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CHRISTINE STEWART: Good morning, good afternoon,
and good evening.
Welcome to the Applied ML Summit.
I'm Christine Stewart, Customer Advocate and Account Manager
here at Google Cloud.
And I have the unique opportunity
to kick off today's session with SADA and WorkFusion
for operationalizing AI and automation
with digital workers.
Organizations are asking themselves,
how can we do more with less and embed automation
in our internal and external processes?
Automation is ever so more important, especially
amid the great resignation.
It's so much more when you think about AI and ML
than training a model.
How do you operationalize the entire process
and lifecycle to solve a business problem, not just
for today, but in the future?
Enter WorkFusion.
WorkFusion, an intelligent automation provider,
has married the art and science of machine learning
and automation to bring digital workers, which you'll
hear about momentarily, to their customers that
can execute tasks like expanding operational capacity for areas
like anti-money laundering and know your customer in banking.
I'm thrilled to introduce Peter Cousins, Chief Technology
Officer here at WorkFusion, and San Thongprasert, SADA Customer
Engineering Manager for the Northeast,
as they discuss the business value that WorkFusion brings
to their customers, why WorkFusion chose Google Cloud,
and how they're using machine learning today.
Without further ado, I'll hand it to San.
SAN THONGPRASERT: Thank you, Christine.
So, Peter, can you tell us what is a digital worker?
PETER COUSINS: Yes.
A digital worker is the personification
of our automation functionality performing
a complete task that normally would be done by people.
Now, our platform has a lot of capabilities,
omnichannel information gathering, and putting machine
learning for doing document extraction,
as well as decisioning.
And it can be complex with that power.
We found that by completing it and packaging it,
it made it more easy for people to consume, more easily
to be successful quickly.
And so our digital workers actually
took a cue from our customers who
began to name these automations that they built themselves
on our platform.
And we love the idea that ML could
feel like a member of the team.
Our digital workers, like I said,
complete knowledge worker work--
so things that were grunt work but require judgment done
by people on the team before.
So this can include things like Evelyn,
who's our entity sanctions screening
analyst and adverse media monitoring analyst.
This kind of entity screening is a key part of doing KYC.
We've had numerous banks who have
used Evelyn to reduce the labor involved
in doing level one KYC work dramatically, as much as 90%.
And the way Evelyn works is ingests data
from multiple channels, from documents, extracts data,
does preprocessing and postprocessing,
does analysis of similarity and differences
when it comes to sanctions hits, builds a case as to why this
is a false positive or a true positive,
or, if it's somewhere in between,
enumerates the reasoning for and against so
that a human operator can more quickly make a decision.
Now, Evelyn can also do adverse media monitoring.
Adverse media monitoring is something
where you search the web using Google Search, for example,
to pull back a series of articles that may indicate
that someone would be a problem to do business with,
whether that is a company or an individual,
because of criminal or reputational risk reasons.
And we also can use data sources like Dow Jones and LexisNexis
to complete the picture.
But at the end, what you need to do
is use natural language processing to both understand
whether the article is on target,
whether the entity that you're talking about
is actually the subject of the problem,
and whether this problem is being definitively
a problem for doing business with this party.
So the same kind of dossier annotation
of definitely not, definitely yes, and somewhere in between
and need a second pair of eyes on it to complete the picture
is a key part of Evelyn doing the job, both in terms
of taking the easy decisions off the table
and being able to do straight-through processing
without a person involved, as well as knowing
what she doesn't know and when it's best for a person
to take over but doing enough work that that person's job is
much easier.
So people can take digital workers like Evelyn,
and they can use them as inspiration
on how to build their own digital workers.
Because it's effectively an open box that
lets you see how it works so you can learn the best
practices and capabilities you might not otherwise
have understood that the platform could do.
We see people then take these lessons
and build their own digital workers,
which they then keep as their own proprietary advantage.
Or they ask us to take over to bring to a broader audience.
SAN THONGPRASERT: Got it.
And why is Google Cloud platform such an important part
of WorkFusion?
PETER COUSINS: Well, Google Cloud
is a platform that many of our largest customers
were making big bets on.
And they turned to us as a trusted provider and said,
we want you to run there.
So that was what inspired our first look.
When we took a closer look, we were
quite happy by how seamless it was
and how well-designed the platform was and really liked
the way the Google Cloud marketplace makes
it easy to acquire and deploy solutions very quickly.
SAN THONGPRASERT: So on the more technical side of things,
can you explain to us how machine learning
is operationalized today as part of your software, more
specifically your digital workers?
PETER COUSINS: So a key part of operationalizing
is the standardization that comes
from using standard packaged digital workers
across multiple companies.
By so doing, number one, we can get started very quickly.
We have the complete functionality ready to go.
Usually, we can have a pretrained model
that we arrive with that comes bundled
with the digital worker.
That can get you started at a pretty great level
of performance at day zero but also allows
for incremental retraining or complete retraining
at the customer's decision in order
to get results that are more tailored to that customer.
By having standardization, though, it also
allows for you to do federated and other kinds of network
learning that would otherwise be impossible
if all the models across all the different companies
were idiosyncratically different.
So that's a key part of operationalizing
it is that standardization.
Second part is what we call the citizen data scientist.
A lot of people talk about citizen developer.
But we're quite keen on making it
very simple in very simple problems to allow even business
users to define things like how a document should be
automatically ingested by giving it simple guidance
and elements to be extracted in a small number of examples.
Making that automatically generated,
automatically evaluated, and automatically
updated makes it a lot easier for people.
Next, by having these standardized models,
many of our customers who are in high-impact industries,
whether that's banking and insurance
or whether that's things where the stakes are high,
model risk management is becoming a very common factor.
Model risk management takes a lot of bandwidth, potentially,
from practitioners who are trying to build bespoke data
science models.
And by standardizing the production of model risk
management reports from within our platform,
it just really streamlines the ability
of getting those things approved and into production.
SAN THONGPRASERT: And is there a way
that Google Cloud is actually making
these digital workers more accessible for your customer as
well?
PETER COUSINS: I think it comes down to the marketplace
more than anything else.
Having people be able to discover us within Google Cloud
Marketplace, see Evelyn's resume as her Marketplace page,
effectively, understand the skills, be able to learn more,
but also, with just a single click, having
Google Deployer stand up Evelyn in the customer's
virtual private cloud or WorkFusion's makes it so much
simpler to adopt in traditional enterprise software.
SAN THONGPRASERT: Got it.
Thank you, Peter.
I'm going to actually send this back over to Christine.
CHRISTINE STEWART: Peter, first and foremost,
thank you so much for choosing Google Cloud.
San, thank you for the time today as well.
To the team, that's all the time that we have here today.
Many thanks again to Peter and to San.
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