Machine Learning in Auditing: Problems, Solutions, Guidelines,
Future Directions
Xinke Bai
a
International Institute of Joint Audit, Nanjing Audit University, Nanjing, China
Keywords: Auditing, Machine Learning (ML), Anomaly Detection, Financial Transparency, Data Privacy.
Abstract: In the modern and extremely dynamic business arena, auditing can be seen as the only way of getting financial
information clarified, taking individuals who have that responsibility to account, and creating the impression
of an honest player. The effectiveness of auditing is not just limited to risk mitigation approaches considering
prevention of fraud, as it also strengthens the principles of legal compliance, market order, and economic
stability in general. Several factors are propelling the auditing industry through a significant transformation
at an increasingly fast rate, and these factors are big data and AI, of which machine learning (ML) is one of
them. Traditional audits are made faster and more precision by ML through scrutinizing and interpreting
enormous and complex data sets, such as detecting risk and anomaly violations and automatically generating
reports. Yet, the auditing activities introduce ML into the auditing processes this creates another problem.
These encompass issues of data quality and reliability, models transparency and interpretability for complex
systems, risks of overfitting and underfitting, and also include security and moral considerations in the
software and hardware regulations. On the other hand, there exists a cognate challenge of the auditors getting
upskilled to effectively exploit these AI-driven technologies. This paper seeks to determine the problems of
empirical evaluation of ML techniques and suggest possible solutions to maximize the efficiency and
productivity of the auditing processes.
1 INTRODUCTION
Audit is one of the keystones of the present-day ever
increasingly complicated and variable business
environment, and audit should be seen as the
foundation for pointing out the gaps in financial
transparency and reliability.
Although it is the assessment of a company’s
performance measure, auditing is also important for
fraud detection and prevention, internal control
improvement, and risk management. The
Implications of necessary auditing are apparent as a
means of legal compliance, maintaining the market
order, and the growth of the economy. In an age
where information can be tantamount to money,
auditing helps to maintain a public trust and assure
the market standard.
The very apace at which technology is now
manifesting itself, especially in data collection and
artificial intelligence, has made machine learning
(ML) emerge as a proverbial tool in a number of
a
https://orcid.org/0009-0009-8547-1349
industries. From self-driving cars to product
recommendation systems, ML technology has
evolved so much that it can analyze and correlate data,
which is fundamentally redefining the conventional
practices. In the case of the auditing sector, which
depends heavily on data correctness and speed, ML
can save so much effort. Inside the audit process, the
application of such techniques as anomaly detection,
risk assessment, and automated report generation can
help boost the efficiency and precision of audit
outcomes significantly. A perfect example is ML; this
technique can monitor financial transactions
continuously in real time to detect any variations,
provide predictive risk analysis, and generate a
complete report about the audit, thus, saving human
errors and increasing productivity (Hassan, 2023).
However, the implementation of Machine
Learning in audits may not be without its difficulties,
such as the ones associated with data quality, model
transparency, overfitting and shifting characteristics,
regulatory compliance, or the auditors’ skills. A field
496
Bai, X.
Machine Learning in Auditing: Problems, Solutions, Guidelines, Future Directions.
DOI: 10.5220/0013269400004568
In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (ECAI 2024), pages 496-503
ISBN: 978-989-758-726-9
Copyright © 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
of auditing that focuses on the implementation of
machine learning identifies five issues: data quality
and visualization, model interpretability and
transparency, overfitting and generalization,
regulatory compliance, and ethics, the skills and
training gap among auditors. The section proposes the
measures to improve data governance, model
interpretability, overfitting control, compliance and
ethical concerns, and auditor's technical skills all of
which would contribute to the solution of these issues.
Such methods are likely to contribute to the
subsequent diligence of machine learning in auditing,
resulting eventually in the growth of the quality and
efficiency of audits. This article tries to deliver
valuable findings and to present some
recommendations of efficiently using ML for raising
the level of audits’ quality and providing compliance
with the laws of compliance.
2 CHALLENGES
2.1 Data Accuracy and Precision
Another major feature concerning the auditors is the
incomplete data that they get. The summary part of
the report may be distorted in most cases when the
transaction data, such as date, time, nature of
transaction, cost of goods sold, etc., are revealed
incompletely or inadequately. These areas inhibit
machine learning from discerning anomalies, fraud
and compliance violations; hence, the level of audit
clutter cannot be trusted and lacks reassurance (Office
of Inspector General, 2012). The utilization of
machine learning on unsuitable and incomplete data
sets will not only yield inaccurate insights, but also
miss out on knowing the profound risks and
anomalies the artificial intelligence should have been
dealing with.
