Use Cases of Machine Learning in Finance

The
changing
definitions
of
customer
experiences
in
the
domain
of
financial
services
have
led
to
growing
demand
for
better
solutions
in
financial
analysis,
forecasting,
and
financial
engineering.
With
the
rising
adoption
of
machine
learning
in
finance,
it
is
important
to
check
how
you
can
use
ML
to
improve
financial
services.
Some
of
the
popular
financial
institutions,
including
banks
such
as
JP
Morgan
and
investment
funds,
have
embraced

AI
in
their
business
operations.

As
a
matter
of
fact,
around
70%
of
all
financial
services
institutions
use
machine
learning
in
some
form.

Machine
learning
can
serve
the
domain
of
finance
with
different
applications
that
can
help
with
optimization
of
processes
in
different
departments
and
types
of
businesses.
Let
us
take
a
look
at
some
of
the
most
popular
use
cases
of
machine
learning
for
financial
services. 

How
Can
You
Use
Machine
Learning
in
Finance?

Machine
learning
is
a
broad
subdomain
in
computer
science
that
helps
computers
learn
from
data
without
the
need
for
special
programming.
It
has
emerged
as
a
significant
tool
for
improving
processes
and
systems
in
different
sectors,
such
as
healthcare,
retail,
and
manufacturing.
The
search
for
answers
to
queries
like
“How
is
machine
learning
used
for
finance?”
draws
attention
to
capabilities
of
machine
learning
to
serve
different
areas,
including
social
media
communications
and
marketing.

Therefore,
it
is
easy
to
imagine
how
machine
learning
can
transform
the
world
of
finance.
For
example,
machine
learning
can
empower
automation
in
finance
with
models
for
credit
risk
predictions.
Banks
can
use
credit
risk
prediction
models
to
determine
the
possible
risks
of
certain
lending
decisions. 

Machine
learning
can
also
serve
as
a
useful
tool
for
improving
finance
by
recommending
suitable
financial
products
at
the
right
time.
The
use
of
machine
learning
in
finance
can
help
banks
determine
which
customers
they
must
approach
for
their
new
services.
As
a
result,
banks
can
ensure
better
management
of
their
service
portfolio
alongside
reducing
costs
through
automation
of
repetitive
processes.

Machine
learning
models
also
help
improve
asset
management
and
trading
decisions
by
analyzing
big
data
sources.

Take
your
first
step
towards
learning
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artificial
intelligence
through AI
Flashcards

Notable
Facts
about
the
Use
of
ML
in
Finance 

Before
you
discuss
the
real-life
use
cases
of
machine
learning
for
finance,
it
is
important
to
learn
some
facts
about
ML
in
finance.
Statistics
about
machine
learning
in
finance
industry
can
help
in
understanding
how
machine
learning
has
matured
into
a
trusted
tool
for
transforming
financial
services.
Research
by
the
World
Economic
Forum
and
Cambridge
Centre
for
Alternative
Finance
has
revealed
that
around
56%
of
financial
services
companies
have
embraced
machine
learning
for
risk
management.

The
banking
sector
has
gradually
increased
the
adoption
of
machine
learning
technology.
Around
75%
of
executives
at
banks
with
more
than
$100
billion
in
their
assets
have
reported
that
they
are
working
on
adoption
of
AI
and
ML
technologies.
On
top
of
that,
48%
of
US
bank
executives
believe
that

generative
AI
can
help
in
empowering
customer-facing
virtual
assistants
and

chatbots.
Therefore,
it
is
important
to
discuss
the
opportunities
for
machine
learning
in
the
financial
services
industry. 

Machine
learning
can
serve
as
a
powerful
resource
for
making
sense
of
large
collections
of
data
by
discovering
important
patterns.
The
insights
from
machine
learning
systems
can
help
enhance
business
processes,
serve
prediction
tasks,
and
make
informed
decisions.
Let
us
take
a
look
at
some
of
the
top
applications
of
machine
learning
in
the
domain
of
financial
services.

• 
  Financial
  Monitoring 

The
most
important
machine
learning
use
case
in
finance
is
financial
monitoring.
Machine
learning
can
help
in
enhancing
tools
such
as
investor
dashboards
and
budgeting
apps
that
are
the
best
picks
for
monitoring
financial
health.
In
the
domain
of
finance,
financial
monitoring
is
also
referred
to
as
personal
capital
management.

Financial
monitoring
tools
help
financial
advisors
track
their
expenses
and
guide
people
toward
their
financial
goals.
The
accuracy
of
machine
learning
can
improve
financial
monitoring
tools
and
notify
users
about
discrepancies
in
their
budget
planning.
Such
tools
can
help
create
recommendations
to
ensure
adjustments
to
individuals’
financial
planning
methods.

Identify
new
ways
to
leverage
the
full
potential
of
generative
AI
in
business
use
cases
and
become
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expert
in
generative
AI
technologies
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Skill
Path

• 
  Automation
  of
  Financial
  Services

Different
companies
leverage
automation
with
machine
learning
for
cost
reduction.
Financial
institutions
such
as
banks
can
have
an
application
program
interface
that
can
generate
new
account
applications.
With
machine
learning
in
finance,
the
API
team
can
automate
their
workflow.

As
a
result,
they
could
focus
more
on
tasks
such
as
interacting
with
customers
and
providing
advice.
Automation
also
helps
reduce
paperwork,
which
is
a
major
goal
for
different
financial
firms.
According
to
Reuters,
banks
would
spend
billions
of
dollars
every
year
on
compliance
activities
and
paperwork.
Financial
advisors
can
also
use
machine
learning
to
avoid
mistakes
in
manual
data
entry
tasks. 

