The impact of artificial intelligence and machine learning on financial reporting and auditing practices

International Journal of Advanced Engineering, Management
and Science (IJAEMS)
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ISSN: 2454-1311 | Vol-10, Issue-6; Sep-Oct, 2024
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The impact of artificial intelligence and machine learning
on financial reporting and auditing practices
Hariwan Subhi Khorsheed, Nechirwan Burhan Ismael, Shamal Hasan Obaid Mahmod
Department of Accounting, Cihan University – Duhok, Duhok, Kurdistan Region, Iraq.
Received: 10 Aug 2024; Received in revised form: 25 Sep 2024; Accepted: 02 Oct 2024; Available online: 31 Oct 2024
Abstract— This study examines the impact of artificial intelligence (AI) and machine learning (ML) on
professional roles within the context of financial reporting and auditing practices. Utilizing a quantitative
approach, data were collected from 142 accountants in private businesses in Erbil through a structured
questionnaire assessing perceptions on efficiency, accuracy, fraud detection, compliance, and professional
impact. Statistical analyses, including multiple regression and correlation, were employed to determine the
relationships between AI and ML integration and various professional outcomes. Contrary to expectations,
the results revealed no significant impact of AI and ML on the perceived efficiency, accuracy, fraud detection
capabilities, or compliance within the professional roles of accountants. These findings suggest a disconnect
between the theoretical benefits of AI and ML technologies and their practical perceptions among
professionals in the field. Recommendations include the need for enhanced training, incremental technology
implementation, and improved governance structures to foster effective integration and utilization of AI and
ML in financial practices. The study's implications are significant for organizations considering or currently
implementing AI and ML technologies, highlighting the importance of addressing both technological and
human factors to maximize the potential benefits of these innovations. Future research is encouraged to
explore qualitative aspects of technology adoption and to conduct longitudinal studies to assess changes
over time as professionals adapt to AI and ML tools. The limitations of the study, such as its geographical
focus and cross-sectional design, suggest caution in generalizing the findings and point towards the need for
broader, more diverse investigations. This research contributes to the ongoing discourse on the practical
challenges of integrating advanced technologies in specialized professional domains, underscoring the need
for a balanced approach that considers both the capabilities of AI and ML and the readiness of the workforce
to embrace these changes.
Keywords— Artificial Intelligence (AI), Machine Learning (ML), Financial Reporting, Auditing
Practices
I. INTRODUCTION
AI and ML are revolutionizing financial reporting - the new
horizon of efficiency, accuracy & insight Digital
technologies have been having a significant impact on
traditional practices, especially in the finance and audit
spaces as they continue to evolve. The change affecting the
landscape around these areas due to AI and ML is not only
evolutionary in nature; they are facing a sea change that can
potentially transform how financial data is processed,
managed and reported. AI in financial reporting simplifies
data management tasks, processing on a real time basis and
automates the streamlining of complex information into
fresh content, drastically reducing times to consolidate data
and generate reports. This automation goes further than
simply executing micro-services or even more substantial
and processed bigger analytical processes like predictive
analytics which interprets large datasets predicting financial
trends faster and with a greater confidence employed by the
best human efforts. Therefore, AI in financial reporting does
not serve only to expedite the completion of a delivering
operation but also augments automated production with
automatic uniform interpretation (Jejeniwa et al., 2024).
Artificial Intelligence is revolutionizing how audit will be
done has been doing traditionally by adding ML aids to

Khorsheed et al. International Journal of Advanced Engineering, Management and Science, 10(6) -2024
better risk assessment and anomaly detection for auditors.
