Artificial Intelligence Adoption in Service Industries: A Systematic Literature Review of key Drives, Barriers, Challenges, and Strategies
T D C Pushpakumara,
Fazeela Jameel Ahsan
Опубликована: Апрель 7, 2025
Artificial
Intelligence
(AI)
is
reshaping
service
industries
by
automating
processes,
enhancing
decision-making,
and
delivering
personalized
customer
experiences
across
sectors
like
tourism,
healthcare,
finance,
governance.
This
systematic
literature
review
consolidates
findings
from
over
100
studies
to
explore
the
drivers,
barriers,
strategies
influencing
AI
adoption.
While
AI-driven
advancements
such
as
robotic
process
automation
(RPA)
predictive
analytics
enable
efficiency
innovation,
significant
challenges
infrastructural
limitations,
ethical
concerns,
organizational
resistance
hinder
its
widespread
High
implementation
costs,
socio-economic
disparities,
data
privacy
issues
further
complicate
integration
efforts,
particularly
in
underdeveloped
regions
resource-constrained
industries.
To
address
these
study
highlights
targeted
training,
policy-driven
investments
digital
ecosystems,
robust
governance
frameworks.
Additionally,
balancing
with
human
interaction
emerges
a
critical
factor
for
stakeholder
trust
acceptance.
emphasizes
importance
of
interdisciplinary
collaboration
align
technological
societal
goals,
ensuring
that
adoption
fosters
sustainability,
inclusivity,
long-term
growth
Язык: Английский
Adaptive AI Systems for Financial Fraud Detection and Risk Management
Tarun Kumar Vashishth,
A. Chaudhary,
Vikas Sharma
и другие.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 431 - 454
Опубликована: Апрель 8, 2025
This
chapter
explores
the
transformative
role
of
adaptive
AI
systems
in
financial
fraud
detection
and
risk
management.
Leveraging
machine
learning
deep
techniques,
these
dynamically
analyze
vast
amounts
data
to
identify
fraudulent
activities
assess
risks
real
time.
Unlike
static
rule-based
methods,
continuously
evolves
by
from
new
adapting
emerging
tactics,
thereby
enhancing
accuracy
reducing
false
positives.
The
highlights
key
algorithms,
such
as
neural
networks
anomaly
models
that
underpin
systems,
along
with
their
applications
credit
risk,
transaction
monitoring,
compliance.
It
also
addresses
challenges
privacy
algorithmic
bias,
offering
insights
into
future
AI-driven
Язык: Английский
Real-Time Fraud Detection in Serverless Financial Systems Using AI
International Journal of Advanced Research in Science Communication and Technology,
Год журнала:
2023,
Номер
unknown, С. 716 - 721
Опубликована: Ноя. 30, 2023
This
paper
evaluates
the
implementation
of
AI
technology
within
serverless
financial
platforms
while
explaining
how
tools
perform
crime
prediction
and
detection
tasks
examines
advantages
for
reducing
traditional
operational
challenges.
The
study
system
architecture
by
examining
models
get
deployed,
real-time
data
processing
works,
ethical
implications
AI-based
decisions.
Serverless
methodologies
create
perfect
environment
executing
fraud
applications
powered
methods
because
they
eliminate
management
burden
infrastructure
complexities.
Fraud
systems
under
these
architectures
grow
their
resources
automatically
to
maintain
consistent
performance
when
transaction
numbers
increase
or
decrease
during
peak
periods.
Multinational
organizations
use
high-powered
algorithms
explore
large
datasets
identify
abnormal
behavior
that
signals
possible
fraudulent
activities.
become
more
effective
in
spotting
developing
emerging
patterns
through
constant
machine
learning
algorithms.
field
continues
attract
institutions
numerous
areas
where
shows
promise
make
improvements.
combination
faster
regulatory
compliance
better
trading
investment
decisions
forms
part
benefits
achieved
this
Язык: Английский