Discrepant data is one of the major difficulties
confronting practitioners. Disparities in data entry
systems, human mistake, and variable specificity of
data sets could be the cause of drifted information
between data source systems. Such differences can
disrupt the engine of machine learning algorithms that
form the basis of this whole idea, thereby provoking
errant conclusions about risk assessment or wrong
identification of mischief during the audit (Stefanov
et al., 2012). For instance, labelling the same financial
transactions originated from one source into different
categories could mean that the machine will not be
able to notice the patterns of fraud or error.
Additionally, machine learning is further
complicated because of wrong data, like
discrepancies in the transaction amounts or
accounting transactions that are not in the right
account. Such errors will result in a situation where
the models are used to learn from the wrong
information, and their outputs may not be useful
inputs to even monetizing the said organization.
Maintaining high quality and integrity of data
becomes increasingly challenging but critical,
particularly with businesses generating huge volumes
of complex data.
In an environment where auditing becomes too
demanding for the teams due to these data quality
matters, the entire audit process may fail leading to
serious mistakes in audit conclusions, and, moreover,
it can result to failing to meet the compliance and
regulatory requirements. Hence, the pillar that goes
hand in hand with the capacity to utilize machine
learning so as to improve audit accuracy, efficiency,
and effectiveness is data quality and integrity
(Stefanov, 2014). If these problems are not solved,
many of the advantages of ML-driven practices will
remain unexploited, and the overall audit result
quality will be compromised.
2.2 ML Interpretation and Describe
An enormous difficulty to use machine learning in the
auditing field is the issue of machine learning models
transparency and interpretability. The advanced
machine learning models, especially deep learning
that are the most robust algorithms nowadays, tend to
be “black boxes” thus having no knowledge of the
way the decision-making is based on (Carvalho,
Pereira, & Cardoso, 2019). These models utilize data
processing as well as decision making systems that
enable the algorithms to successfully do that, but the
humans, including the auditors, cannot follow the
internal procedures used for arriving at the decisions
because such procedures are not clearly shown to
them.
In auditing, this kind of uncertainly may be
problematic since the auditors need to know the path
of how decisions have been taken to achieve every
conclusion. In finance, traditional auditing entails the
following and logically verifying procedures and
steps that are easy to understand. However, the
process of machine learning has been found to be
complex, and it is not easy to follow the road from
input data to output decision, especially when there
are deep neural networks with numerous layers in
between. This complexity impedes auditors from
interpreting the reasoning behind the model outputs
to validate and attest audit findings which is one of
the most important steps in the whole audit process.
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Algorithmic systems’ “black box” nature is usually
taxing for an auditor because it becomes impossible
to correctly illustrate the basis for unearthing the
fraud or signs of suspicion. Having no specific
understanding of how a model generates results
presents an issue for auditors since this will make it
quite hard to ensure the reliability and are correct
these results (Knechel, 2013). This lack of clarity may
weaken supposed confidence and truthful audit trails
of the audit process, which the stakeholders rely on
for the credibility of this profession.
2.3 Overfitting and Generalization
Overfitting is notoriously the biggest hazard that a
machine learning model is in possession of,
especially if this model is used to provide a complex
task like auditing. In a nutshell, one type of the
needless compliance with the training data appears to
happen when the model captures the data set which it
is already familiar with, namely capturing the general
patterns beside the noises and anomalies present in
that data set (Adler et al., 2018). Now, the model,
which was otherwise perplexed by the training data,
does almost perfectly on this new data set. So, it can't
learn or maybe draw inferences from this data set
which is not seen yet. The paraphrase of this is that
the production of genuine data would be
compromised by this situation of overfitting, and
subsequently cannot be used in real time, which
involves not only diverse but also varying data among
different audit engagements.
In shapes of self-regulatory conflicts, overfitting
to be found is more particular in the case of audit
inspection. Auditors lag on ML algorithms and
machines for identifying the fraudulent activities,
estimating risk, and coming up with decisions based
on historical and current financial data. Nonetheless,
an overzealous working model that fails to see the
randomness or specific pattern variants that are
duplicates of the training data set can lead to false
positives or false negatives. E.g., it might be found to
incorrectly flag ordinary transactions to be fraudulent
if they have the resemblance to the distortions of the
training data, or miss out the real fraudulent
transactions, which might appear to have the slight
dissimilarities from the patterns the ML has learned.
