The ai Corporation’s
solutions can be tailored to battle fraud across various sectors and can be
implemented far and wide. Utilizing the power of machine learning and
artificial intelligence, the systems take the frontline in protecting and
elevating payment experiences. Serving a vast array of banks, an expansive
network of multi-channel merchants, and cardholders, they diligently monitor
over 25 billion transactions and authorizations every year. Recently, there’s
been a noteworthy collaboration between The ai Cooperation and
Shell Business Mobility to further protect Shell Card users.
The ai Corporation’s advanced solutions, powered by machine
learning and artificial intelligence, play a pivotal role in safeguarding and
enhancing payment experiences for a clientele exceeding 100 banks, an extensive
network of over three million multi-channel merchants, and more than 300
million consumer cardholders. The company monitors in excess of 25 billion
transactions and authorizations annually.
“With Shell, we provide them what we call an end-to-end fraud managed
service, where we utilize our people and expertise, as well as our technology
set, including the APML technology, which is machine learning using artificial
intelligence, to do the entire fraud prevention process for them. What that
means is we use an analytics component to create rule sets at a very high level
that we implement into our technology that identifies fraudulent transactions
on the cards that they issue to their B2B customers in 42 countries globally,”
says Piers Horak, The ai Corporation’s Chief Commercial Officer.
Piers Horak, The ai Corporation’s Chief Commercial Officer.
The ai Corporation’s systems essentially monitor the use of Shell Cards
to find problem transactions that Shell is then alerted to, “We also have
automated systems that take actions immediately upon certain triggers to block
those cards so that no further transactions, fraudulent transactions can occur,”
says Horak.
In layman’s terms, this is achieved by taking Shell’s data, about half
a billion transactions a year in the card space, according to Horak, and using
machine learning to look for patterns of misuse, or fraud. Once a pattern is
identified, rules can then be put in place to prevent similar frauds from occurring.
For more complex frauds, or things a machine might miss, the company still
relies on data scientists, “The human is good for the things that are not
continuously recognizable patterns. So we can pick out in data sets, things
that the machine can’t. The machine’s going to look for patterns of behavior.
When we have our interfaces where it presents the results to us of a rule
break, that might be using a machine learning rule or a human created rule,”
says Horak.
Oliver Tearle, Head of Innovation Technology at The ai Corporation.
“As well as generating new fraud rules for the emerging fraud trends, the
system also selects the best combination of rules. So be it machine learning
generated rules and also the manual rules to present to the user. It’s also
suggesting rules to be removed from the system, rules which are generating false
alerts,” says Oliver Tearle, Head of Innovation Technology at The ai
Corporation.
Speed and Efficiency
The one of the key advantages of the system is the speed at which it
can operate. Exact numbers or examples are difficult to estimate, but the
machine-driven system is much faster than a human at spotting issues and
creating breaks. Horak estimates that a prevention strategy that might take
humans four to seven days to complete can be done “overnight” by a machine. It’s
not that people can’t detect these problems, it’s that it takes much longer,
and therefore the cost is much higher. It bears restating that all of this can
be done automatically.
The system was able to reduce specific fraud rates to well below 0.1% in certain regions.
Horak can’t give specifics regarding Shell, but he points out that all
international companies have to protect their transactions as a matter of due diligence.
He also says that, like all card-using companies, there was an issue with “skimming”,
where cards are reproduced by criminals and used time and time again. According
to Horak, “we quickly brought down that fraud rate to well below 0.1% in those
regions that were being hit”. That’s impressive.
In general, The ai Corporation aims to provide fully automated
end-to-end payment solutions that require minimum human intervention by utilizing
AI and machine learning. The key the company’s aspirations is their years of
experience within the fraud prevention space. Services in this space include
smart alerts, where messages are immediately sent out when a potential fraud is
detected and that cover balances, credit limits and more. All this allows
companies to quickly take action.
Finally, there’s another component, “We have is what we call a payment
gateway or front-end processor, which takes the transaction in and switches
that transaction to the necessary systems in the background, whether that’s a bank
for a direct merchant or whether it’s our own card issuing system that a
customer like Shell might use,” says Horak. The end result of all this is an automated
end-to-end payment technology that requires minimal human intervention using
machine learning.
