(music)
- E-commerce has a secret.
Whilst the space is booming
with nearly four trillion
dollars in revenue
predicted in the retail space in 2020
2.2% of this revenue goes straight
to fraudulent transactions.
This amounts to 90 billion
dollars in losses each year,
along with 175 billion
dollars in associated costs.
Whilst merchants are delighted
at the increasing revenue
they're shocked at increased costs
due to liability shift with
card not present transactions.
At Microsoft we face the same problem.
Across both our in-store
and online channels
we were hit by high fraud costs.
We looked out to the market
and couldn't find a
vendor to meet our needs,
so we went ahead and
designed a solution in-house.
This solution focuses on
finding the right balance
between reducing fraudulent transactions
without blocking legitimate transactions
we receive from our customers.
Our internal solution protects against
payment fraud and much more,
managing over 1.1 billion
transactions per year and
ultimately saving the company
over one billion dollars in fraud stopped.
The solution has been nominated
for the INFORMS Franz
Edelman Award in 2019.
The solution we delivered for Microsoft
uses the same technology that
we are now commercializing.
With Dynamics 365 Fraud Protection
we are combining all our
expertise as a merchant
with Microsoft's AI and
fraud protection technology
and access to a depth of data that other
solution providers don't have access to.
We aim to help customers
with three outcomes.
Firstly, to help reduce
fraud and fraud related costs
through our connected
graph and advanced AI
that adapts to the
changing fraud landscape.
We do this in the following way.
The product builds a graph of data
that provides a view
of fraudulent behavior
across all participating
fraud protection customers
while still helping to protect
the privacy of shoppers
and the competitive
intelligence of merchants.
Another benefit of the unique
data in the connected graph
is that customers have immediate access
to the extensive fraud
protection intelligence
Microsoft has gained as one of
the worlds largest e-commerce merchants.
The result of all of this is
fraud protection customers
get high accuracy fraud assessments
delivered by advanced adaptive AI
that uses both their own real time data
and that of the connected graph.
Secondly, we help increase
our customers revenues
by ensuring more of
their legitimate customer
transactions go through.
We're doing this in combination
with innovative bank and
issuer partnerships that share
fraud related information which
can ultimately help to
increase acceptance rates.
And finally, we improve
your customers experience
by reducing wrongful rejections
and lowering friction during
the buying experience.
In the event that a legitimate transaction
gets blocked we provide tools
to help your customer support
quickly resolve the problem.
Our experience with customers so far
has highlighted that they come from
both ends of the spectrum.
Some customers have sophisticated
industry-specific fraud management tools,
and come to Microsoft to enrich
their machine learning models,
whilst other customers have very
basic functionality and
little understanding
of where to begin to improve
their fraud prevention today.
We are able to meet our customers
wherever you are in
your fraud protection journey.
So let's go ahead and look at the demo.
I'm a fraud manager, and
in my organization today
we have basic fraud prevention capability.
This tool will help me find areas
to improve our fraud protection.
Let's start with the Diagnose experience.
This is a differentiated
experience from Microsoft
as I can actually preview
the products ability
to detect fraud in my
business before I deploy
a proof of concept,
which ultimately helps me
justify the investment.
Diagnose is a 3-step process.
Step 1, I come in and I
upload my historical data
and this is going to include transactions,
payment instrument information
and any charge back data.
Once I've uploaded my
data I can now access
a full data diagnostic report.
The data diagnostic
report identifies gaps in
the data that we have today
and suggests key areas
where I can go ahead and improve it.
We can see here two
gaps in the date range,
and the solution is
actually also prompting me
to capture specific payment
instrument information.
I can go ahead and improve the quality of
this data before I move on to step three,
where we'll access a full
risk diagnostic report.
The risk diagnostic
report summarizes patterns
across my business and presents me with
AI analysis of fraud protection
and revenue opportunities.
It also enables me to adjust
the operating characteristics of the model
to see the effect that has on my business.
I can then proceed with
the proof of concept
in our live transaction environment.
As the fraud manager,
I can now use this tool
over the coming months and
it will actually help me
to justify changes to our
fraud protection landscape.
We're becoming more
sophisticated and including
real-time transactions
using fraud protection APIs
along with machine-learning,
connected graph,
device fingerprinting,
and trust knowledge
integration with issuers.
Ultimately, we can improve
our acceptance rates
and reduce the number of
fraudulent transactions.
However fraud landscapes
continue to change
and I have identified some
high risk payment instruments.
I want to go in and
adjust the operating point
of the model using our
virtual fraud analyst.
The virtual fraud analyst
looks at specific segments,
I can see the impact of the
fraud driven by that segment.
I can look at how much
fraud is stopped versus
good customer transactions.
But the key is to find the right balance.
By moving the model operating point
I can actually go ahead
and increase transactions
by thousands but only
allow in a small number
of fraudulent transactions.
The cost of these fraudulent
transactions are easily
covered by the profit
margins of the good quality
transactions that we
will receive as a result.
I can go ahead, put this into production.
Now, all fraud models
stop some good customers
so it's crucial to have
a good support tool.
Summer is a customer who has recently had
a transaction rejected.
The reason for this is high PI velocity
in combination with the
fact that the transaction,
the billing address, and
the shipping address have
a large distance between them.
So as a result, this
transaction has been rejected.
Now when I look at Summer's
record, she has good spend
and no history of fraud,
and when I speak to Summer,
she clearly identifies this was a gift for
a family member who lives on
the other side of the country.
I have identified this is
a legitimate transaction
and I can go ahead and add
her to a temporary safe list
so that she can successfully
make that transaction.
Using embedded Power BI, I
am presented with a scorecard
that clearly demonstrates
the impact of the work
I have been doing using
Dynamic 365 Fraud Protection.
I am presented with
month-to-month performance of
fraud protection systems
and I could easily filter
and give access to relevant users.
And ultimately I can
bubble this information up
to business leaders so that they
can clearly understand the impact
of Fraud Protection on their business.
In summary, Dynamic 365
Fraud Protection combines
Microsoft expertise as
one of the world's largest
e-commerce merchants
with our comprehensive AI
and fraud protection technology to deliver
a solution that working alongside bank
and issuer partnerships,
can help our customer to increase revenue,
lower fraud-related costs, and deliver
a better experience to your customers.
Thank you very much for listening.
To learn more, follow the link.
Thank you.
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