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From YouTube: Digital Banking CX: Using Data for Bottom-Line Results
Description
Presented at AI Summit NYC 2020:
Now, more than ever, banking customers are making use of digital channels for communication, payments, and any number of transactions. Maintaining a consistent 360-degree view of the customer is critical to ensure a positive customer experience in this new dynamic. Join us for this demo to learn how to use event-driven architectures, AI and ML, and rules-based decision management to dramatically improve experience with your business.
Presented by Sadhana Nandakumar
A
This
is
sadnanda
kumar
and
I'm
a
senior
solutions
architect
with
red
hat
today,
we're
going
to
be
looking
at
a
use
case,
demonstration
of
how
organizations
can
improve
their
customer
satisfaction
by
rethinking
their
customer
engagement
strategies
with
the
digital
age.
Intelligence
is
everywhere,
and
data
drives
decisions
by
providing
personalized
real-time
service
to
the
customer
based
on
their
needs,
we
will
dramatically
be
able
to
improve
their
loyalty.
A
A
A
A
A
As
you've
noticed,
sarah
is
using
the
card
on
her
file,
which
is
4567
to
complete
this
purchase,
and,
as
this
transaction
has
been
completed,
an
airline
transaction
event
is
being
put
in
into
the
event
stream.
This
event
would
then
be
acted
upon
by
our
business
decision,
incompetent
to
come
out
with
the
right
offer
for
sarah.
A
A
A
A
All
of
your
rounded
rectangles
that
you
see
on
this
diagram
are
inputs
that
come
in
into
the
decision
model.
I
have
color
coded
the
inputs
so
that
we
understand
the
source
of
this
information,
age,
income
and
customers
class
are
all
customer
profile,
related
information
that
comes
from
the
customers,
databases
or
data
stores.
A
All
of
this,
in
addition
to
your
customer
segmentation,
which
is
a
machine
learning
model
that
I
introduced
you
to
earlier
on,
is
going
to
go
on
to
determine
the
offer.
So,
as
you
can
see,
the
customer
segment
model
is
a
random
forest
classifier
model.
We
are
pulling
in
this
trained
model
and
we
are
using
it
as
a
part
of
our
decision
graph
so
that
we
can
determine
the
best
offers
for
the
customer.
A
Now,
once
this
model
has
been
created
by
the
machine
learning,
scientist
has
been
trained
against
the
production
data.
We
are
trying
to
use
that
trained
model
as
a
part
of
our
evaluation
of
the
row
so
coming
back
here,
you
can
see
that
all
of
this
information
is
going
to
go
on
to
determine
the
offer
all
right.
I
understand
that
the
decision
model
kind
of
governed
the
offer
that
was
extended
for
the
customer,
but
one
thing
that's
still
missing
is
I
don't
quite
understand
what
really
happened
with
that
particular
transaction
that
sarah
performed.
A
A
A
She
had
a
customer
classification
of
platinum,
the
age,
distribution
and
income
distribution
fell
in
this
particular
section
from
her
historical
purchases.
We
knew
that
she
had
performed
an
airline
transaction
even
predominantly
in
the
past.
So
finally,
you
can
see
that
her
customer
predictive
profile
also
came
back
high.
A
So
what
would
have
been
a
black
box
with
respect
to
the
prediction
results
created
by
your
machine
learning
model
is
now
being
exposed
via
the
decision
layer,
so
there's
better
control
on
what
can
be
extended
for
the
customer.
So
this
overrides
that's
put
on
top
of
the
intelligence
that
is
coming
from
your
historical
model
and
your
predictive
model
that
kind
of
determines
the
offer
for
the
customer.
A
So
now
we
clearly
understand
what
exactly
happened
for
that
particular
use
case.
So
when
you
go
back
and
read
the
decision
model
again,
we
had
an
airline
purchase
that
was
done
by
sarah.
She
was
a
platinum
card.
Customer
her
customer
segmentation
fell
in
high.
Her
qualified
purchases
said
she
has
predominantly
made
airline
purchases
in
the
past,
and
so
she
was
extended
this
offer
to
upgrade
to
an
airline
car,
so
we
are
able
to
understand
completely
end-to-end
what
happened
with
this
particular
use
case.