MICHAEL BALDWIN: Hi, everyone.
I'm Michael Baldwin.
I lead Financial Services Product Management
for Google Cloud.
With me today is my colleague Tom Thane, also
a financial services product manager.
Thanks for joining us.
Tom and I both spend our days thinking
about how we can help financial institutions transform
their businesses with artificial intelligence and machine
learning.
Today, we'll be sharing with you an overview
of some of the major areas where we
see this transformation happening
and how Google Cloud can help.
We know that business transformation
is top of mind for many of our financial services customers.
However, these are challenging times,
with financial services institutions navigating
the pandemic.
Interest rates are at an all-time low.
Customer expectations are changing with the shift
to digital.
For example, digital banking has increased from 63% in 2019
to 72% today.
Regulatory requirements are evolving
and compliance costs are increasing.
Also, the financial services industry as a whole
is facing new and creative types of fraud.
For example, the dollar value of attempted
fraudulent transactions grew 35% in April year-over-year.
And finally, with the move to digital,
disruption is accelerating.
For instance, the pandemic drove a 72% increase
in the use of fintech apps in Europe.
Today, we'll cover three areas where
we see AI helping to unlock business transformation
in financial services.
First, AI can help reimagine the customer experience
to deliver things such as personalized digital banking,
contact centers with a AI-powered virtual agents,
and smoother customer experiences for traditionally
paper-intensive processes, like lending.
Second, financial institutions are looking for ways
to use data and analytics to transform how
they detect and manage risk.
And finally, financial institutions ask us
for a best-in-class AI platform that is flexible, secure,
and compliant, that empowers them
to build their own AI-powered solutions
to solve their most critical business problems.
As we talk about through examples
of AI-powered transformation in financial services,
you'll see that AI isn't a science experiment.
It's being used to solve real-world problems
across banking, capital markets, insurance, and payments.
The key thing for us is working with our customers
to identify their top business problems, then figuring out
how AI can be most helpful for them in solving
for their key needs.
And ultimately, our goal is to make it as easy as possible
to deploy these solutions in real-world, end-to-end
workflows.
Deloitte recently surveyed over 2,700 global IT
and line-of-business executives to obtain a global view of how
organizations are adopting, benefiting from, and managing
AI technologies.
And as you can see from this recent Deloitte survey, shown
here on the slide, 81% of customers
are reporting payback on their AI in less than two years.
When AI is absorbed in this way, it
can lead to true business transformation.
So first, let's talk about how artificial intelligence
can help transform the customer experience.
Customer experience is, obviously, a broad area.
Let's talk through a few examples of what this can mean.
Many financial institutions today
struggle to get a single view of the customer, sometimes known
as customer 360.
With the continued shift from physical branches to digital,
financial institutions are looking for new ways
to deliver high-quality and helpful experiences across all
of their customer touch points, lighting up
omnichannel operations across web, mobile, contact centers,
physical branches, and ATMs.
When it comes to acquiring new customers,
financial institutions want to onboard in days, not weeks,
and are looking to use AI-powered intelligence
to help retain those customers.
Finally, as customer 360 becomes a reality,
we'll be able to enable AI-powered experiences
to sell the right product to the right customer
at the right time, and also drive
other personalized insights to help with customer retention
and new business acquisition.
Our Contact Center AI solution is one example
of how AI is helping reimagine the customer experience.
First, let me share a quick overview of our Contact Center
AI solution.
The goal of Contact Center AI is to deliver exceptional customer
service and increase operational efficiency
using artificial intelligence.
It enables virtual agents to converse naturally
with customers and expertly assist human agents
on complex cases.
It does this through a set of underlying powerful features,
for example, Dialogflow, which automates basic chat and voice
interactions, a feature called Agent Assist,
which makes human agents more effective,
and Insights, which unlocks insights about call drivers
and customer service interactions.
Customer support is particularly relevant in financial services,
as financial institutions have seen 1,000% increase
in call volume since the onset of the pandemic.
Customers are calling in with questions
like are branches still open?
Can I defer loan payments?
Are you waiving any fees right now?
And, of course, security is paramount
in financial services.
So our teams are hard at work making sure
that we have bank-level security built into this product.
Next, I want to share how we're working
to make lending more operationally
efficient and create smoother customer experiences.
Our Lending Document AI alpha is built on top of our Document AI
technology.
Document AI helps organizations unlock insights
from their mountains of documents using
artificial intelligence by turning unstructured data
into structured data, which means you can now actually
understand and utilize it.
With mortgage rates low, there's a surge
in mortgage applications.