Moving Beyond the Closed Loop
Intriguingly, Horak says that the system can be adapted to work outside
of a closed loop. Shell Cards are an example of a closed loop system, where a
card used in a service station is authorized by their own system.
However, “The
trend in the market is that, and driven by legislation as well, like PSD2 and
PSD3 coming within the payment space, certainly we’ve seen it in South Africa
where the Payments Association of South Africa has mandated that all cards need
to be scheme issued, which is an open loop card approach, meaning it has a Visa
or a MasterCard or an Amex or whatever brand on it, and it needs to go across
the rails that are set forward by those schemes. They’ve done that in South
Africa and we’ve developed now the open loop capability off the back of our
system to issue those open loop cards,” says Horak.
Becoming open loop allows a company to use its card system to take payments
from anywhere, “…a driver could need to go for repairs on his vehicle. A driver
could need to stay in a hotel overnight to pay a toll. All of those types of
things can now be aggregated through a single card because it’s on the normal
banking rails, the Visa and MasterCard scheme rails. And that enables that
Shell as a retail fuel operator to provide almost any service offering under
their card to the customer base and aggregate that all under one invoice at the
end of the day to that customer,” says Horak.
The system can expand beyond paying for fuel to include a wide range of services.
The obvious issue is that as the systems expand, so too does the potential
for fraud, “So us having these machine learning technologies in place and
experience deeply within the banking space is also facilitating the enablement
of great protection as these businesses expand into the open loop market, which
has great benefit to them,” says Horak.
The benefits for companies leveraging The ai Corporation’s offerings
are clear to see, and there’s no doubt that card fraud is a major issue in the
retail fuel space. However, the truly interesting element of this story is the
potential for automated systems to drastically reduce the levels of fraud
experienced affecting more traditional, open loop cards.
This is a story that, like so many involving machine learning, bears
watching.
The ai Corporation’s
solutions can be tailored to battle fraud across various sectors and can be
implemented far and wide. Utilizing the power of machine learning and
artificial intelligence, the systems take the frontline in protecting and
elevating payment experiences. Serving a vast array of banks, an expansive
network of multi-channel merchants, and cardholders, they diligently monitor
over 25 billion transactions and authorizations every year. Recently, there’s
been a noteworthy collaboration between The ai Cooperation and
Shell Business Mobility to further protect Shell Card users.
The ai Corporation’s advanced solutions, powered by machine
learning and artificial intelligence, play a pivotal role in safeguarding and
enhancing payment experiences for a clientele exceeding 100 banks, an extensive
network of over three million multi-channel merchants, and more than 300
million consumer cardholders. The company monitors in excess of 25 billion
transactions and authorizations annually.
“With Shell, we provide them what we call an end-to-end fraud managed
service, where we utilize our people and expertise, as well as our technology
set, including the APML technology, which is machine learning using artificial
intelligence, to do the entire fraud prevention process for them. What that
means is we use an analytics component to create rule sets at a very high level
that we implement into our technology that identifies fraudulent transactions
on the cards that they issue to their B2B customers in 42 countries globally,”
says Piers Horak, The ai Corporation’s Chief Commercial Officer.
Piers Horak, The ai Corporation’s Chief Commercial Officer.
The ai Corporation’s systems essentially monitor the use of Shell Cards
to find problem transactions that Shell is then alerted to, “We also have
automated systems that take actions immediately upon certain triggers to block
those cards so that no further transactions, fraudulent transactions can occur,”
says Horak.
In layman’s terms, this is achieved by taking Shell’s data, about half
a billion transactions a year in the card space, according to Horak, and using
machine learning to look for patterns of misuse, or fraud. Once a pattern is
identified, rules can then be put in place to prevent similar frauds from occurring.
For more complex frauds, or things a machine might miss, the company still
relies on data scientists, “The human is good for the things that are not
continuously recognizable patterns. So we can pick out in data sets, things
that the machine can’t. The machine’s going to look for patterns of behavior.
When we have our interfaces where it presents the results to us of a rule
break, that might be using a machine learning rule or a human created rule,”
says Horak.
Oliver Tearle, Head of Innovation Technology at The ai Corporation.
