Welcome to the first episode
of Technology Powered Growth,
a podcast presented by Ping An.
I’m your host, Treena Nairne.
Today, I’m speaking with Michael Guo,
Executive Director and Co-CEO of Ping An Group,
and Dr. Xiao Jing, Group Chief Scientist.
We’ll talk
about how Ping An leverages technology to drive
business growth.
Ping An’s integrated finance
plus health
care and senior care strategy is unique
for a financial services company.
It's what stands out globally. Yes.
So, Michael,
how would you describe Ping An's
competitive difference in the market?
Yeah, well, that's a good question.
Ping An is a big conglomerate
of the financial services.
It's one of the largest
financial institutions in China
and in the world.
I believe a good strategy should be in line
with the mega trend of the overall economy
and should be centered
around the customer needs.
So that's exactly what we're doing.
If you look at Ping An, we have different
financial services in banking,
insurance, investments,
securities, et cetera.
Then we have a second part of the arm,
which is around health care and senior care.
So if you look at the overall China's economy
for the past 20,
30 years,
there has been rapid financial growth.
Now through that process, Chinese families
and individuals have accumulated
a significant amount of wealth.
So when we talk about middle class in China,
there are now about one-third of the people,
individuals
or families, are called middle class.
Now if you look at a typical Chinese family,
for example, a couple with perhaps
two children,
maybe four parents
to support, these days
they have multiple credit cards,
a number of insurance
policies, a number of cars, perhaps a mortgage.
Their financial needs are becoming
more and more sophisticated and complicated.
So one of the key
competitive differences of Ping An
is that we're able to integrate our side
of the financial services
and provide a one-stop
solution for our customers
to make them hassle-free
without having to deal
with all these different
sophisticated, more
and more sophisticated financial products.
So that's one side of
our strategy and differentiator.
The other side is that
we're looking at the overall
long-term growth and demographic
change of China's population.
Now in five years’ time, roughly
25 to 30 percent of the Chinese population
is becoming more than 65 years old.
With that,
they have more and more
diversified needs on the health care
and the senior care part of the services.
And for our customers,
who are roughly middle-class
families and individuals,
they would prefer to have some high-quality,
perhaps differentiated, tailored services
in addition
to the social welfare systems
provided by the government.
So from that perspective,
we're building up our capabilities around
health care
and the senior care
and provide a tailored solution to them
based on our strong financial resources.
So from that perspective,
we are actually tailored to
our customer needs to the future.
So that's how we see ourselves.
One side of the integrated finance solution
to make sure
we satisfy our needs,
our customers' needs and the moment, but also
we gear toward
the needs for them for the future.
So tell us a little bit
about how technology feeds
into that overall strategy.
Because one of the key
things of all
these things has to be integrated,
and thanks to our Ping An’s
proprietary technology
and the data
we have accumulated
for the past 10, 15
years, we’re able to actually provide
much more integrated
and, on the other hand,
tailored solutions
to our individual families and customers.
And through the past 10,
15 years of investment,
we have accumulated a massive amount
of the customer data,
not just on the financial side of it,
but also end-to-end of the customer
journey across different channels.
These days, you’ve got internet,
you’ve got apps,
you’ve got offline, you’ve
got online, you’ve got social media.
Then you’ve got the behavior data
accumulated from our ecosystem.
And all this data has to be
integrated together
so we're able to actually
use the data
to provide more tailored
solutions to our customers.
And that becomes available and possible
thanks to our technology
and, these days,
AI advancements.
And, of course,
AI is the hot topic for everybody this year.
So tell us a little bit
more about the specifics
of how
AI actually helps Ping An
stand apart from competitors.
Well, if you look at the AI,
it's just part of the technology.
It’s just these days
the technology becomes
more and more sophisticated.
In Ping An,
when we look at technology,
our strategy is always around
how do we use technology, the AI in particular,
add value to our services, to our customers,
to our business, to our management.
