers
on the go.
Implement customer-friendly
technology -- Two-factor authentication,
where a one-time use code is sent to
the consumer’s cell phone via SMS or
a special app, is an effective way to
help protect customer accounts and
confirm legitimacy. Enhancing mobile
apps to permit customers to log in
using fingerprints or facial recognition is
another way to provide a simple, secure
means of authenticating users.
While preventing illegitimate transactions
from being completed is a critical part of
a risk department’s mission, it is equally
important that legitimate customers
have a safe shopping experience with
as little friction as possible. We believe
increased focus on good customers is
a winning strategy for any eCommerce
business, and will pay handsome
dividends through increased loyalty and
sales in the years ahead.
Sources:
1 = Kingwill, Ian (2015). What is the Cost of Customer Acquisition vs Customer Retention? Retrieved from: https://www.linkedin.com/pulse/
what-cost-customer-acquisition-vs-retention-ian-kingwillwhat-cost-customer-acquisition-vs-retention-ian-kingwill
About MRC:
The MRC is an unbiased global trade association providing a
platform for eCommerce fraud and payments professionals
to come together and share information. As a not-for-profit
entity, the MRC’s vision is to make commerce safe and
profitable everywhere by offering proprietary education,
training and networking as well as a forum for timely and
relevant discussions.
Find more information and visit:
http://www.merchantriskcouncil.org
011

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CONNECTING YOU WITH THE PEOPLE TO POWER YOUR BUSINESS EFFICIENCY
CONTACT US NOW
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Digital Source | Herengracht 576 | 1017 CJ | Amsterdam | The Neterlands | +31 (0) 202 373 639

expert interview
Understanding the
Total Cost of Friction
Armen leads worldwide marketing strategy and
execution for ThreatMetrix. Previously, he directed
the go-to-market strategy for IBM’s $1B portfolio of
100+ SaaS solutions. Armen joined IBM through
the $440M acquisition of DemandTec, where as VP
of Corporate Marketing he built a modern demand
generation engine and repositioned the business
supporting a 3x increase in revenue over a 5 year
span.
Armen Najarian
CMO, ThreatMetrix
Tech-savvy millenials are
becoming the predominant
demographic as customers as
well as employees. We talk to
Armen Najarian from ThreatMetrix,
about the right balance of remaining the
competitive with new tech innovation
whilst not compromising the customer
experience.
PCN: When you talk about ‘Digital
Transformation’ in the Financial
Services industry, what does that mean
to you?
AN: Digital Transformation as it relates
to fraud and security breaches is no
longer just an IT issue, it’s a business
issue. Banks don’t typically measure
the cost of friction but the tangible
losses are in the billions. Banking and
Commerce transactions are increasingly
done online or via mobile devices and
we’re putting our personal information
out there at an accelerated rate. This
creates greater opportunity for fraud
and security breaches.
When fraud occurs two things happen: 1.
the industry responds by implementing
stricter authentication measures -
passwords, captchas, verification
codes - that end up hampering the
user experience, and 2. customers lose
trust. Adding more hoops for users to
jump through degrades their experience
enough that they can be willing to walk
away - creating financial loss. On the same
hand, loss of customer trust means that
they won’t be recommending services to
friends and family, and that also results
in financial loss. Digital transformation
effectively addresses business concerns
and IT concerns, minimizing customer
friction while tackling fraud.
PCN: In March, 2016, First Annapolis
conducted a study* on behalf of
ThreatMetrix on controlling friction
while tackling cybercrime. What
surprised you most about the data?
AN: There were a couple of things. The
sheer volume of individuals using digital
means for important transactions, and
the high cost of loss due to fraud and
friction. In our study, 38% of people
reported experience with banking and
payments fraud within a 3-year period.
66% had their payment card information
compromised, while 45% said their
information was used to make online
or mobile purchases. Because of fraud,
90% took actions to secure their account
and 34% changed their behavior, but
10% actually left their bank. Of the
demographics, millennials were more
inclined to take action as a result of real
or perceived problems.
Let’s talk about that 10%. Assume that
of 215.1MM banked consumers, 9%
experienced fraud in the last 12 months.
That’s 19.4MM fraud victims, of which,
the 10% that left/will leave amounts to
around 1.9MM relationships lost. No
matter what the dollar value of those
relationships, that’s a staggering loss.
PCN: As a consumer, wouldn’t you
rather go through challenges than risk
compromising your identity and data?
AN: The Catch-22 for the industry is that
the cost of fraud and fraud prevention
is significant, but so is the cost of
friction created by customer-facing
step-up challenges. The study shows
that fraud and the customer response
to it don’t vary much by geography
or demographics. The majority of
013

