International Journal of Economics and Financial Issues,
Journal Year:
2024,
Volume and Issue:
14(2), P. 218 - 233
Published: March 18, 2024
Investigating
the
correlation
between
digital
financial
services,
mobile
money
usage,
and
velocity
in
Ghana,
study
analysed
time
series
data
spanning
from
1992
to
2022.
A
composite
index
was
constructed
by
principal
component
analysis
using
extracted
world
development
indicators,
with
components
of
money.
The
estimation
utilised
an
impulse
response
function
vector
error
correction
model;
results
indicated
that
money,
are
related
both
short
long
term.
Furthermore,
application
a
standard
deviation
innovation
produced
increases
positive
negative
magnitude
for
all
variables.
This
suggests
banking
Ghana
interdependent
asymmetric
manner.
In
order
facilitate
increase
research
concluded
policymakers
should
guarantee
greater
proportion
population
has
access
services.
addition
promoting
online
payment
methods
on
purpose,
government
reduce
reliance
physical
currency
expedite
circulation
It
is
recommended
future
longitudinal
studies
involving
African
nations
employ
diverse
techniques.
GSC Advanced Research and Reviews,
Journal Year:
2024,
Volume and Issue:
18(2), P. 167 - 197
Published: Feb. 14, 2024
As
the
financial
service
sector
rapidly
evolves
with
integration
of
cutting-edge
technologies,
intersection
security
and
privacy
becomes
paramount.
This
paper
delves
into
intricate
landscape
issues
within
sector,
offering
a
comprehensive
analysis
challenges
opportunities
presented
by
emerging
technologies.
From
blockchain
to
artificial
intelligence,
explores
vulnerabilities
inherent
in
these
innovations
consequential
threats
sensitive
data.
Through
an
examination
recent
case
studies,
regulatory
frameworks,
technological
advancements,
this
work
aims
provide
nuanced
understanding
evolving
threat
landscape.
Additionally,
proposes
strategic
solutions
best
practices
fortify
architecture
surrounding
fostering
resilient
trustworthy
ecosystem.
research
contributes
ongoing
dialogue
imperative
safeguarding
systems,
ensuring
that
innovation
aligns
seamlessly
imperatives
confidentiality,
integrity,
availability
era
where
services
advancements
are
inextricably
linked.
Technologies,
Journal Year:
2025,
Volume and Issue:
13(4), P. 141 - 141
Published: April 4, 2025
Bank
fraud
detection
faces
critical
challenges
in
imbalanced
datasets,
where
fraudulent
transactions
are
rare,
severely
impairing
model
generalization.
This
study
proposes
a
Gaussian
noise-based
augmentation
method
to
address
class
imbalance,
contrasting
it
with
SMOTE
and
ADASYN.
By
injecting
controlled
perturbations
into
the
minority
class,
our
approach
mitigates
overfitting
risks
inherent
interpolation-based
techniques.
Five
classifiers,
including
XGBoost
convolutional
neural
network
(CNN),
were
evaluated
on
augmented
datasets.
achieved
superior
performance
noise-augmented
data
(accuracy:
0.999507,
AUC:
0.999506),
outperforming
These
results
underscore
noise’s
efficacy
enhancing
accuracy,
offering
robust
alternative
conventional
oversampling
methods.
Our
findings
emphasize
pivotal
role
of
strategies
optimizing
classifier
for
financial
data.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 281 - 304
Published: Feb. 28, 2025
The
rise
of
consumerism
and
technology
has
accelerated
the
demands
general
public.
With
growth
ecommerce
platforms
there
been
an
exponential
in
rate
trade
that
takes
place
which
was
previously
unimaginable.
need
for
data
security
preventing
fraudulent
activities
now
at
highest
level
scrutiny
than
ever
more
advanced
systems
can
handle
increasing
number
challenges
discussed
our
chapter.
debate
on
whether
traditional
methods
detecting
such
vulnerabilities
have
talked
about
a
while
now.
today
is
system
robust,
adaptive
to
new
trends
also
be
proactive
mitigating
threats.
Artificial
Intelligence
Machine
Learning
are
pivotal
technologies
help
shape
better
robust
systems.
Their
ability
identifying
patterns
thus
outliers
go
hand
with
what
anomaly
or
event
automating
services
without
any
human
intervention.
Electronics,
Journal Year:
2025,
Volume and Issue:
14(9), P. 1754 - 1754
Published: April 25, 2025
This
study
presents
an
optimization
for
a
distributed
machine
learning
framework
to
achieve
credit
card
fraud
detection
scalability.
Due
the
growth
in
fraudulent
activities,
this
research
implements
PySpark-based
processing
of
large-scale
transaction
datasets,
integrating
advanced
models:
Logistic
Regression,
Decision
Trees,
Random
Forests,
XGBoost,
and
CatBoost.
These
have
been
evaluated
terms
scalability,
accuracy,
handling
imbalanced
datasets.
Key
findings:
Among
most
promising
models
complex
data,
XGBoost
CatBoost
promise
close-to-ideal
accuracy
rates
detection.
PySpark
will
be
instrumental
scaling
these
systems
enable
them
perform
processing,
real-time
analysis,
adaptive
learning.
further
discusses
challenges
like
overfitting,
data
access,
implementation
with
potential
solutions
such
as
ensemble
methods,
intelligent
sampling,
graph-based
approaches.
Future
directions
are
underlined
by
deploying
frameworks
live
environments,
leveraging
continuous
mechanisms,
anomaly
techniques
handle
evolving
patterns.
The
present
demonstrates
importance
developing
robust,
scalable,
efficient
systems,
considering
their
significant
impact
on
financial
security
overall
ecosystem.