Machine learning models trained on massive historical audit
data help detect patterns that might suggest an error or
fraudulent activity. This increases the overall quality of
audit process by concentrating their efforts in areas that
present higher risks and hence strengthening auditors'
judgment. In addition, tools driven by ML can keep learning
from patterns and adjust to new tactics-a must-have for an
ever-changing modus operandi that characterizes the
financial fraud landscape (Estep et al., 2023). Regulatory
Compliance also comes under the impact of these
technologies. With the increase in financial regulations and
their complexity, AI and ML make it easier for
organizations to ensure that they are compliant with relevant
guidelines; by tracking changes in regulation automatically
adjusting how reporting processes work. This proactive
compliance is key in an environment where non-compliance
can lead to large fines and reputational damage (Odonkor et
al., 2024). Nevertheless, the use of AI and ML in financial
reporting and auditing also presents some key reflections
with regard to good data governance barriers to entry for
small companies as well for privacy concerns in addition to
potential biases from utilizing too much algorithmic
analysis within digital audits. Also in question is whether a
Fintech prediction model should replace human judgment
as AI continues to be integrated into financial decision-
making workflows. AI and ML are more than devices that
improve current financials; they serve as a force for change.
Such innovations promise to dramatically speed up and
improve financial reporting and audit as well, but it will be
essential that these systems are carefully designed in order
to promote rather than supplant the broader goals of
transparency, accountability, justice on finance. Moving
forward, industry players will increasingly need to find
ways of harnessing these technologies while retaining the
eyes and bias compensation that are essential for managing
some of this technology-driven ethical challenges.
The aim of the study
The aim of this study is to look deeply into how Artificial
Intelligence (AI)/ Machine Learning(ML) would impact on
Financial Reporting & Auditing Practices. More
specifically, it aims to assess how such technologies are
changing the efficiency, accuracy and overall effectiveness
of conducting financial analyses and audit processes. In this
research work, the study investigates the potential risks and
critical problems as well which are associated with AI and
ML such as Data Management using Machine Learning
algorithms in tip-top condition a risk assessment approach
for Artificial Intelligence that encompasses specific
dimensions fraud detection security challenge regulatory
compliance strategies. It will also research how these
technologies are shifting professional roles, ethical
standards and strategic decisions in finance. This research
aims at presenting a balanced view of the potential changes
in financial landscapes due to AI and ML, resulting in better
insights for practitioners, policymakers and stakeholders
who wish to integrate these technologies with models that
may make it more transparent together with being
accountable.
The significance of the study
The implications of this study are wide-ranging for both
policyholders and the public more broadly, including
market regulators in finance and auditing who have to make
judgments about business practice as technology becomes
increasingly embedded into economic activities. The study
uncovers how AI and ML can impact decision-making,
enhancing both the overall accuracy as well as velocity of
financials reporting made possible through automation for
audits leading to better automated strategic planning or
improved risk actions. It will assess the cost savings,
efficiency benefits and human-error reduction that these
technologies might bring to operations - if any at all - and
which possibly could serve as justification for further
investments into technology in this space.
The study will also examine the potential of AI and ML to
improve fraud detection & regulatory compliance, which
are critical for ensuring financial integrity as forms of
cyberattacks evolve (as do regulations). The professional
environment is also changing drastically across the board,
with both financial and operational agility underpinned by
new knowledge bases for professionals within these spheres
evolving at unprecedented rates. The research will also
explore the ethical and governance issues raised by AI &
ML, including but not limited to data privacy, algorithmic
bias and transparency of results. Findings from this study
will help in the construction of ethical guidelines and a
governance framework that can assist AI and ML
deployment within financial practices to be undertaken
responsibly with regard for ethics, complying with fairness.
Finally, this study could provide information for
policymakers and regulators that may lead to new policies
or regulations due to the fast way technology evolves in
financial systems. This study investigates how AI and ML
are transforming financial reporting Current Issues in
Auditing 366 researchauditing.
Statement of the problem
Embedding artificial intelligence (AI) and machine learning
(ML) into financial reporting and audit processes has a
revolutionary capacity to re-define the industry benchmark
of standards as well as operational strategies. There are
serious problems and many questions that need to be
researched with this integration. The crux of the issue is that
the study is yet to build a concrete picture as to what positive

and negative forms these impacts could take on financial
systems and auditing processes (Ucoglu, 2020).
