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From YouTube: Applying AI to critical challenges in financial services: fighting fraud and digital banking
Description
Know your customers—to keep an eye on them or to serve them better. Harnessing AI to streamline data harvesting for deeper insight-driven business decisions and rules helps organizations do both. See our demos that show both use cases based on the same hybrid cloud platform built completely from open source software.
One demo shows how to augment batch and real-time transaction processing with analytics and machine learning models derived from data and then how to integrate into investigations and suspicious activity reporting.
The second demo shows how to maintain a 360-degree view of the customer over various digital channels for communication, payments and any number of transactions by creating event-driven architectures for immediate response to customer situations. Embedding into these architectures AI and ML, and rules-based decision management can dramatically improve the customer experience.
A
A
I'm
my
name
is
maris
bogovich,
I'm
a
chief
solutions
architect
for
financial
services
at
red
hat.
I
work
with
you
know
the
kind
of
the
top
30
financial
institutions
in
u.s
and
canada,
and
particularly
in
the
field
of
digital
transformation,
and
to
help
them
build
ai-based
solutions.
I'm
joined
today
by
my
colleague,
sadananda
kumar,
let
her
a
second
to
introduce
herself.
B
A
Thanks
adena
so
again,
once
again
thanks
everyone,
we're
gonna,
spend
a
very
little
time
kind
of
setting
the
problem
and
then
kind
of
dive
into
the
two
demos
that
we
have
that
we'd
like
to
show
you.
I
just
wanted
to
kind
of
recap:
real,
quick
of
the
context
of
this
conversation
and
you've
probably
heard
that
you
know
ai
is
redefining
success
for
financial
institutions.
What
does
that
mean?
It
means
that
banks
generally
have
relied
on
economies
of
scale
and
have
relied
on
physical
footprint
and
have
relied
on.
You
know
the
relationship
exclusivity
with
customers.
A
You
know
to
kind
of
keep.
You
know
ensure
that
they
have
a
customer
base.
The
larger
you
were,
the
the
more
likely
you
were
to
retain
customers
and
to
succeed,
but
you
know
emerging
technologies
in
a
particular
ai
have
changed
that
dynamic.
Instead
of
you
know
the
scale
of
assets,
it's
more
about
the
efficiency
than
which
you
use
your
data.
Instead
of
you
know
being
able
to
provide
services
at
scale.
It's
how
well
do
you
serve
your
customers
and
how?
A
Well,
you
you
understand
what
they
need
and
in
fact
one
of
the
demos
that
you
will
see
today
is
exactly
coming
into
the
space.
So
no
understanding
this
and
applying
the
artificial
intelligence
cleverly
is
creating
differentiators,
either
by
helping
financial
institutions
work
more
efficiently
or
by
helping
them
capture
and
define
new
ways
of
of
doing
business.
So
you
know
things
and
interactions
are
either.
You
know
the
the
kind
of
the
operational
aspects
are
commoditized
and
made
more
efficient,
but
new
models
of
business.
A
A
new
understanding
of
the
customer
creates
new
opportunities
for
for
banks,
and
this
is
the
main
question:
how
do
they
how
they
are?
How
are
they
efficient?
How
are
they
how
they,
how
enterprises
can
be,
can
do
better?
And
the
starting
point
for
that
and
kind
of
the
key
to
our
demo
today
is
that
yes,
building
models
and
having
the
right
algorithms
for
the
business
is
definitely
a
success.
A
Criterion
is
extremely
important,
but
you
can,
as
you
can
see
in
this
diagram,
that's
as
that's
adapted
from
a
famous
paper
and
published
you
know
called
hide
and
technical
veteran
machine
learning
systems.
The
model
is
just
a
small
part
of
the
whole
system
and,
in
fact,
intelligent
solutions
require
the
proper.
A
So,
in
order
for
that
to
happen,
is
we
need
to
build
like
we
need
to
have
a
way
to
essentially
apply
the
workflow
at
the
top,
like
from
the
setting
goals,
to
getting
preparing
data,
developing
the
machine
learning
model
and
doing
all
these
other
activities
in
a
consistent
manner?
That
brings
everyone
at
the
on
the
same
page.
This
is
you
know
where
you
know
red
hat's
portfolio.
A
A
A
In
this
case,
I'm
going
to
open
the
open
data
hub,
dashboard
and
launch
jupyter
hub
jupyter
hub
will
allow
me
to
create
jupiter
running
jupiter
notebooks,
that
that
will
in
fact
be
the
environment
in
which
the
data
scientists
will
do
their
data
exploration
and
work.
This
is
very
important
by
using
resources
by
using
a
by
using
containers
and
by
using
a
clean
orchestration
platform.
A
A
A
So
the
next
step
is
once
all
everything
is
defined,
to
take
this
notebooks
and
to
execute
them
and
to
create
actually
running
services.
So
in
this
case,
what
I
have
here
is
an
open
shift
pipelines
pipeline
that
takes
these
notebooks
from
their
github
repository
and
creates
and
turns
them
into
a
container
image
that
can
be
deployed
as
a
service
to
serve
in
and
run
the
in
the
model
inference.
A
Let's
start
the
pipeline
run,
it
will
run
for
a
while,
so
I
will
just
kind
of
get
it
started.
I
just
want
to
kind
of
show
you
a
few
of
the
options
here.
For
example,
I
have
a
builder
image
that
does
the
the
encoding
I
have
a
list
of
notebooks,
which
are
which
contain
the
code
that
our
data
scientist
has
developed.
A
I
have
a
two
other
parameters
like
a
base
model
image
that
has
libraries
and
in
everything
that
the
model
image
is
actually
the
target
where
the
kind
of
the
containerized
image
of
the
service
will
build
and
the
source
of
this
of
this
build
process.