individuals don’t perceive mobile
transactions to be as safe as in-person
or even online transactions regardless
of increasing security barriers. That’s
important information for the banking
and ecommerce.
The traditional method of adding
increasing measures of security, i.e., stepup
challenges, puts the burden back on
consumers and introduces a potential
point of failure in the relationship. 83%
of our study respondents said that they
had experienced step-up challenges in
the past year, with nearly 50% saying it
happens frequently.
PCN: Of those 83%, what was their
perception of the customer experience?
AN: Consumers left their banks because
the experience was too irritating.
Forgetting usernames or passwords,
getting locked out of their accounts...this
is frustrating to consumers, especially
on a mobile device where there is
an expectation of immediacy and
convenience. Increased authentication
methods can lead to increased customer
complaints, customer servicing costs,
decreased account revenue if they leave,
and lost customer relationships that cost
the financial services industry billions of
dollars.** It’s not enough to push the
problem back on the consumers and
there are consequences to that.
PCN: Fraud and security challenges will
likely escalate, how can the financial
services industry keep up with the
costs to themselves as well as their
consumers?
AN: The financial services industry
needs to invest in both preventing fraud
and preventing the friction that comes
with the consumer-facing prevention
techniques. In countries such as Europe,
where EMV has long been in place,
there are still significant rates of fraud
and loss of consumer confidence. This
finding underscores the fact that EMV
isn’t the answer to all security and fraud
challenges and step-up challenges are
creating greater friction.
This is what digital transformation is all
about. Security and a positive digital
Expert Interview
experience are not mutually exclusive.
It’s important to take a holistic approach
to digital security. The net is that the
financial industry has to do better to
tackle ongoing threats while preserving
the customer experience.
*First Annapolis conducted a multi-market consumer survey
based on a sample of 3,090
consumers from the U.S., U.K., and Australia
**Of the 83% experiencing step-up challenges, 3% will likely
leave their bank as
a result of friction. This equates to 3.9MM Relationships lost
at an approximation of $2,533 Est. revenue remaining per
relationship, or $10.0 Bn Lost Relationship Value
ThreatMetrix®, The Digital Identity Company, is the market-leading cloud solution for authenticating digital personas and transactions
on the Internet. Verifying billions of annual transactions supporting tens of thousands of websites and thousands of customers
globally through the ThreatMetrix® Digital Identity Network, ThreatMetrix secures businesses and end users against account
takeover, payment fraud and fraudulent account registrations resulting from malware and data breaches. Key benefits include an
improved customer experience, reduced friction, revenue gain, and lower fraud and operational costs. The ThreatMetrix solution
is deployed across a variety of industries, including financial services, e-commerce, payments and lending, media, government, and
insurance.
For more information about the study or related topics, please contact:
Jaci Robbins, Director of Marketing, (jrobbins@threatmetrix.com)
014

Spotlight
You think you have what it takes to start a
business in a super-hot market?
PCM takes a close look at some of the most
innovative and promising startup companies in the
payment industry.