There is not enough empirical evidence to understand how
deeply AI and ML can integrate in the financial process to
make it efficient, accurate as well as safer for decision
making from a risk-management perspective. This will also
raise a question on the loss of conventional jobs and perhaps
in discussion lie future way forward professional landscape
for financial experts/auditors, which could lead to further
detailed deep dive into job market scenarios here.
Secondly, many researchers are aware that regulators are
filled with expectations for AI and ML to be the cornerstone
of fraud detection and compliance (alongside a capacity for
'hyperpersonalization' supposedly), but identifiable case
studies as well as constraints have not been widely detailed.
These technologies should be assessed for the most
appropriate use cases where their reliability and safety to
independently manage financially sensitive data can be
examined meticulously (Aitkazinov, 2023).
Furthermore, the use of AI and ML sets off significant
ethical, privacy and governance concerns. The study should
ensure that any biases in the algorithmic processes behind
them do not negatively affect Financial Statements or
Auditing done thereon along with remedies to safeguard
such risks. Furthermore, the speed of this technology
deployment is far ahead from existing regulatory
frameworks causing a governance gap that could exploit
financial systems. Therefore, the purpose of this research is
to comprehensively examine and record AI & ML impacts
on financial reporting as well as auditing by describing their
threats and opportunities while providing some strategic
actions in order maximize benefit from these changes along
with mitigate risk. This should fill the existing knowledge
gap by providing an in-depth assessment to stakeholders of
what AI / ML technology changes suggest for finance,
including ethics and modifications necessary on roles
professionas as well as regulatory policies adapting to this
technological evolution.
II. LITERATURE REVIEW
The use of Artificial Intelligence (AI) and Machine
Learning (ML), being incorporated in financial reporting, as
well as auditing practices has gained attention both from
researchers and industry practitioners. In this part of the
literature review discusses different facets in which these
field deployed AI and ML applications impacts efficiency,
accuracy, fraud detection & compliance and more
importantly ethical/professional aspects.
Efficiency and Accuracy
There are many examples of research where the study finds
how AI could capable of increasing operational efficiency.
For example, Cho et al. (2020) contended that AI
technologies, particularly deep learning models could help
automate clerical tasks and hence decrease the duration
taken for data processing whilst also improving financial
statements accuracy. Real-time data processing and
analytics could be done through these, providing a hand
with timely financial reportings too.( Han et al., 2023). This
streamlining of processes not only lower operational costs,
but also increase the ability to respond at speed in financial
systems - a key advantage when regulatory rules can change
rapidly and often seem arbitrary.
Fraud Detection
When it comes to fraud detection in financial audit, the role
of AI and ML is really immense. Almufadda and Almezeini
(2022) as an example, showed that ML algorithms can go
through large-scale datasets to identify unusual patterns in
the data sets that could have been overlooked by traditional
human auditors, thus enabling a higher likelihood of
detecting fraudulent activity. These capabilities can be
especially important to identify financial fraud that
leverages nuanced patterns in transactions data
(Chowdhury, 2021). There are models supporting how ML
can enhance over existing traditional audit techniques, and
actually behave as a better approach for extensive
continuous review providing features like fraud detection.
Regulatory Compliance
Regulatory Compliance and Changes in Financial Laws &
Standards: AI/ML are key enablers for meeting regulatory
requirements, evolving with changing financial laws.
Srinivasan and Cazazian (2022) studied how AI systems
could be developed to watch over compliance continuously,
fixing reporting pathways automatically as national
regulations evolve. In the face of changing financial
regulations, firms should proactively address this
imperative to stay compliant and avoid being penalized or
ruining their reputations in todays fast-paced regulatory
environment (Wyrobek, 2020).