Generalizing is made even more difficult by the
fact that there are many different types of financial
data. Undertaking of auditing purposely means
dealing with diverse data from different clients,
industry, or time, which all have differing
characteristics. Striking a right balance between a
model that is hyper-focused on one set of data and
widely enveloping datasets can make the audit
process reliable across various audit scenarios (Adler
et al., 2018). This heterogeneity renders the machine
learning models apart from being pre-programmed to
learn from past information, they need to be pre-
programmed to move freely within the real world and
handle multiple and diverse situations.
2.4 Regulating Coherently and
Ethically
Machine learning in the audit process introduces
substantial challenges with respect to regulatory
compliance, data privacy, security, and ethical
dimensions. The advancement of the machine
learning models, which are increasingly applied in the
analysis of sensitive financial data and in making
decisions that influence many stakeholders, imposes
strict standards of ethics so that the regulation
provides transparency, fairness, and customer
satisfaction.
Another important aspect is the data privacy that
is often prioritized and involve large volume of
sensitive information with the clients, employees, and
customers that is recorded on paper (Criado, Ferrer,
& Such, 2021). The utilization of machine learning
also becomes dependent on the ability to access and
process the sensitive data; therefore, the sensitive data
would be at risk of accidental data breaches and
unauthorized access. People with data privacy in
mind may not be comfortable with complying with
the strict rules such as the GDPR in the EU of their
personal data (Fasterling 2012). Machine Learning
algorithms should thus employ techniques like
pseudonymization, encryption, and additionally
should have restricted access to the data by giving
only the authorized personnel right of use.
Security is a fundamental element of strategy.
Training models of machine learning demand the
robust datasets that must be kept in a safe
environment. It is within these datasets that probing
and hacking may plague the system and thereby result
to the undermining of the model outputs and therefore
the audit results could be incorrect. Auditing entities
are liable to build up resilient cybersecurity measures
as the chief focus of thwarting any attacks by an
organization and to guard their data infrastructure in
a safe manner.
2.5 Education of Auditors and Skills
Riding on the border of progress in indexing machine
learning into auditing is the gap in skills between the
auditors. Historically, auditors defined themselves as
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financial analytics, risk assessors, and compliance
auditors. However, there has been a simulation of AI
in the current auditing system, and this has led to the
addition of the technical form skills. These skills are
understanding how machine learning models involve,
interpreting their outcome, and detecting issues like
encoding biases and model overfitting (Fasterling,
2012). Currently, many auditors are not adequately
knowledgeable about these advanced tools, which can
be a big deterrent to leveraging the utmost of these
technologies, thus hampering the overall audit
operation.
The speed at which new innovations in machine
learning technologies are coming in increases the
skills gap of those being trained and currently being
tolerated for auditors. In conclusion, AI tools for
measurement can create a lot of problems for an
auditor who is not ready to meet a need for complex
tasks include such as decision-making, risk
management, and control. This situation brings about
so many issues. Among the many other possibilities
is the auditors not inspecting the machine learning
models’ outputs well and the assumptions and
limitations inherent in the models; instead; they
would take the model’s outputs for granted and this
might end with false audit conclusions.
The problems also involve the auditors who may
have insufficient or deficient training; hence, they
may have difficulties in interpreting the machine
learning models’ outputs. By contrast, the accounting
principles that categorically specific each traditional
audit procedures are not used in machine learning
models. That is because it runs as a “black boxes,”
looking for the patterns in data that managers or
auditors may not directly know (Cao, 2017). Non-
specialized auditors in machine learning may have
difficulties in forming audit findings that could be
easily understandable, which is a counterbalance for
why audit can be traced back and through.
3 SOLUTIONS
3.1 Data Accuracy and Consistency
Audit departments' data quality and integrity risks can
be greatly minimized by introducing an effective data
governance framework. The framework details how
data is captured, stored, processed, and shared and it
should be based on sound data management policies
and standards. In the audit procedure, a data
governance framework not only secures the
consistency and reliability of these systems'
performance but also prevents the occurrence of
wrong judgments that may arise from the data quality
issues. Besides, data stewards must frequently
perform data quality assessments and audits to verify
outdated, inaccurate, or incomplete record entries to
ensure data accuracy. The data governance machine
learning model has the potential to produce high-
quality input data due to the internal data cleansing
processes enforced by the audit departments, in turn
enabling highly reliable and accurate audit results.