'WE MAKE NEW
TECHNOLOGY
APPROACHABLE...'
Roberto Giorgio Valerio, CEO at Risk Ident GmbH, studied
Business Administration but his programming history reaches
far back. He started programming at the very young age and
therefore he is very technically skilled for a business focused
professional. Before Roberto started Risk Ident he was already
involved in 3 other startups as a founder.
to create a device fingerprinting solution in 6 months. The
good news is that our team kept that promise and delivered
on time. After being approved by the Otto Group, the initial
project turned into a company in March 2013 and by now
has become a leading provider of fraud prevention software
within the online space.
Where did the idea for Risk Ident originate and what’s the
organization’s vision?
Risk Ident started out as a project within Liquid Labs, an
incubator of the Otto Group based in Hamburg, Germany.
The Otto Group is second largest European online retailer. This
company has an estimated 12 billion Euro annual turnover,
roughly 7 billion of that is online retail. Within Liquid Labs the
project team looked into different aspects where they could
leverage the knowledge and data of the Otto Group. Actually
Risk Ident was founded as Device Ident, which was a focused
on device fingerprinting. The Otto Group’s goal at that point
was to use device fingerprinting for its online shops.
That’s the point where I came into play because I promised
What sets you apart from other anti-fraud solution
provider?
Risk Ident was able to use the knowledge of the Otto Group
and its great amount of historic data to built its first fraud
prevention product. With tens of millions transaction every
year having the Otto Group as a domain expert (including
its years of experience of how fraud looks like) is a great
advantage and benefit for Risk Ident.
Even though the Risk Ident team consists out of mostly data
science professionals and senior software engineers who
did not have in-depth experience with fraud prevention or
payments before, we were able to create some of the most
technically advanced products for anti-fraud solutions on the
market. The reason for that is that we might be seen as the
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startup spotlight
latest to the game but we do use the latest technology that
others weren’t using a couple of years ago.
As the technical landscape is undergoing continuous change
and innovation, we always try to be at the vanguard of that
front. So, with more than half of the Risk Ident team being
either data scientists or software engineers we focus mainly
on a continuously evolving product that strongly benefits from
the latest technology.
Where does Risk Ident stand now and what’s on the
horizon for the company?
We currently have 50 clients which are mostly big enterprises.
Our client group includes some of the biggest mobile network
operators, big ecommerce stores (with at least 100-200 Million
Euro annual turnover) Fintech companies as well as banks.
I think we have a strong position in the German speaking
markets and therefore have become one of the leaders on
our home domain. We just started to venture into additional
European market and at the beginning of next year we’ll also
be operating in the US.
Putting it into a startup environment perspective you
commonly need three essential components to become
successful. These include having a great team, building
excellent products and eventually selling it to real clients. We
have accomplished and gathered these vital startup elements
and now it’s all about how to scale up our business. Moreover,
we do target only larger companies who can use our tools to
enhance their fraud protection.
More importantly we realized that it doesn’t matter if
we sell to a big German e-commerce player, a UK based
telecommunication company or an US travel portal because
many fraud cases are very similar in the way fraudsters
operate. One of our advantage is that the tools we built
are very agnostic to the market and will also work for other
markets. The only thing we have to do is to train the tools
with different data. For that reasons I’d say that we have good
chances to expand to new markets on a global scale.
In your experience of fraudulent transactions, what is the
most common source of fraud?
When talking about online retail there are two main aspects
about fraud. On the one hand, there is the single fraud cases
in which commonly people with bad credit score try to obtain
goods. You can usually identify them by behavioral actions
like changing their name or address and they try to get one
product or service. In contrast to that there is the very harmful
organized fraud. These people are very professional and
oftentimes even do that illegal activities for a living. What is
more, they don’t stop at e-commerce fraud alone, they would
try to get to other people’s online loans or similar personal
account information. Another fact why organized fraud is
considered a lot more harmful than single fraud cases is that
with the increasing number of fraud cases, the risk to have full
complete loss scales up. Unfortunately, the trend of organized
fraud is becoming stronger right now. In conclusion organized
fraudulent activities represent the bigger problem and risk
for the merchants.
Will upcoming regulations in Europe influence Risk Ident’s
business, and how?
Our main advantage in this regard is that our fraud prevention
software can be installed on premise. That’s very handy when
we talk with banks for instance, as they are working with very
sensitive customer information which banks won’t share with
third parties. And even the large e-commerce customers with
the big data breaches that happened throughout the last years
they were very reluctant to hand out their end costumer data
to a third party. When working with our software the clients
can install and integrate it on premise and they can train it with
its data without sending out a single customer information.
That is an immense value we can offer in comparison to other
anti-fraud solution provider. In that light regulations are not a
major challenge for us to overcome.
How do you teach a machine to get smarter at identifying
fraud?
In the light of machine learning there is one major
misconception. Many people say that their machine learning
component of their product is like a secret ingredient. I think
that the machine learning algorithm you use is not the main
advantage. There are many rather simple algorithms that can
be very good, such as Random Forest, Naive Bayes or Logistic
Regression. However, there are two things which are more
important than the machine learning component. First of all,
it’s about the type of data you feed in – when feeding in lowquality
data then you can only expect poor results. The other
vital aspect is how to scale a machine learning system on a
production level in terms of amount of data and response
times. That’s why Risk Ident is very transparent about our
machine learning components and sometimes we even tell
our customers which specific set of algorithms we use on their
data because we know how hard it is to provide it on a stable
productive software solution and you need to know what you
put in. The machine algorithm typically will only understand
numbers, so you have to get your data pre-processed also
017