• 
  Data
  Management 

The
applications
of
ML
in
finance
also
involve
data
management.
Data
management
is
a
crucial
requirement
in
the
financial
services
sector
as
it
helps
collect,
store,
and
segment
data
for
better
analysis.
The
use
of
machine
learning
in
finance
can
help
in
enhancing
data
management
to
ensure
effective
monitoring
of
volatility
in
financial
markets.
For
example,
ML
systems
can
monitor
data
about
all
transactions
by
a
firm
to
recognize
patterns
or
trends
that
may
indicate
malicious
activities.

• 
  Trading
  Assistance 

Trading
strategies
used
commonly
by
firms
have
a
major
effect
on
efficiency
and
costs.
The
applications
of
machine
learning
systems
can
offer
new
prospects
for
creating
trading
strategies
based
on
algorithms.
You
can
find
examples
of
‘How
is
machine
learning
used
in
finance?’
in
the
algorithmic
trading
tools.

The
algorithms
can
automatically
purchase
and
sell
on
your
behalf
according
to
market
conditions.
As
a
result,
trading
firms
can
prevent
placing
trades
that
cannot
offer
the
desired
profits.
Algorithmic
trading
strategies
have
emerged
as
a
popular
alternative
to
ensure
efficient
management
of
risk
alongside
generating
more
returns. 

• 
  Loan
  Approval

Financial
institutions
have
to
deal
with
a
lot
of
paperwork
in
the
process
of
loan
approval.
The
manual
process
involves
a
review
of
loan
applications
to
make
decisions
for
approval.
It
can
be
a
costly
and
time-consuming
process.
However,
loan
approvals
provide
the
scope
for
a
popular
machine
learning
use
case
in
finance
with
new
ML
systems.
The
new
systems
can
ensure
automatic
review
of
loan
applications.

On
top
of
it,
machine
learning
can
also
make
recommendations
rather
than
just
provide
approval
or
deny
loan
applications.
It
helps
save
time
and
ensures
approval
of
loans
for
people
who
would
pay
back
the
loans.
As
a
result,
ML
algorithms
can
help
in
lowering
the
risks
for
lenders.

• 
  Fraud
  Detection 

Machine
learning
systems
can
also
help
financial
service
firms
detect
fraud.
The
traditional
methods
involved
rule-based
systems
that
used
predefined
criteria
to
point
out
malicious
transactions.
However,
the
uses
of
machine
learning
in
finance
industry
have
been
evolving.
As
a
result,
ML
systems
can
offer
faster
data
analysis
with
better
accuracy.

On
top
of
it,
machine
learning
systems
can
also
identify
anomalies
and
patterns
that
indicate
potentially
malicious
activities.
For
instance,
Amazon
Fraud
Detector
by
AWS
uses
ML
algorithms
to
comprehensively
analyze
transaction
data
and
predict
the
possibility
of
fraud
risk
in
real-time.
Therefore,
machine
learning
can
help
financial
institutions
reduce
operational,
reputational,
and
financial
risks. 

• 
  Regulatory
  Compliance 

Compliance
is
an
integral
aspect
of
the
financial
services
sector.
However,
it
is
difficult
for
financial
institutions
to
stay
updated
with
new
changes
in
regulations
across
different
countries
and
jurisdictions
within
a
country.
Failure
to
comply
with
regulations
can
lead
to
penalties
and
loss
of
reputation. 

On
the
other
hand,
machine
learning
algorithms
can
help
identify
potential
violations
in
regulatory
compliance.
ML
systems
can
also
create
reports
for
regulatory
agencies
to
prove
that
financial
institutions
follow
all
the
required
regulations. 

• 
  Churn
  Prediction 

Another
prominent
case
of
ML
being
used
in
finance
is
churn
prediction.
It
helps
in
checking
out
which
customers
would
leave
a
financial
services
firm
or
platform.
Such
applications
of
machine
learning
in
finance
involve
churn
tracking,
which
helps
identify
potential
areas
for
improvement.

For
example,
financial
services
firms
can
offer
better
training
to
their
advisors
and
improve
their
customer
experiences.
On
top
of
it,
churn
prediction
also
helps
develop
a
better
understanding
of
customers
and
figure
out
potential
ways
to
prevent
churn.
With
the
right
ML
tools,
advisors
can
also
identify
the
clients
who
are
likely
to
leave
and
offer
useful
advice
to
them. 

Understand
the
actual
potential
of
AI
and
the
best
practices
for
using
AI
tools
with
the AI
For
Business
Course.

Final
Words      

The
use
of
machine
learning
in
diverse
areas
of
the
financial
services
sector
provides
new
opportunities
for
transforming
finance.

Machine
learning
is
a
powerful
innovation
that
can
elevate
the
world
of
finance
to
the
next
level.
However,
the
use
of
machine
learning
in
finance
must
follow
certain
best
practices
and
standards
to
achieve
the
best
results.

Some
of
the
prominent
uses
of
machine
learning
systems
in
finance
include
risk
management,
data
management,
and
algorithmic
trading
and
churn
prediction.
With
the
right
training
approaches,
you
can
find
new
ways
to
transform
the
world
of
finance
with
machine
learning.
Find
more
information
about
the
other
use
cases
of

ML
algorithms
in
finance
and
discover
how
machine
learning
can
serve
as
a
transformative
force
for
finance.