Before the auditors use the machine learning
models, clean and prepare the data they receive using
advanced techniques. It can entail the use of
automating tools for detecting and correcting
inconsistency, errors, and problems with outliers. For
example, such algorithms can be applied during
financial transaction audits to discover potential
erroneous entries. Analyzing and processing lots of
data before exposing them to machine learning
models can eliminate influences from the data-related
biases and inaccuracies. This clearly demonstrates,
therefore, that the machine learning systems provide
with accurate and reliable insights and results during
audits, adding on the effectiveness of audits.
The range and detail of audits can be broadened
by gathering data from different sources and
integrating and verifying the collected information.
This includes incorporating data from various in-
house sectors such as finance, sales, and inventory
systems, as well as external data consisting of market
trends, economic indicators, and competitor
information. By including such extensive data into
the audit process, machine learning technology can
analyze the vast patterns and trends and hence
increase the precision of anomaly detection and risk
assessment. Moreover, auditors ought to perform
cross-validation on integrated data also because of its
verification and consistency, which is an amplifier of
the credibility of accounts.
3.2 Clarity and Explainability of Model
By integrating explainable AI like LIME or SHAP
into the pre-audit process, it becomes easier for the
auditors to scrutinize the developed system and obtain
consistent outputs. These technologies are useful not
only to explain how machine learning models reach
their decisions, being probably able to verify and
validate the correctness of outputs, and also to
auditors to check that everything stays conformant.
This clear connectedness is indispensable for the
auditors to check whether the models facilitate the
desired outcomes and subsequently precise their
results to stakeholders.
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Establish total model building documentation for
machine learning used in audit systems. This
document must contain the model's architecture,
assumptions, input data, output data, and procedures
on decision making. Furthermore, it is in the interest
of trust and reliability to record model inputs and
predictions in order to assist auditors in the
improvement of audit results.
Leo should create machine learning models that
are specifically designed for auditing, where
simplicity is an absolute necessity. This may involve
employing less complex approaches such as decision
trees or linear regression, for example, which are
straightforward as opposed to intricate deep networks.
While these models have slightly lower predictability,
they can still give you important explainable audits.
This can be related to understanding the decisions
made for compliance and accountability.
3.3 Model Overtraining and Auditors
Broadening of Model Use
Employ k-fold cross-validation or other cross-
validation methodologies to set the model’s
performance under different segments of the data to
test how well it performs on new data. This
methodology gives a better picture of underfitting by
analyzing consistency in validating models across
various data splits. The cross-validation method is, in
fact, a quite rigorous method of checking the
performance of the model, that is, its ability to
generalize to unseen data. Since it is very crucial for
the audit outcomes trustworthiness, it is widely used
in practice.
Integrate L1 (Lasso) and L2 (Ridge) regression
models for enforcing penalties on excessively
complex ones, which are also more likely to overfit.
Also, in tree-based classifiers, which are often
important activity in auditing, pruning is applied to
eradicate branches with unimportant impact or
unreasonable numbers based on noise in training data.
By means of these techniques, the model is simplified
and thus it is possible to generalize it for the new data
more easily.
All audit abusive scenarios should be considered
by making audit training data convenient and
representative of the different realities we see in our
work. This can involve increasing the training dataset
by creating synthetic data similar to that of infrequent
or unusual cases. The model has been trained over a
broad spectrum of examples, thereby it is more likely
to be generalizing instead of building a model for a
specific number of auditing contexts, with
considerable risk posed by overfitting.
3.4 Legal Compliance and Ethics
Regulations
Provide a range of audit-specific compliance
professionals, such as data privacy experts and ethics
officers, and ensure machines are appropriately
integrated into auditing processes. The desired
committees have the presence of lawyers, data
privacy professionals, auditors, and ethicists. Theirs
would be to develop policies and evaluate processes
ensuring that the use of machine learning models
abide by the relevant laws and regulations. Otherwise,
individuals who abuse data in processing and/or
usage without the right processes end up
compromising the audit process. Thereby, it is
unethical or not allowed according to the set
standards.