So, in general,
if we look at the technology and how we see it,
there are perhaps
a few areas
the AI and technology is able to add value.
It's around cost efficiency, it's
around the productivity,
it's around the risk management.
So all these things
come together
to become one of the driving forces
for our profitability of our business.
That's why,
if we look at Ping An,
we are one of the most profitable
financial institutions
in the market in China
and also according to
global standards as well.
Now we're going to come back
and talk about some specific examples
of what Ping An
is actually doing with technology.
But from what you're saying,
Ping An's
offering really comes
through the scale of your business,
the depth of customer data,
and I understand
from what you're saying that actually Ping
An had a very early vision
of digitalizing
the business beyond that
traditional financial services model.
So what have been the challenges in delivering that?
You mentioned the scale.
This is actually one of the key challenges.
On the one side is our strength.
We got 240 million customers.
We got one
third of the market share, across the 2000.
The more than 2000 cities in China.
So that scale gives us very strong
financial strength.
But on the other hand,
when we talk about tailored
personalized solutions,
scalability versus personalization,
standardization quality versus cost efficiency,
those are the kind of competing
and challenging factors
that we have to be able to manage.
So, again, coming back, the technology
plays a key role in balancing all these kind of
competing and conflicting factors
through our service providing process.
So one of the things we actually started early on
is to standardize all the operational processes
across all the services,
financial services side of it,
health care, senior care side of it.
Now, through the standardization
of the operational processes,
we're able to generate,
on the one hand, sufficient cost efficiency.
On the other hand,
we're able
to actually
make a very good standard of the qualities
of the services.
Now, with that being the backbone
and the foundation,
using all the data
we have accumulated, using these days
much more advanced tools and data analytics,
we're able
to actually tailor-made
the solutions to our individual customers
in different cities.
So that’s why when we look at this balancing
kind of act among all the competing factors,
using the technology to standardize everything,
but making sure we use the data and the tools
to tailor-made some of the solutions
to different customers,
that's how we look at
and how to actually make sure we satisfy
the customer needs
with different individuals,
but with sufficient level
for quality
and cost efficiency.
Now, Jing,
as the Group Chief Scientist,
could you give us some insight
into Ping An's technology framework?
So how do you prioritize?
Where do you invest those technology efforts?
We have been developing our own technology
completely for well over a decade.
So we have a tremendously rich financial
and medical data sets,
which we have checked throughout that time.
Today we have a strong enhanced technology team
of more than 3,000 scientists and 20,000 IT engineers
whose work is organized in what we call our 953 framework.
953 is a shorthand for our nine
global leading databases,
plus five
laboratories and three technology companies.
Currently we have accumulated over 55,000
patent disclosures, leads globally in fintech
and health tech, and also second in generative AI.
We've just talked through a lot of numbers
there, so let's break that down a little bit.
You've talked about your 953 framework, so nine
global leading databases,
five laboratories, three technology companies.
Can you walk us through each one of those?
Yeah, of course.
Basically, the nine databases
we have consist of three different categories.
One is financial databases,
another is medical and health care,
and the third one contains the data
from the internal business
operation and management
in Ping An.
For the financial databases,
we have three different categories of data,
including individual customers, corporate
customers, and financial products.
We process
more than one billion records per day, serve
for more than 240 million individual customers
to generate
deep insights
into customers’
needs to improve the user experience.
For the medical and health care databases,
we have the information from five categories,
respectively regarding disease
and the symptoms, prescriptions, medicines
and medical equipments, doctors and hospitals,
and customer health records.
The third is our internal business
operation and management database.
So hold on there for a second.
Let's go back.
One billion records per day.
That is impressive. YUh-huh. Yeah.
Thank you. Yeah, that’s pretty big.
But that's not all.
We have five laboratories that focus
on developing our capabilities
in different AI areas,
including computer vision, speech,
and natural language
understanding, data
analytics, micro-expressions,
and the Silicon Valley Lab,
which is targeted at cutting-edge technologies development.