“As well as generating new fraud rules for the emerging fraud trends, the
system also selects the best combination of rules. So be it machine learning
generated rules and also the manual rules to present to the user. It’s also
suggesting rules to be removed from the system, rules which are generating false
alerts,” says Oliver Tearle, Head of Innovation Technology at The ai
Corporation.
Speed and Efficiency
The one of the key advantages of the system is the speed at which it
can operate. Exact numbers or examples are difficult to estimate, but the
machine-driven system is much faster than a human at spotting issues and
creating breaks. Horak estimates that a prevention strategy that might take
humans four to seven days to complete can be done “overnight” by a machine. It’s
not that people can’t detect these problems, it’s that it takes much longer,
and therefore the cost is much higher. It bears restating that all of this can
be done automatically.
The system was able to reduce specific fraud rates to well below 0.1% in certain regions.
Horak can’t give specifics regarding Shell, but he points out that all
international companies have to protect their transactions as a matter of due diligence.
He also says that, like all card-using companies, there was an issue with “skimming”,
where cards are reproduced by criminals and used time and time again. According
to Horak, “we quickly brought down that fraud rate to well below 0.1% in those
regions that were being hit”. That’s impressive.
In general, The ai Corporation aims to provide fully automated
end-to-end payment solutions that require minimum human intervention by utilizing
AI and machine learning. The key the company’s aspirations is their years of
experience within the fraud prevention space. Services in this space include
smart alerts, where messages are immediately sent out when a potential fraud is
detected and that cover balances, credit limits and more. All this allows
companies to quickly take action.
Finally, there’s another component, “We have is what we call a payment
gateway or front-end processor, which takes the transaction in and switches
that transaction to the necessary systems in the background, whether that’s a bank
for a direct merchant or whether it’s our own card issuing system that a
customer like Shell might use,” says Horak. The end result of all this is an automated
end-to-end payment technology that requires minimal human intervention using
machine learning.
Moving Beyond the Closed Loop
Intriguingly, Horak says that the system can be adapted to work outside
of a closed loop. Shell Cards are an example of a closed loop system, where a
card used in a service station is authorized by their own system.
However, “The
trend in the market is that, and driven by legislation as well, like PSD2 and
PSD3 coming within the payment space, certainly we’ve seen it in South Africa
where the Payments Association of South Africa has mandated that all cards need
to be scheme issued, which is an open loop card approach, meaning it has a Visa
or a MasterCard or an Amex or whatever brand on it, and it needs to go across
the rails that are set forward by those schemes. They’ve done that in South
Africa and we’ve developed now the open loop capability off the back of our
system to issue those open loop cards,” says Horak.
Becoming open loop allows a company to use its card system to take payments
from anywhere, “…a driver could need to go for repairs on his vehicle. A driver
could need to stay in a hotel overnight to pay a toll. All of those types of
things can now be aggregated through a single card because it’s on the normal
banking rails, the Visa and MasterCard scheme rails. And that enables that
Shell as a retail fuel operator to provide almost any service offering under
their card to the customer base and aggregate that all under one invoice at the
end of the day to that customer,” says Horak.
The system can expand beyond paying for fuel to include a wide range of services.
The obvious issue is that as the systems expand, so too does the potential
for fraud, “So us having these machine learning technologies in place and
experience deeply within the banking space is also facilitating the enablement
of great protection as these businesses expand into the open loop market, which
has great benefit to them,” says Horak.
The benefits for companies leveraging The ai Corporation’s offerings
are clear to see, and there’s no doubt that card fraud is a major issue in the
retail fuel space. However, the truly interesting element of this story is the
potential for automated systems to drastically reduce the levels of fraud
experienced affecting more traditional, open loop cards.
This is a story that, like so many involving machine learning, bears
watching.
- SEO Powered Content & PR Distribution. Get Amplified Today.
- PlatoData.Network Vertical Generative Ai. Empower Yourself. Access Here.
- PlatoAiStream. Web3 Intelligence. Knowledge Amplified. Access Here.
- PlatoESG. Carbon, CleanTech, Energy, Environment, Solar, Waste Management. Access Here.
- PlatoHealth. Biotech and Clinical Trials Intelligence. Access Here.
- Source: https://www.financemagnates.com//trending/shell-the-ai-corporation-defending-the-payment-pipeline-from-fraud/