This solution provides a bundle of specialized models, focused
on specific document types used in lending.
And with this solution, we're able to work with lenders
to quickly onboard customers and speed up
the processing of paper-intensive mortgage
applications.
Also, when the US Congress authorized the Paycheck
Protection Program as part of the pandemic relief package,
we made this same technology available to help
with the processing of PPP loans.
Next, let's talk about insurance.
Dealing with a car accident can be
a major headache for the automobile owner
and a time-consuming process for the insurer.
We worked with USAA, an insurer, and a Google partner, Mitchell,
to build ML models that analyze images
of cars that have been in accidents
and make damaged parts estimates.
This, in turn, can be converted into an estimate of costs
needed to repair the car, saving appraisers' time
and leading to smoother customer experience.
Now I'm going to hand things over to Tom Shane,
my colleague and fellow product manager for financial services,
who will walk us through how AI is driving business
transformation and risk management,
and also will share an overview of how our AI platform is being
used by developers and data scientists
inside financial institutions to deliver on their top business
priorities.
Over to you, Tom.
TOM SHANE: Thanks, Michael.
When Google Cloud thinks about how
we can help apply AI and ML to transform
financial services and the risk management space,
we typically think about four key things.
First is mitigating risk across channels
and digital experiences.
As Michael said, financial institutions
are increasingly looking to move to a digital-first posture
in the post-COVID environment.
That provides great benefits from a customer experience
perspective.
But it does open up new vectors of risk and potential fraud,
for example, rising scams in the retail banking segment.
Second, moving to holistic and contextual analysis--
by holistic, we mean extracting signals
from traditional structured data sources,
but also combining that with insights from unstructured data
to form one holistic or customer 360 view.
That allows financial services organizations
to contextually analyze customers and events
and detect true anomalies or risks that
need to be reviewed from normal behavior for that customer
or event.
Third, thinking about how to dynamically and rapidly respond
to changing risk types--
for example, changing macroeconomic and market
conditions or new fraud types in the post-COVID world.
And finally, being able to offer that worry-free and seamless
customer experience--
customers want to know that the risk management systems that
are operating in the background are keeping their accounts
and assets safe, but they don't want
those systems to inappropriately impact the customer experience.
For example, a false positive alert on a fraud transaction.
Next, I want to talk about a customer who's
working to help transform their risk management processes.
HSBC is working with Google Cloud
to leverage the latest in machine learning
to dynamically assess potential financial crime risk
and holistically look at customers.
HSBC is bringing their extensive knowledge
of combating financial crime, which
combined with Google's expertise in developing ML solutions,
is already leading to encouraging results
in this area.
You may have heard from our CEO, Thomas Kurian's, keynote
that Google Cloud is focused on building up
digital transformation solutions powered by Google's AI.
In risk management, we're focused
on giving you a preview of two of those solutions today.
The first is anti-money laundering, or AML,
and the second is know your customer, or KYC.
I want to talk about some of the challenges and opportunities
in each of these areas.
When our customers ask us about AML,
they often talk about challenges around the current rule-based
system, and that sophisticated bad actors can sometimes
get around these systems, leading to potential missed
money laundering events.
And the system often lead to many false positives.
On the data side, we also hear challenges about poor data
quality and the lack of that holistic or customer 360 view
to inform their AML efforts.
And finally, we hear that all of these false positives
are creating heavy amounts of manual work, which
is taking away from higher-value opportunities.
In terms of opportunities for innovation,
Google Cloud can help extract insights from data
and apply AI and ML technologies to better detect and prevent
and identify those real money laundering events,
and then help organizations operate more efficiently
by reducing false positives, as well as providing more insights
in the alert review process.
Know your customer, or KYC, is another area
that financial institutions often ask us about.
We hear challenges, such as manual processes
around looking at documents and combining
that with other information.
We hear how this sometimes impacts the customer onboarding
process, either delaying it or leading
to excessive outreach to customers
to collect information.
And finally, we hear about challenges
of integrating all this information
into banks' overall systems.
We think there's three key opportunities
to help improve this process.
The first is helping in the automation part.
By automation, we mean looking at all
of the documents received in the KYC process
and automatically extracting insights, entities,
and information from these documents,
and putting them into downstream processes.
We also think there's an opportunity
to move to a rapid and digital onboarding process, especially
important in the post-COVID environment.
And finally, we think extracting insights from these documents
can better identify potential risk sooner, as well as inform
financial services organizations' overall customer
360 efforts.
Next, I want to turn to the capital markets
space and BNY Mellon, who's using
machine learning to predict treasury settlement failures.