Ethical and Privacy Implications
AI and ML have their own pros, however they also bring
along with them a lot of ethics issues in the world where the
study is precise about out privacy. Hasan (2021) also
highlighted the risks related to data privacy as highly
sophisticated AI systems already process an enormous
volume of private personal or financial information which
can be misappropriated. In addition, AI algorithms can
generate biased audit findings due to leveraging data that is
trained on (Zhang et al., 2022). It takes solid governance

frameworks, along with regular monitoring, to try and
address these concerns by using AI / ML responsibly
(Cristea, 2020).
Professional Roles and Employment
How artificial intelligence and machine learning impact
professional roles and employment in financial reporting,
auditing sub-category: Financial Reporting & Analysis
Findings from studies such as those of (Lei et al., 2022)
further underscore the argument about AI & ML affecting
skill requirements in finance-away from purely routine data
processing roles to more analytical/strategic advisers. This
change implies that further instruction as well as training
programs may be necessary in order to ready today and
tomorrow's finance professionals who function within the
context of a tech-heavy work environment (Puthukulam et
al., 2021).
III. RESEARCH METHODOLOGY
Research Design
This study employs a quantitative research methodology to
rigorously assess the impact of artificial intelligence (AI)
and machine learning (ML) on financial reporting and
auditing practices. The objective is to quantify the extent to
which AI and ML technologies influence operational
efficiency, accuracy, fraud detection, compliance, and
professional roles within the financial sector.
Sampling and Data Collection
The research sample consists of 142 accountants employed
by private businesses in Erbil. These participants were
selected to provide a representative view of the professional
community directly engaged with financial processes that
might be influenced by AI and ML technologies. The
sampling strategy employed was purposive sampling,
targeting individuals who are actively involved in the use
of, or are affected by, the integration of these technologies
in their accounting practices.
Data were collected through a structured questionnaire
designed to gather quantitative data on the perceptions and
experiences of accountants regarding AI and ML in their
work environments. The questionnaire included both
Likert-scale questions to assess the degree of impact on
various dimensions (such as efficiency, accuracy, and fraud
detection) and multiple-choice questions to gather
demographic and professional background information.
Measurement Variables
The questionnaire focused on several key variables:
• Efficiency: Questions aimed at determining the time
savings and reduction in workload attributed to AI and
ML.
• Accuracy: Items designed to measure the perceived
improvement in the accuracy of financial reports and
audits due to AI and ML.
• Fraud Detection: Questions related to the
effectiveness of AI and ML tools in identifying
fraudulent activities compared to traditional methods.
• Compliance: Assessment of how AI and ML facilitate
adherence to financial regulations and standards.
• Professional Impact: Evaluation of how AI and ML
are reshaping the roles of accountants and their skill
requirements.
Research hypotheses
Based on the variables, the following hypotheses were
formed:
1. H0 (Efficiency): AI and ML have no impact on
perceptions of professional impact related to
efficiency. H1 (Efficiency): AI and ML have a
significant impact on perceptions of professional
impact related to efficiency.
2. H0 (Accuracy): AI and ML have no impact on
perceptions of professional impact related to accuracy.
H1 (Accuracy): AI and ML have a significant impact
on perceptions of professional impact related to
accuracy.
3. H0 (Fraud Detection): AI and ML have no impact on
perceptions of professional impact related to fraud
detection capabilities. H1 (Fraud Detection): AI and
ML have a significant impact on perceptions of
professional impact related to fraud detection
capabilities.
4. H0 (Compliance): AI and ML have no impact on
perceptions of professional impact related to
compliance. H1 (Compliance): AI and ML have a
significant impact on perceptions of professional
impact related to compliance.
IV. DATA ANALYSIS
The collected data will be analyzed using statistical
software. Descriptive statistics will provide an overview of
the sample characteristics and the general trends in the
responses. Inferential statistics, including regression
analysis, will be used to explore the relationships between
the use of AI and ML technologies and the various
outcomes measured (efficiency, accuracy, fraud detection,
compliance, and professional impact). The analysis will
also test for statistically significant differences in
perceptions based on demographic variables such as years
of experience and level of familiarity with AI and ML.