The demand for a transparent and auditable
system of machine learning in development and
deployment should be established. This may require
bringing the decision-making processes, data sources,
and data-utilization methods of these models into
clear light and publicly accessible. Transparency of
all algorithms and data processing routines
documentation not only develops confidence but also
creates an audit journal to investigate data privacy
and ethical problems.
The continuous assessment of ethical and
compliance of machine learning systems in auditing
ought to be done. These audits need to make sure that
the models are up to standard with the latest
regulatory requirements and ethical guidelines for
auditing. Areas of concern may include data use,
fairness of models, including “model bias” that make
some of these decisions subject to scrutiny, and
identification of possible threats. Regular audits
contribute to the immediate diagnosis of any
identified problems without fuelling severe
consequences. This, therefore, is used to confirm that
the use of machine learning in auditing is always
compliant and textually sound.
3.5 Knowledge and Experience of
Apprentices
Establish specific training programs on machine
learning, which are mainly oriented towards auditing.
This part should include basic machine learning
concepts, data analysis methods, as well as practical
functionalities such as anomaly detection, risks
assessment, and trend analysis in audit data. Real-life
scenarios may be carried through in case studies that
can exemplify how machine learning can be of
benefit in the auditor's day-be-day job. In this regard,
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the fact that they will have practical experience in the
field does not only help these auditors to grasp
machine learning concepts but also to be able to use
them in their works.
Generate and launch audit management tools and
systems that bring machine learning capabilities, thus
allowing the auditors to use these technologies. This
tool can reduce the burden on the auditors by
automating data processing, anomaly detection, and
risk identification tasks, thereby letting auditors to put
maximum emphasis on their judgment and decision
making. In this, the tools ought to have intuitive user
interfaces and offer detailed assistance and support to
help the auditors sooner adapt themselves to the
technologies and manage to acquire mastery of the
new techniques.
Get a machine learning support team in-house
dedicated to auditors or utilize the external machine
learning consultants to provide assistance with
complicated technical problems. This team can act as
a helpdesk to help auditors with technical problems
regarding the application of machine learning or with
complex data analysis tasks. Consequently, auditors
will be able to internalize and apply the machine
learning techniques while assuring the correctness
and quality of the audit procedures.
4 BEST PRACTICES
4.1 Continuous Learning and
Development
To give auditors a chance to get acquainted with the
possibilities of machine learning tools, companies
should regularly hold training events to introduce the
fundamentals of ML, recent advances, and their use
in auditing. These training courses can solve practical
problems. For example, auditors can receive practical
case study examples and will be able to apply what
was learned in the training room to the real world that
awaits this concept in its preparation. This practical
method provides that auditors comprehend the
concepts of machine learning and getting skills to use
them by auditors accordingly.
4.2 Form Cross-functional Teams
It is better to involve scientists, audit experts, and IT
professional within interdisciplinary teams to audit
those machine learning models. These groups may
work together to obtain and fine-tune models that are
scientific yet still meet the requirements of auditing.
While working in a teamwork manner, the audit
professionals tend to get a better insight into the audit
specifications and hence design appropriate technical
solutions. Such an inclusive approach always ensures
that the developed models are not only technically
good but are also practically important for auditing.
4.3 Develop Agile Development
Methodologies for Businesses
Adopting Agile development techniques is the best
way to apply machine learning in auditing. This
approach lets teams build, analyse, and retest their
models quickly, uniquely modifying them based on
the feedback they get. Agile methodology supports
the flexibility and correctness of those models and
aids quick detection of possible disruptions that could
arise and cause serious losses. Up to date, by using
iterative development, organization can gradually
improve design of their models according to the
changes in audits processes.
4.4 Improve Data Management and
Governance
The quality of data and its management bore keen
importance for the use of machine learning is a
successful audit. We must come up with a data
management and governance model that addresses
these critical dimensions to enhance the data
reliability, validity, and completeness. The entire
process of data generation, storing, running, and
sharing should obey strict standards, protocols, and
guidelines to help the machine learning applications
along with the efficient and reliable ones. As a
process, data governance not only improves the audit
quality but also allows for compliance with rules
governing data protection and privacy.
4.5 Model Validation and Continual
Improvement
Application of the regular model validation and
evaluation methods is a primal stage keeping the
quality in check of the machine learning models as
well as the accuracy of the audit results. Through
ongoing observation and feedback loops,
organizations become capable of quickly spotting
aberrations in the models, which inevitably leads to
their consistent and optimal performance. Regular
validation keeps the models in line with auditing
standards and practices upgrade, making them the
tool which remain effective in long term.
