With
the
continuous
advancement
of
digital
transformation,
payments
are
playing
an
increasingly
important
role
in
financial
industry.
This
study
aims
to
utilize
machine
learning
models
predict
and
analyze
payment
behavior.
Initially,
background
significance
sector
introduced.
Subsequently,
current
status
trends
traditional
distribution
reviewed,
alongside
related
work
on
behavior
prediction.
Methodologically,
principles
applications
such
as
logistic
regression,
decision
trees,
random
forests
elaborated,
along
with
experimental
design
data
preprocessing
methods.
The
results
discussion
section
illustrates
performance
each
model
prediction
explores
their
impact
credit
decisions.
exploration
equips
institutions
more
effective
user
analysis
risk
management
tools,
thereby
fostering
future
development
application
technologies.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: April 23, 2025
Mathematical
tools
are
crucial
for
dealing
with
uncertainty
because
they
provide
a
rigorous
and
logical
framework
evaluating,
measuring,
making
decisions
in
the
presence
of
ambiguous
information.
The
bipolar
complex
fuzzy
is
one
mathematical
methods
simultaneously
handling
dual
aspect
second-dimensional
Thus,
this
script,
we
propound
aggregation
operators
"partitioned
Maclaurin
symmetric
mean
partitioned
mean"
within
set
that
mean,
weighted
operators.
We
also
related
axioms
invented
By
employing
deduced
operators,
produce
technique
multiattribute
decision
sets
to
overcome
awkward
uncertainties.
After
that,
demonstrate
an
explanatory
example
revealing
significance
practicability
theory
then
analyze
reliability
legitimacy
propounded
by
comparing
them
some
prevailing
work.
With
the
continuous
advancement
of
digital
transformation,
payments
are
playing
an
increasingly
important
role
in
financial
industry.
This
study
aims
to
utilize
machine
learning
models
predict
and
analyze
payment
behavior.
Initially,
background
significance
sector
introduced.
Subsequently,
current
status
trends
traditional
distribution
reviewed,
alongside
related
work
on
behavior
prediction.
Methodologically,
principles
applications
such
as
logistic
regression,
decision
trees,
random
forests
elaborated,
along
with
experimental
design
data
preprocessing
methods.
The
results
discussion
section
illustrates
performance
each
model
prediction
explores
their
impact
credit
decisions.
exploration
equips
institutions
more
effective
user
analysis
risk
management
tools,
thereby
fostering
future
development
application
technologies.