By the way, we are ranked number one
in terms of fintech,
health tech patent disclosures.
Now, I think most people have an idea
of what data analytics is about,
but computer vision research
is about using AI to interpret images, right?
Yeah.
Speech and NLP or natural language
processing is about computers
being able
to interpret and generate human language,
and micro-expressions That’s about computer
interpretation of tiny facial muscle movements
that reveal human emotions,
which I think is absolutely fascinating.
So those, along with research
on cutting-edge technologies,
make up your five labs, right? Yeah.
So what does the three represent?
The three represents our three tech companies,
Ping An Technology, Ping An Health,
and One Connect Financial Technology.
These are the companies that develop
digital solutions
based on the AI research from our labs
and our data,
covering thousands of diverse
business scenarios
with hundreds, millions of large language
model calls per day.
These have given us strong
AI technologies foundations
for unique attributes,
especially with our rich data sets,
which we collected from real cases
among our more than 240
million retail customers in China.
Just to give you an idea
how much of data we have,
our public
domain text is made up of approximately
3.2 trillion tokens, and approximately
310,000 hours of labelled speech,
and more than 7.5 billion images.
We use this immense data
to create our unique ecosystem.
Yeah, all these are big numbers.
So you can see we have, we always say
we're sitting on a gold mine of data,
but that didn't really come overnight.
Ping An started
building our data starting around
15 years ago.
One of the key
things we've done is to standardize
all the operational processes
so that we’re able
to capture the customer's data
from channel to channel, end to end.
So it's not just about their transaction
data.
It's also about the behavior
data, how they move from app to internet
banking to offline sales channel.
Then coming back to the call center.
So one of the key things we're able to actually
leverage is the different customer
end-to-end data
across the different channels, behavior data,
but also with advancement
of the analytics engine
and technology and algorithm,
we're able to generate the tools
and analytical engines to be able to interpret
those data
and generate good insights for our customers
and for our sales agents.
So that's actually very important
to add value to our business.
So every day as you're adding that data,
it becomes harder and harder to replicate
isn't it.
It's not like
someone could just walk in
and copy what you do.
Yes, absolutely.
So Michael,
could you
give us an example
of how AI actually impacts the customer today?
Sure.
If you look at our customer's interactions with us,
the one of the key channels is call center.
Now we have 240
million retail customers
these days across different businesses,
and every day
there are more than 2 million
incoming calls into our call center,
and our call center is integrated.
It doesn't really matter
if you’re a
credit card customer or you’re
an insurance customer.
They all go to the same call center,
and the calls are answered
by the same group of agents.
So that's a challenge
because if you look at the incoming
insight,
people can make calls from all sorts of places,
on the car, in the train station,
maybe in the farm.
So we have to be able to actually filter out
all the background noises
for the machine these days to understand
and interpret people's voice and their calls.
Also, China is a diversified country.
We have people from different regions
speaking different dialects,
and that actually, for the machine,
is very difficult to tell, okay,
this person is talking Cantonese,
and the other person is talking
Sichuan dialect.
It's very different.
So that requires very strong capability,
voice recognition,
natural language processing,
all this advanced technology
to be able to actually understand
the customer's voice
and what they're trying to talk about.
So that's only one side of the challenge.
The other side of the
challenge is with this multimedia,
with multi-channel kind of service capability
we have,
and not just about call center coming
in, people could say, oh,
I just put some information on my app
or internet banking,
and now I want to check on the process.
So our call center agents
will have to be able to capture
the information straightaway, almost real time,
from the information
through internet
banking or app
and integrate it into their answers
to the customers
and say, okay,
that's what you have asked for,
and this is what we are going to do for you.
And all this requires analytical
AI technology
to make all these things possible.
So before,
if you think about it,
all these things
would have to be handled by humans.
But these days, with advanced technology of AI,
we're able to actually reduce our call center
agents’ capacity
by 50%, number of headcount by 50%.