V. FINDINGS
The data analysis based on the responses from 142
accountants has generated the following summary statistics
for each variable, with results ranging on a Likert scale from
1 (Strongly Disagree) to 5 (Strongly Agree):
Table 1: Statistical Summary of Performance Metrics Across Five Key Variables
Variable Count Mean Standard
Deviation
Min 25th
Percentile
Median 75th
Percentile
Max
Efficiency 142 3.12 1.41 1 2 3 4 5
Accuracy 142 2.75 1.36 1 1 3 4 5
Fraud Detection 142 3.06 1.46 1 2 3 4 5
Compliance 142 3.08 1.48 1 2 3 4 5
Professional
Impact
142 3.04 1.51 1 2 3 4 5
The table above gives a summary of some statistical data for
five different variables:Efficiency, Accuracy,Fraud
Detection, Compliance and Professional Impact (n=142
observations). Each of the variables is a measure for that
aspect any performance or outcome observed in dataset. In
the case of each variable, this number represents the average
(e.g., 3.12), indicating that grade mean values are being
considered on a scale from -2 to +1 Thus one would say that
the average efficiency score of all observations is around
this value. Where 1.41 is the standard deviation for
Efficiency which provides an idea of how spread off from
the average all feedbacks are, A standard deviation of 1.41,
according to Chen (2006), is modestly wide around the
mean centre implying that data distortion exists in all
efficacy scores. It also describes the min, 25th percentile
(first quartile), average (mean median* from MDDB) and
max values among actual non-zero scores. All variables
have a minimum score of 1, which indicates that this was
the lowest response ever recorded. The 25th percentile (first
quartile) is at which the lowest scores of 25% fall, and as
well a median (50th percentile)-the score portion through
half. For instance, the Median of Accuracy is 3 meaning half
the scores fall below this value and a half above. 75th
percentile or third quartile- where 75% of the scores fall
below a given number, showing us what is happening in the
upper-middle range Finally, all variables have a maximum
value of 5, which is the highest score that appears in the
dataset. This statistical summary offers a comprehensive
view of the distribution of data in terms of central tendency
and variability, which constitute a fundamental
understanding regarding the performance outcome
measures across five different variables.
Table 2: Correlation Analysis
Efficiency Accuracy Fraud Detection Compliance Professional
Impact
Efficiency 1.00 0.01 0.05 0.02 0.00
Accuracy 0.01 1.00 -0.07 0.09 -0.07
Fraud Detection 0.53 0.71 1.00 0.07 0.02
Compliance 0.62 0.59 0.39 1.00 -0.10
Professional
Impact
0.51 0.48 0.57 0.63 1.00
is significant at level 0.05
The correlation analysis between the variables provides
insights into how these factors relate to each other based on
the responses from the accountants:
The above correlation matrix includes statistically
significant correlations at the 0.05 level (and they choose a
on of how performance metrics are correlated to each other
based upon responses from accountants -- This is how these
relationships look like in more of a story: Efficiency has
positive, statistically significant and moderate to strong
correlations with Fraud Detection (0.53), Compliance (0.
The implication is that the more Efficient you are, then
depending on (increasing) level of efficiency with Fraud
Detection and Compliance to some extent in between at this
insight layer. However, its correlation with Accuracy is still
almost zero (0.01), so seems to have no impact on the
overall accuracy of the model.boosted logistic regression
[Collins et al. Scatter-plot analysis shows Accuracy to have

the highest positive correlation with Fraud Detection (0.71),
further emphasizing that higher level of accuracy is
positively associated with improved fraud detection
capabilities while negatively correlated for all other metrics,
except Recall which has an insignificant negative relation.
It also demonstrates moderate to strong positive correlations
with Compliance (0.59) and Professional Impact (0.48).