So
when
I
start
this
process,
you
can
see
that
you
know
it
will
run
for
a
while.
It
will
run
for
about
10
minutes.
So
we
don't
have
time
today.
A
A
I
I
can
see
that
it
has
applied
the
code
that
was
in
the
notebooks
to
actually
train
this
model
with,
with
with
data
that
was
provided
to
the
build,
it
has
turned
it
into
a
container
image,
and
in
this
case
it,
this
container
image
has
been
deployed
as
a
serverless
service
in
my
in
my
application.
So
this
makes
it
easy,
for
example,
for
for
developers
to
use
the
result
of
the
work
of
data
scientists.
A
That
runs
the
service,
but
also
it
makes
it
easy
for
data
scientists,
for
example,
to
hand
over
the
their
work
to
developers
by
knowing
that
there's
a
process
that
turns
everything
that
they
did,
makes
it
a
container
image
and
and
runs
it
on
the
runs
it
as
a
service,
and
indeed
I
can
see
that
the
result
of
this
is
a
is
an
openshift
serverless
service
that
runs
here
in
can
run
inference.
I'm
gonna
do
a
quick
one,
more
quick
thing
to
show
you
how
this
works
in
this
notebook.
A
A
Quick,
you
can
see
as
we
go
through
the
to
the
different
notebooks,
the
result
of
the
evaluation,
as
well
as
a
few
other
tests.
These
tests
are
important
because
they
cover
the
last
part
of
what
we
want
to
show,
which
is
monitoring
everything
that
the
service
does
is
essentially
published
as
metrics
to
prometheus.
A
If
you
are
following
the
space,
you
know
that,
generally
speaking,
fraudulent
and
legitimate
transactions
have
a
atypical
distribution
right,
I'm
expecting
to
have
far
more
legitimate
than
fraudulent
transactions.
So
when
the
ratio
starts
to
shift,
it
might
be
a
case
where
the
model
has
drifted
is
not
applicable
anymore.
So,
by
monitoring
the
data
in
prometheus,
I
can
actually
see
whether
these
transactions
are
whether
my
model
is
still
applicable,
or
maybe
I
can
start
I'm
required
to
retrain
it
or
re-evaluate
it.
B
B
Personalization
is
a
topic
of
discussion
across
every
industry
today
and
with
the
post
pandemic
era
that
we
are
in.
It
is
more
relevant
than
ever
for
businesses
to
make
sure
that
the
distance
does
not
discount
on
the
services
that
the
customer
deserves,
and
while
the
first
use
case
spoke
about
fraud,
the
second
one
talks
about
how
you
can
improve
the
overall
customer
satisfaction
by
providing
better
digital
experience
to
the
customers,
and
so,
let's
keep
going
here
so
with
the
various
researches
that
are
happening
across
the
globe.
B
B
The
likelihood
of
a
customer
accepting
an
offer
offer
of
a
particular
type
and
the
business
users
discretion
again
when
you're
thinking
about
a
solution
of
this
nature,
something
that's
critical
is
the
ability
to
do.
You
know
traceability
into
the
whole
process
that
happens,
and
so
a
very
high
level
logical
view
all
of
the
customers,
interaction
by
other
various
events,
they're
going
to
come
in
through
different
channels
into
an
even
stream.
B
So
as
soon
as
an
event
occurs,
it's
going
to
look
at
the
behavior
patterns
of
the
customer
and
it's
going
to
respond
back
to
the
customer
in
real
time,
providing
that
offer
right
when
they
actually
need
it
and
when
they
are
best
suited
to
accept
it
and
so
high
level
view
of
the
architecture
here.
So
you're
gonna
have
the
event
stream.
B
So
with
that
overview,
let's
look
at
this
in
action
and
I
just
wanted
to
call
out
that
all
of
the
pieces
related
to
the
data
science
aspect
that
marius
walked
through
in
the
first
demo
is
applicable
here
as
well.
So
it's
the
same
data
science
experience
same
way
that
they
create
the
notebooks,
the
same
flexibility
they
get
with
deployment.
B
B
So
as
soon
as
the
purchase
is
being
performed
here,
you
can
see
that
an
airline
transaction
event
is
put
into
the
event
stream.
This
event
is
then
being
acted
upon
by
our
decision,
incompetent
and
it
generates
an
offer
on
the
fly
in
real
time.
So
when
you
go
back
and
refresh
the
offer
section,
you
can
see
that
they've
received
an
offer
to
upgrade
to
an
airline
car
now.
B
So
in
this
case,
you
had
an
airline
purchase
with
a
customer
who
is
a
platinum
card
customer
and
the
qualified
purchaser
said
she's
predominantly
made
ln
purchases
in
the
past,
and
hence
that
offer
was
extended
to
the
customer
now,
in
order
to
better
understand
this,
we
spoke
about
the
aspect
of
traceability
and
which
is
why
I
think
it's
important
to
talk
about
how
you
can
use
the
event
source
to
do
analytics.
This
again
feeds
back
into
the
data
science
loop
to
make
sure
that
the
models
are
constantly
being
made
better
and
better.
B
You
can
see
that
several
of
our
products
from
our
portfolio
have
joined
together
to
create
a
solution
of
this
nature
and
the
open
data
science
aspect
of
the
concepts
that
you
know
maria
spoke
about
in
the
beginning
of
this
conversation
is
going
to
add
that
data
science
clue
to
the
rest
of
the
application
development
stack
by
making
sure
that
the
intelligence
can
be
put
to
use
when
it
needs
the
most.
So
with
that,
I'm
just
gonna
transfer
it
back
to
marius
for
some
final
thoughts.