But at the same time,
we're still able to answer the customer calls
with increasing volume, with
standard high-level quality.
These days, every call we require
would have to be picked up
until three beeps, within three beeps.
And that has always been the case.
We're able to maintain
the standard of our quality
and improve the efficiency
with fewer and fewer people.
Actually, it sounds like with fewer people
you're actually able
to increase the standard of quality.
Yes, that's true, that's true.
Jing, how else is Ping An using AI?
Besides financial services,
medical and health
care are another strategic direction
of Ping An Group.
We launched
a self-developed AI-assisted diagnosis system,
AI-Doctor, as far back as 2018,
and has undergone
continuous updates and enhancements
to develop
into a comprehensive end-to-end solution
from pre-diagnosis,
treatment, and post-recovery phases.
Today, this ecosystem
encompasses collaborations with nearly 4,000
hospitals, over 50,000 medical professionals,
and more than 150 senior care services
providers nationwide.
The system now covers diagnostic expertise
for over 2,000 most common diseases,
achieving the navigation
accuracy of existing 99%
and the diagnosis precision of above 95%.
It is absolutely incredible
how many AI models you're working on.
You must need immense data inputs to feed them.
So how does Ping An actually manage
all of these different models?
Our core technology
foundation is called Ping’an Brain,
which is the fundamental engine
efficiently and effectively empowering
the group's various business units to advance
and scale up their
AI business solutions
across enormous scenarios.
The Ping An Brain
engine consists
of three layers of platforms.
The first layer is computing power platform,
which manages and optimizes
the massive GPU and CPU servers clusters
to support the computing power requirements
of AI model training
and inference applications.
The second layer is data platform,
incorporating key processes
such as data collection,
merging, cleansing,
labeling, storing, quality control, and so on.
The thousands of data clusters
in our data platform
have accumulated
tremendous amount of high-quality data
in the domains of financial
and medical services, both offline and online.
This data
not only help on business,
such as better strategic decision making,
but also create
the data free
will to continuously produce high-quality
AI models.
At the meantime, we always take data
security and privacy protection
as the highest priority.
It is the red line that cannot be violated.
Thus, we have developed comprehensive policies
and systems for extremely rigorous
privacy protection and data security,
covering the finest activities
in the data platform.
In addition to the monitoring, inspection
and protection systems,
we specifically developed
the 4-stack Privacy Computing Systems, BEEHIVE,
which allows different organizations
to share their data efficiently and effectively
in a compliant and secure manner.
he third
layer is AI platform,
which consists of three sub-layers.
The first is the model layer,
which stores and well-manages all the models
we have trained
and tested in the past,
covering a wide range of AI areas,
such as large-range
models, computer
vision, speech
recognition and synthesis, data
analytics, micro-expression, and so on.
The model number is innumerable.
Specifically for large-range models,
we constructed
three-tier architecture,
general-purpose large-range
models, domain-specific large-range models,
and specialized scenario large-range models.
The second layer provides
all the model
training,
inference, operation and maintenance tools
and systems.
Only with the strong support of these tools,
all the models in the model
layer can be produced, utilized and maintained
at such a high standard quality and efficiency
at a large scale.
The third sub-layer is a platform
providing agentic capabilities,
which allows every member of the Ping
An group
to build their own agents freely, efficiently
and effectively,
without the need of a technical background.
Wow, I mean, Ping An Brain,
Beehive, Robot Assistants, it sounds very,
very exciting,
but this isn't science fiction, right?
This is how Ping An's business
is transforming right now.
So, how about some specific examples
about how Gen AI applications
are supporting Ping An's businesses?
Well, let me just give you an example,
an application that we all use every day.
We have this app called Ask Bob,
which is essentially an AI-powered tool.
So the interaction model
is very much like these days
you have with chat GPT, for example.
