10991_AI_Business_School_Ganesh_Padmanabhan_FINAL
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[AI Business School]
[What is changing in the
financial services industry]
[and why is AI important
to consider in this sector?]
>> Ganesh:
The financial services industry
is going through a
massive transformation.
The traditional bank
that did everything from
investments, loans, marketures,
are now being disaggregated
into multiple
specific ecosystems.
A few things to note:
There is a new generation
of customers and prospects
that have fundamentally
different expectations
than prior generations.
If you look at veld management,
there is a $30 trillion
veld transfer that's going on
from baby boomers
to younger generations.
And every time this
has happened in the past,
clients change advisors.
Less than 50% of
the mass affluent market
has life insurance.
This new set of
customers and prospects
have fundamentally different
expectations of engagement,
and using AI to understand
what these customers want, need
at a very detailed level,
will fundamentally transform
the way you interact with them.
Second, there is more data
available than ever before
to make investment
management decisions,
and it creates an opportunity
for institutions to
create new products,
new asset classes and
new digital experiences.
There's a company in
London called Bridgeweave,
who is building this next
generation of financial assets.
They process
350 million data points,
135,000 calculations a day,
to provide very specific
insights on traded assets
to individuals,
asset managers and advisors.
Technologies like
machine learning
and natural
language processing
can massively improve
the productivity of
employees within institutions.
Banks and
exchanges are using AI
to do trade and
market surveillance
to reduce false positives
and improve compliance,
making the life of
the trade compliance officers
much more effective,
much more productive.
The opportunity with AI
is an increasing
revenue growth,
reducing systemic risk
and improving productivity,
and organizations
that take advantage of this
will then in this new
world in this new future.
[What have you observed
about how AI changes]
[the way employees work
in the financial sector?]
>> Just like any
other disruptive technology
that changes and transforms
markets and industries,
even AI will have an impact on
the labor market in general.
But what we have seen
happen is most of the --
even though
the narrative for AI
has been primarily
written by Hollywood.
When you think of AI
you think of the Terminator,
the big red button,
the Ex Machina,
the whole fully-automated world
that is completely taking
the humans off the loop.
The more practical part of AI
that is disrupting
and changing industries
is where you augment
the human in the picture.
So that leads to two different
distinct patterns that we see.
One, your employees
in your organization,
their daily life
gets a little bit better.
When you're a veld advisor
who is actually
supporting and handling
20 to 30 clients today,
with AI,
you have the opportunity
to improve your productivity
so you can now handle
about 100 to 150 veld clients.
The average day becomes
a lot more fulfilling
and it becomes
a lot more productive
for the employee with AI.
If you look at
15 years ago,
whether there was a role
called social media marketing,
it didn't exist
because the internet created
new industries, new patterns,
and it created
this new set of jobs
that came to life.
Similarly, with AI,
you're going to
start the creation
of what I call
a new college ops,
where you're bringing out
the best in what humans can do.
And it's not about
humans versus machines,
but about machines and humans.
How do they
actually work together
to accentuate,
to improve,
to bring out the best of
human creativity and ingenuity?
So we'll start seeing
a lot of new jobs being
created in the workforce
that is all about,
for example,
how do I train algorithms
with better human intuition
so that it starts mimicking
that intuition
in decision making?
So if you look at --
one of the things we've done,
we've seen
in banking institutions,
is compliance is
a very complex process.
And we did this work with
a large financial institution
where we look at IT assets
and the user access patterns
of how the users
are interacting
and accessing these IT assets,
and then applied
natural language processing
to understand
internal and external policies
to start looking at
compliance hot spots.
Now you give that
and serve these as insights
to not just the
compliance checker,
who is looking for
violations and threats
in terms of compliance,
but also give that
visibility to the user
and the IT departments
to go make their
jobs a lot better.
Now the impact
of something like this
is you are now
fundamentally transforming
the productivity equation
across the entire enterprise.
You're using data to
empower better decision making,
you're using machine learning
to remove the mundane tasks
of actually reading documents
and understanding that,
to actually
giving them insights
and what they
should be looking for.
You still aren't removing
the human from the loop.
The human is very
much in the loop.
You're just empowering them
to make better decisions,
you're empowering them
to bring out the
best of what they do,
which is bring
out their creativity,
bring out their ingenuity
in the process.
[What types of use cases]
[should financial
services organizations]
[consider prioritizing?]
There are a number
of places you can start,
but like I mentioned before,
it's important to
align business outcomes
and start in places
where you'll have a
strong business impact
and you have the data
and expertise to execute.
One way to start is
the front and middle office.
Traditionally, institutions
have a view of their customers
through a
traditional CRM system,
transaction history
and portfolio holdings,
but today,
customers live their lives
way outside
these traditional boxes.
They declare
certain preferences to you,
but you can also
observe behaviors
and how they interact
with your products and services
and you can infer or predict
what their most preferred way,
based on social,
carrier data and so forth.
At cognitive scale,
we call this the profiler I.
It's using AI to
understand the customer
at a very intimate level,
a complete 360-degree view,
and have this knowledge
of their declared, observed
and inferred characteristics.
This could be for an
institutional client, as well.
If you're an
asset management firm
looking to launch
a new fund or a product
you can use this
AI-powered understanding
of all your clients
to target the ones
that are most likely to buy.
If you have this information
and you're an
investment advisor,
you can predict
how a particular client
will react to market
movements or new sentiments
and give them a more
proactive personalized service.
You can also
use this 360 view
for a client to
predict life events
and upsell, cross sell
new financial products,
be it an educational loan
or a mortgage
or give them better service.
You can use this knowledge
to define and build
new financial products
that can open
new markets for you.
I'd say start with the
understanding of the customers.
That's one of the
easiest places to start.
[What advice do you have
for other business leaders]
[looking to implement AI?]
>> AI is an opportunity
where the organizations
that take advantage of it,
sooner rather than later,
will win and
win against competition.
But it's also very confusing,
and there is a lot
of noise in the system.
And it's important
to have a framework
to get started with AI.
First, understand the
power of this technology
and commit to being a business
that will invest in AI.
Now this is critical,
and it's not just
about experimenting
with machine learning,
but its corralling resources
across the organization,
getting the
leadership to buy in,
organizing for success,
understanding the cultural
implications, and so forth.
Second, start with
the business outcomes
that you want to drive with AI.
Too many projects fail
when it starts with,
"I have a lot of data,
I want to do something with it,
let's do AI."
Understanding
the strategic levers
and the business outcomes
that you're trying to drive
is very critical
to drive success.
Third, identify and
prioritize the use cases.
I've seen very successful
AI-first organizations
use a matrix.
It prioritizes use cases with
maximum business benefit,
by their ability to execute
based on data expertise,
and that helps them prioritize
and come up with a list
that they can start executing.
Fourth, develop an
AI center of competence
and a road map to execute.
You will need a track
for constant experimentation,
a track to move successful
pilots to production
in a repeatable fashion,
and a track to train and scale
and better your
internal expertise.
And lastly, I'd like to say
AI is not a technology.
It's a mindset
of problem-solving,
scientific method,
dealing with uncertainty
and constant
learning and improvement.