Artificial Intelligence in Accounting
IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 75 - 102
Published: April 18, 2025
Artificial
Intelligence
(AI)
is
transforming
accounting
by
automating
tasks,
improving
data
accuracy,
and
enabling
predictive
analytics.
These
advances
enhance
efficiency
support
strategic
decision-making,
but
also
raise
ethical
risks,
regulatory
uncertainty,
skill
shifts.
This
chapter
examines
AI's
impact
across
auditing,
compliance,
reporting,
advisory,
highlighting
its
role
in
value
co-creation
through
collaboration
between
technology
human
judgment.
Examples
from
firms
like
PwC,
Deloitte,
EY,
KPMG
show
how
AI
enhances
transparency
stakeholder
engagement.
Using
the
lens
of
Service-Dominant
Logic
(Vargo
&
Lusch,
2004),
reframes
as
a
participatory
system
where
co-produced
professionals,
clients,
systems,
regulators.
It
stresses
importance
governance,
explainable
AI,
upskilling
to
ensure
responsible
adoption.
Ultimately,
accountants
are
portrayed
not
passive
users,
co-creators
digitally
enabled,
ethically
aligned
ecosystems.
Language: Английский
Navigating Augmentation and Automation Paradox: Evidence from the UK Algorithmic Trading Industry
Published: Jan. 1, 2025
Language: Английский
AI challenges conventional knowledge management: light the way for reframing SECI model and Ba theory
Journal of Knowledge Management,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 28, 2025
Purpose
Nonaka’s
SECI
(Socialization-Externalization-Combination-Internalization)
model
and
Ba
theory
have
been
dominant
frameworks
in
knowledge
management
(KM)
for
decades,
but
less
attention
is
given
to
their
revolutionary
changes
the
era
of
human-intelligence
interaction.
Thus,
this
study
aims
explore
profound
impact
artificial
intelligence
(AI)
on
conventional
theory.
Design/methodology/approach
This
integrates
systematic
literature
review
(LDA)
abductive
reasoning
as
research
design
analyze
existing
(12,075
results
from
Web
Science
Core
Collection)
find
gap
potential
clues
proceeding
our
future
direction.
Findings
reconstructs
reinterprets
AI-based
AI-enabled
Ba.
Specifically,
it
reimagines
forms
functions,
establishing
a
new
paradigm
through
dimensions
socialization,
externalization,
combination
internalization.
Additionally,
examines
knowledge-driven
pathways
via
perceptual,
cognitive
behavioral
intelligence.
It
further
develops
conduct
an
in-depth
analysis
sharing
creation,
aligning
these
processes
with
updated
framework.
Notably,
replaces
traditional
Dialoguing
Interpretation
Systemizing
Decision-making
introduces
concept
“AI-based
force”
proposes
method
measuring
its
influence
rising
spiral.
also
conceptualizes
basis
nature
symbiosis,
emphasizing
shift
human-centric
relationship.
The
affordances
employed
relational
dynamics
terms
existence,
perception,
actualization
effects
affordances.
Meanwhile,
doctrine
mean
used
illuminate
relationship
across
technological
content
dimensions.
Practical
implications
findings
inspire
managers
decision-makers
adopt
various
strategies
accelerate
transformation,
thereby
enhancing
overall
force
human
decision-making.
These
can
help
rationally
manage
innovate
boost
reserves,
well
promote
development
AI
technologies
related
creation.
Originality/value
leverages
tool
reconstruct
by
Ba,
revealing
complete
conversion
process
underlying
mechanisms.
broadens
application
creation
literature,
highlighting
symbiosis
among
humans,
tools
environment.
As
result,
emphasize
need
synergistic
collaboration
between
agents
humans
KM.
Language: Английский
High-Risk Artificial Intelligence
Business & Information Systems Engineering,
Journal Year:
2025,
Volume and Issue:
unknown
Published: May 9, 2025
Language: Английский
Harnessing Big Data and AI for Predictive Insights: Assessing Bankruptcy Risk in Indonesian Stocks
Data & Metadata,
Journal Year:
2024,
Volume and Issue:
3
Published: Dec. 31, 2024
Introduction:
This
research
aims
to
investigate
the
use
of
financial
Big
Data
and
artificial
intelligence
(AI)
in
predicting
bankruptcy
risk
companies
listed
on
Indonesia
Stock
Exchange
(BEI),
with
Altman
Z-Score
model
as
main
framework.
Objective:
In
this
research,
an
intervening
variable
form
data
quality
is
introduced
assess
role
mediation
increasing
accuracy
predictions..
Method:
The
method
used
quantitative
analytical
Structural
Equation
Modeling
Partial
Least
Squares
(SEM-PLS),
which
allows
analysis
relationship
between
independent
variables
(Big
AI),
(quality
data),
dependent
(bankruptcy
prediction).
Result:
results
show
that
integration
AI
significantly
increases
company
predictions
IDX,
acting
strengthens
relationship.
influence
prediction
through
has
also
been
proven
provide
more
precise
faster
compared
conventional
model.
Conclusion:
These
findings
confirm
a
key
factor
must
be
considered
optimizing
capital
market.
implications
for
development
technology
(Fintech)
management
strategies
public
companies,
especially
identifying
risks
effectively
by
utilizing
latest
technology.
Language: Английский