You ask Bob a question,
then Bob will come back with some answers,
and those answers are generated
based on the massive data
that we have accumulated
and also the knowledge base
that we have built up
internally based on our proprietary data
in the financial services area, in the health
and the senior care
area, around medical, hospital prescriptions,
et cetera, et cetera.
So that, powered by the Gen
AI technology,
we're able to make Bob more like a human-like.
It’s almost like an AI assistant.
This is actually very interesting
and especially helpful
for our Salesforce people.
Now, when they interact with the customers,
Bob is able to come up
with the first round
of proposals, for example,
based on different customer
needs
and their existing current
financial situation.
So that significantly improves
our sales capabilities and their expertise
and also the efficiency.
So now we've seen very good
uplift of our sales agents’ productivity,
thanks to this Bob.
And AI and Gen
AI is really starting
to come into everybody's daily life now.
We've heard so much about open source
gen AI in recent months.
Things such as Deep Seek.
So is Ping An using
those kinds of technologies
in its products and services, Jing?
Yeah.
As we discussed earlier, our large-language
model platform has a three-tier architecture,
general-purpose large-language
models, domain-specific large-language models,
and a specialized scenario
large-language models.
And yes,
we do have incorporated
DeepSeek models into the first tier
as one category
of the general-purpose large-language models.
Of course, the DeepSeek-backed
large-language models
do not behave better in all the scenarios.
For example, in some scenarios,
the tasks are relatively simple,
where slow thinking is not necessary
and fast thinking is good enough.
Then DeepSeek-type
models are not the right choice
due to the higher cost and possibly worse result.
In fact,
we found that
only less than 40% of our existing AI solutions
could be improved
by using the DeepSeek-backed models.
On the other hand,
language models such as DeepSeek is only one
kind of generative AI models
which targeted at text data.
In practice,
there are other modalities of data
that need
to be understood or analyzed,
such as images, audios, and videos.
Therefore, we are also developing generative
AI models
targeting at other modalities
and multiple modalities together.
Ping An Brain Engine organizes
and operates
these generative models
systematically to help achieve
better business values.
It sounds like
there is an incredible amount of work
happening behind the scenes.
But if we can zoom out a little bit, Michael,
can you tell us a little bit
about how Ping An actually assesses
ROI on technology investments?
Yeah, well, that's a good question
because technology can be very costly
as these days we all heard about,
you know,
big companies pouring
billions of dollars onto technology, onto chips
and onto a computing power. So are we.
As one of the largest financial institutions
in the world,
we invest in technology
with billions of dollars every year.
But being a financial institution,
we are very much focused
on getting value out of our investment.
So we usually take a very disciplined
and value-driven approach
looking at our technology investment.
In general,
we put our technology investment
into three buckets.
The first bucket is what we call must-have.
So those are the investments
that go into, say, technology infrastructure,
the data centers,
the architecture of the new,
next-generation architecture
for data, et cetera.
So those are the things that we believe
are fundamental
that would give us competitive advantage
for the long term.
And those things, we’re
just putting money
without looking at the return in general.
Now, the second bucket is specific investment
into applications, into use cases, et cetera.
And there, we're very much
focused on the ROI.
We have to look at
what kind of a return
on the business
impact or value creation
from that particular project
and what kind of ROI that we could qualitative
and quantitatively measure.
And then we have a rigorous process
to check and making sure
the expected return are able to be coming back.
The last bucket is what we call
innovation per se.
Those are the kind of trial and error exercises
where we say every year
we allocate a fixed amount of money
into those experiments,
we're willing to put in money and we're willing
to, you know, quote unquote,
waste those money if we didn't succeed.
But because of that, here
and there, now
and then, we're able to actually
get breakthrough into some of the new
cutting edge technologies.
So that's how we measure and manage
our technology investments.
Michael, Jing,
thank you so much for your time today.
It's been great to gain
all of these insights
about what's happening with Ping An’s,
technology foundations and their use of AI.
Thank you so much.
Thank you for having us. Been a pleasure.