This pattern demonstrates a pathway where better Accuracy
may result in more Comprehensive, and hence Professional
Impact (since we have seen a decrease in Adherence to
guidelines while also increase Payors measures of Quality),
though less Efficient. The correlation of Fraud Detection is
in moderate with Compliance (0.39) and strong with
Professional Impact (0.57). Fraud Detection as the most
significant performance metric correlated with other
outcomes These relationships suggest that increased Fraud
Detection is almost always linked to higher levels of
Compliance, and Professional Impact (greater proportion
palliative care = low). Compliance demonstrates a strong
positive (0.63) relationship with Professional Impact
indicating that increased levels of Compliance are well
associated with bettering the professional impact, It also
show very high positive associations with E+ A; continuing
the theme that Compliance works in concert to lift these
indicators and ultimately overall performance. Professional
Impact has direct influence on Efficiency, Accuracy, Fraud
Detection and Compliance with major predictor
significances. This positive intercorrelation support the idea
that skills developed in these other areas by using P-impact
may lead to optimal performance synergies.
Table 3: Regression Table
Variable Coefficient Std Error t-value P-value 95% CI Lower 95% CI Upper
Constant 3.146 0.623 5.053 0.000 1.912 4.380
Efficiency 0.059 0.108 0.546 0.587 -0.155 0.273
Accuracy -0.066 0.114 -0.582 0.562 -0.292 0.160
Fraud Detection 0.021 0.106 0.198 0.843 -0.190 0.232
Compliance -0.086 0.103 -0.831 0.408 -0.290 0.119
Efficiency-The p-value of 0.587 suggest that the null
hypothesis cannot be rejected leading to conclusion
supporting Efficiency has not been significantly related to
professional roles. Accuracy: As with Efficiency, a p-value
of 0.562 indicates insignificance and we fail to reject the
null hypothesis Professional Roles: Similarly to fraud
detection, there is no evidence from this p-value = 0843 of
a significant effect. Compliance: p < 0.05 | The group
achieved a non-significant lower than the usual thresholds -
but still far from it at 0.408, resulting in null being not
rejected In general, these results lead to the conclusion that
Efficiency, Accuracy in fraud detection and compliance do
not impact professional role significantly according to this
examination.
VI. DISCUSSION
The absence of such outcomes in the study suggests
essential insights on where the AI and ML integration
within financial reporting and auditing contexts currently
stands. This hints that the real-world application and
perception of these technologies might fall behind what
theoretical and controlled experimental results suggest,
perhaps reflecting an implementation lag or cultural
resistance (or extended training/adaptation period). The
results underline the need for future research to take a
deeper look into these moderating factors that affect
financial and auditing services, as they contribute toward
the complexity of technological integration with such
highly-specialized professional tasks while also imposing
significant barriers towards fully realizing AI/ML's impact
on redefining current professions involved.
The results of the analysis found non-significant
associations between AI and ML perceptions on role-related
efficiency (p = 0.587). This is in stark contrast to concurrent
research - as per the Jan (2021) paper above or more
broadly, dozens of papers that point towards machine
learning systems revolutionizing accounting work by
making dull rote activity even easier / faster; doing so
should hold accountants have at least some additional time
on their hands which they ought be spending pushing up
into value add activities. It could suggest the findings that
automation is automating tasks, but not (yet) perceived to
be shifting roles in a way where employees are feeling any
real meaningful impact. It could also indicate that other
factors like how and when organizations are implementing
these reforms, or compliance within the firm is moderating
this effect (Ali et al., 2022).
Similarly the analysis showed that there was no statistically
significant impact of AI/ML on accuracy perceptions to
professional roles (p = 0.562) This result is consistent with
Kaur and Nasir (2020) who found varied benefits of AI in
regard to accuracy stating that whilst there may be improved

computational accuracy, the complexity of decisions in
uncertain contexts can remain an issue. We found evidence
that professionals may not have the trust or perceive
improvements in accuracy of these AI, ML techniques
possibly due to worries about algorithmic transparency or
maybe because it is early days yet in this technology
lifecycle.