startup Spotlight
For more information please visit https://riskident.com/en/
known as feature extraction. Examples: An address could be
converted into a geolocation, an email could be evaluated by
name, domain or general structure of the email. After that
you need to know how to feed it to the machine learning box
and only then you can obtain good results you can work with.
To give an example for that, you can feed in an email
address and the machine learning component can find other
transactions with a similar email structure. Based on that I
believe that the competitive advantage in our work space
rather lies in how strong you are on the technology level,
which contributes to your ability to build good software and
ultimately making sure that the software is scalable.
What are some best practices for business owners when
it comes to protecting their customer’s information?
Personally I think, that one of the best practices is educating
your customers. By instructing them to not use the same
password for all their different online shops and online services
and reminding them to change the passwords occasionally
and write them down. Simply because today it is much harder
to get access to a paper note with written passwords than
hacking into your laptop. To that end, it is paramount for all
types of businesses to educate the end-customer about being
prepared and careful about that sensitive information.
Besides, not handing out any end-customer information is of
vital importance. Fraud prevention can be done in-house by
using on-premise software. Every time you let a third party or
service access your end-customer data you risk customer data
loss. Furthermore, try to store vital customer data encrypted:
Businesses should use individual hashing for passwords and
to be more specific they should be using individual salts to
ensure the passwords cannot easily be decrypted even after
customer data was stolen.
The Risk Ident team always strives to enhance their products
018

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019

events
Events
Accra - Ghana
This year’s theme is “Connecting people to Banking & Financial Service
through Financial Inclusion”. This conference aims to make financial
inclusion a reality for the masses. Through this event, all the key
decision makers in the Banking and Finance sectors will be brought
together as a way of facilitating the exchange of ideas, experiences
and strategies that they have come across in their careers.
1–2
7–8
London, United Kingdom
After last year’s huge success, Digital Travel returns for its second
year with an action packed 2-day agenda of interactive case studies,
roundtables and workshops. The sole aim at the event is to provide
a “how to” guide to transform your digital strategy to drive online
revenue growth.
London, United Kingdom
Discount Code: PCN20
PayExpo Europe is the ultimate event for anyone looking to make their
payments process faster, easier and more secure. As the UK’s largest
event, PayExpo attracts 2000+ attendees from over 50 countries
and 700 different organisations who meet to network, learn about
the ever changing payments landscape and make essential business
contacts for the year ahead.
7–8
7–8
London, United Kingdom
The Center for Financial Professionals invite you to attend Vendor
& Third Party Risk Europe, a two-day conference featuring an FCA
KEYNOTE ADDRESS from Robin Jones, Head of Prudential Specialist
Supervision, Financial Conduct Authority (UK). Plus don’t miss the
opportunity to hear interactive discussions and presentations from
over 20 senior risk experts.
020

events
Amsterdam, The Netherlands
Discount Code: partner
This large scale international conference hosts various cities,
system operators, energy companies, industrial organizations and
technological parties. They come together to share knowledge on how
to achieve a Smart City. Projects where climate ambition and energy
transition are the main focus point. Amsterdam Smart City is official
host of the Smart City Event.
7–10
14 -15
New York City, United States
The Center for Financial Professionals invite you to attend Vendor &
Third Party Risk USA, a two-day conference set to feature interactive
discussions and presentations from over 20+ senior risk experts
who will address; Regulatory Landscape, Defining Vendors, Due
Diligence, Categorizing Vendors, Operational Risk, Documentation,
CyberSecurity and much more!
Victoria Island Lagos, Nigeria
Intermarc Consulting hereby brings to you the annual CBN Cashless
CardExpo Africa. The objective of this conference is to focus on the
future of retail payment as well as promote and deepen the adoption
and usage of electronic payment in Africa with the theme “Retail
Payment & Ecommerce”.
14 -16
20 -21
London, United Kingdom
Retail Banking Analytics, is a two day event bringing together 150 retail
banking professionals to discuss solutions for data management and
cultural challenges and enable banks to define an agile, automated
and actionable analytics strategy to improve profitability and
enforce customer centricity in an increasingly digital and competitive
landscape.
021

events
London, United Kingdom
Discount Code: WMPCN10
This landmark one-day summit at The British Museum assembles the
most exciting, forward-thinking innovators and captivating thought
leaders in money, banking and finance. The international lineup of
speakers covers a fascinating range of topics including the evolving
role of digital currencies, issues of security and trust, crowdfunding,
friction-free transactions and the democratisation of investment and
lending.
23
27-29
Discount Code: BBF_PCN
New York - United States
Engage in an open dialogue with professionals at the forefront
of biometrics. Come address the emerging challenges banks and
financial institutions are facing regarding the adoption of biometrics
and the integration of these technologies into mobile devices, online
service applications, and ATM machines in order to ensure customer
satisfaction and enhance security.
London - United Kingdom
Mondato Summit Europe is the mobile financial services and related
commerce space, more succinctly termed Mobile Finance and
Commerce (MFC), has witnessed the emergence of several inter- and
intra-industry constellation service propositions, such as Apple Pay
and CurrentC, which are shaking up the status quo.
5–6
022

Payments & Cards
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