The treasury market is one of the largest and most liquid
in the world.
However, 1% to 2% of transactions
fail every day, which equates to about $70 billion.
So BNY Mellon is leveraging Google Cloud
to develop end-to-end pipelines to better predict when
these settlements might fail.
This has allowed BNY Mellon to get
in front of these potential failures
and help mitigate them before the data cutoff.
Outside of solutions in the customer experience and risk
management space, we also know financial institutions
want to build their own AI and ML
models for a range of different use cases and business
problems.
So at Google Cloud, we're focused
on building and deploying a flexible platform
to allow financial services organizations to build out
these models.
And this is based on Google's learnings
running large-scale ML systems.
We believe that responsible AI equals successful AI.
When we at Google think about responsible AI,
we typically think about four key pillars.
The first is robustness.
And that's making sure results stay robust over time.
The second is ensuring that these systems
are understandable.
The third is considering the fairness implications
of these systems.
And the fourth is incorporating security by design
to keep both the data and the assets
secure throughout the entire model lifecycle.
In terms of robustness, at Google,
we found that transition into end-to-end machine learning
pipelines greatly helps in this way.
We see that an ML system is much more than just a model.
It's about consistently extracting and validating data,
ensuring the data distributions don't shift over time.
It's about applying the right features and the right model
to the problem, training the model, evaluating the results,
and ultimately deploying it to production.
A common failure mode that we often hear from our customers
is a great model that works well in development,
but can't transition to production.
It doesn't replicate results in production.
So we've introduced AI Platform Pipelines,
which is coming soon, to help with this problem.
AI Platform Pipelines is integrated
with other GCP native services, such as our data
warehouse, BigQuery, and our AI serverless training platform.
We know that financial institutions are also
focused on making sure ML systems are understandable.
We know that this is key in financial services
for a couple key reasons, the first being
that these systems often underpin high-value business
problems, where the results need to be robust
and they need to be understandable.
Third, these systems often interact with humans,
whether it's in a model governance approval process
or in downstream processes, such as an analyst reviewing a fraud
alert.
And finally, we know these models need
to meet regulatory obligations.
So Google has built our explainable AI solution
based on Google's experience understanding and interpreting
ML systems.
This solution provides feature attributions
to say which attributes or features are most predictive
of any given prediction?
In this hypothetical example that you see in the lower
right-hand corner, you can see that the amount, feature,
or attribute is most predictive of this particular prediction.
Google is also very committed to AI and ML fairness.
We've released our AI principles,
which govern applications in which Google applies its AI
and ML technology.
And we've also released a fairness toolkit.
I want to highlight a couple of components of it.
First is the what if tool, which allows
organizations to dynamically dissect their data and results.
The second is fairness indicators,
which allows organizations to provide categories
and then slice different fairness performance
metrics by those categories to evaluate differences
across groups.
And finally, I want to talk about our responsible AI
practices to provide guidelines to organizations implementing
these systems.
Next, I want to talk about security
and how at Google Cloud, we're incorporating this
by design in our AI platform and solutions.
We know security is absolutely table stakes
for a financial services organization.
So we're focused on a couple areas.
The first is preventing data exfiltration
with our VPC Service Controls and ensuring
no external internet access.
The second is providing choice.
So customers can use their own encryption keys
to encrypt and decrypt data.
And customers can choose where data processing happens
and where their data is stored in terms of data residency.
And finally, always ensuring authorized and appropriate
access to that data through fine grain identity and access
management controls, as well as our Access Transparency Logs.
Finally, I want to turn to a customer who's
using Google Cloud's AI and ML infrastructure.
Two Sigma has moved some of their deep learning research
to the cloud, in large part to leverage
the latest AI and ML infrastructure.
They're using Google Cloud's tensor processing units,
or TPUs, which are purpose-built chips by Google
for machine learning training and inference.
And you can see when Two Sigma moved their TensorFlow
workloads to TPUs, they saw a boost in productivity, which
allows them to spend less time worrying
about managing their AI and ML systems
and more time crafting results.
In summary, Google Cloud is focused
on providing data-powered innovation
in financial services.
We're building out AI-powered solutions
in customer experience and risk management
for financial services organizations.
And we're also focused on enabling a digital platform
to allow financial services organizations to build
and deploy their own AI and ML solutions in a way that
meets the requirements.
We look forward to meeting you and working
with you help transform the future of financial services.
If you're interested in learning more,
please follow the URL on the slide.
Thank you so much for taking time out of your day.
And again, we look forward to working with you.