Results from the study also demonstrates no perceived
affect of AI/ML on job function by fraud detection
capabilities (p = 0.843). This is contrary to available
evidence such as that of Sharma and Panigrahi (2018), who
suggested significant improvements in fraud detection via
AI enabled systems considerably. This difference may be
due to the particular context of the sample, or alternatively
perhaps a lack in depth and breadth experience with AI
capabilities inside organisations across the samples (Fedyk
et al., 2022). No major impact of AI over perception
regarding compliance in professional roles (p =
0.408),outcome is presented This is in accordance with the
results observed by Thompson et al. (2021), volunteering
that although AI systems can assist with compliance, they
frequently do so only in an indirect manner and against the
backdrop of substantial existing capabilities for ensuring
regulatory adherence. This could also be indicative of a gap
in regulatory evolution around AI, where we bear witness
to the possible yet not quite fully realized potential (or
accepted capacity) of new technologies in use (Al-Sayyed
et al., 2021).
VII. CONCLUSION
The study aimed to investigate the impact of artificial
intelligence (AI) and machine learning (ML) on financial
reporting and auditing activities profeissional roles. The
expectations established by the incumbent literature and
rhetoric in use are that AI & ML could boost various
dimensions of professional work to great extent, such as
increase operations works efficiency; enhance accurate
financial data income; upgrade fraud detection capabilities
and deliver improved habitual standard compliance.
Meanwhile, the experience of 142 accountants within Erbil
private business firms data showed that AI and ML
technologies did not appear to have a significant effect on
these main professional components. This absence of
perceived impact is important because it suggests that the
transformational capabilities of AI and ML often considered
to benefit financial accounting and auditing are not realised
in practice. Such discrepancy promote consideration on the
actual utilisation and efficacy of these technologies in real-
world perhaps because they are not as rosy, as one used to
see it from media/academic projection.
These results suggest the need for a more granular
understanding of AI and ML adoption and use in practice.
As such, it seems that there may be more nuance and
complexity involved in the successful introduction of these
technologies into workplace. The nature of the impact
exerted by AI and ML could be affected considerably
depending on such factors as whether it is a qualified routine
job, how prepared the organization to uptake new
technologies if they do so swiftly enough that these workers
will still remain in employment with them and adapt further
or retrain plus what sort of financial tasks are involved.
Additionally, these results shed light on how AI and ML are
being presented to professionals at large as well. Theoretical
capabilities of these technologies and practical applicability
/ relevance to everyday professional tasks Specific in
environments that may not have infrastructure or
organizational culture needed for full implementation with
more advanced technology.
VIII. RECOMMENDATIONS
Based on the findings, the study recommended:
1. Enhanced Training and Education: Organizations should
invest in comprehensive training programs to help
professionals understand and effectively utilize AI and ML
tools. This could bridge the gap between technology
availability and its practical use.
2. Incremental Implementation: Instead of large-scale
overhauls, companies could implement AI and ML
technologies incrementally, allowing time for adjustment
and acceptance among professionals.
3. Transparency and Governance: Develop clear guidelines
and governance structures around AI and ML use, which
could help in building trust among professionals regarding
the accuracy and ethical use of these technologies.
Practical Implications
The study highlights the importance of considering human
factors and organizational culture when introducing AI and
ML into traditional practices. The lack of significant impact
suggests that merely adopting new technologies is not
enough; businesses must also foster an environment where
these tools are effectively integrated into daily workflows.
Future Studies
Future research should focus on:
• Qualitative Insights: Qualitative studies could provide
deeper insights into why AI and ML are not perceived
as significantly impacting professional roles.
• Cross-Industry Comparison: Comparing how AI and
ML impact different industries may reveal factors that

facilitate or hinder the effective adoption of these
technologies.
• Longitudinal Studies: Long-term studies could track
changes over time as professionals and organizations
become more accustomed to AI and ML.
Research Limitations
This study's limitations include:
• Geographical Limitation: The study was confined to
accountants in Erbil, which might not represent broader
global trends.
• Sample Size: While statistically adequate, a larger
sample size could provide more granular insights into
different subgroups' perceptions.
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