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5 FUTURE DIRECTIONS
5.1 Broadening the Field of
Interpretability and Transparency
Interpretability as well as transparency are both
critical in machine learning of audits as the auditors
need to comprehend and explain the reasoning
process of machine learning models to ensure their
fair and accurate results. Although complex models,
such as deep learning, achieve the aforementioned
results through their black-box nature, full knowledge
of model processes is complex and difficult for
auditors. The next step of building interpretable ML
methods should become the key goal of modern
research, both in academic and industrial
environments. In addition to the current models like
LIME (Local Interpretable Model-agnostic
Explanations) and SHAP (SHapley Additive
exPlanations), the other important aspect is to see
how we can leverage the neural networks and deep
learning models and make them more transparent.
There're different ways to achieve that, these include
putting forward user-friendly model architectures or
introducing interpretability as an objective function
during model development. Implementing such
techniques would serve not only in the proper
understanding of the output of the models but also the
increasing of the credibility of theses so that the audit
findings can be relied on and accepted.
5.2 Integration with Multimodal Data
Auditing focus relies too much on financial
dimensions in reality and does not harness the
opportunity of unstructured data. Next-generation
audit technologies must be multifunctional, hence
should be able to handle and combine multiple data
types, such as text, images, and audio. This is made
possible by multimodal machine learning models,
which allow the auditors to observe and learn from
studying data from various scope. One option could
be to use NLP techniques to perform an analysis of
the internal communication records (such as email or
chat logs) that are stored within a company to help out
in the identification of fraud or any other compliance
violations. On the contrary, computer vision
techniques can be used to reveal discrepancies and
anomalies that might be present in scanned contracts
and documents. It will vastly improve the uniformity
and correctness of the audit process to consolidate
into one the complementary types of data that
insightfully goes together in audit results.
5.3 Live Auditing and Continuous
Monitoring
With the operations environment of businesses
becoming much more sophisticated and evolving
rapidly, instant auditing with constant surveillance
seems to be the trend. Consequently, such a viewpoint
requires machine learning models to ingest and
comprehend data streams from a multitude of sources,
with the goal of immediate response to anomalies and
risks, which may appear at the same time. The
resultant effect is that such real-time data stream
processing algorithms and systems architectures that
are equipped with the ability to continuously monitor
business processes and detect anomalies based on an
established criterion or model will be developed.
Taking note of the potential for financial crimes at
that moment, real-time tracking of financial
transactions and account activity could provide notice
about the existence of any possible fraud or financial
crime and prompt the undertaking of corrective
actions. For such an accomplishment, studies would
need to be extended to not only the development of
ML models, but also resource management that
involves computing, communication, and system
integration.
5.4 Firming up Ethics and Regulatory
Compliance
Machine learning use in auditing undoubtedly brings
the challenges in data privacy and ethics with it.
Future research should concentrate on how to make
the integration of data utilization and analysis not
only effectively but also maintaining the data privacy.
The algorithms of privacy maintenance, for instance,
differential privacy, can lessen the chance of
disclosure of sensitive information about some
personal parts during the processing of the data.
Additionally, the development of data protection
regulations such as GDPR and CCPA pushes
organizations into designing compliance algorithms
to make sure that models’ operations do adhere with
laws of the land. Another central issue is algorithmic
bias; machine learning models may unconsciously
capture the biases that were present in the training
data. Thus, a future research focus should be on
algorithms which are developed to detect and
minimize bias, thus ensuring models make impartial
and fair decisions. The features will not only increase
the acceptability of models in the legal realm and
social but also help in enhancing their applicability in
the auditing field.
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6 CONCLUSIONS
In a business environment that is so varied and
competitive, auditing is still valuable in terms of
book-keeping, adherence to laws, and financial
system stability. The inclusion of machine learning
(ML) has brought with it a slew of benefits in the form
of improved accuracy and processing times in audit
routines, such as fraud detection, risk profiling, and
automated auditing reports, to name a few. However,
despite the convenience, the application of ML in the
area of auditing still has some issues that need to be
addressed. These problems are features of data
quality, model transparency, overfitting, and
regulatory compliance. Also, ethical concerns in
combination such as data ownership and fairness of
algorithms have to be dealt with prudence. Instead of
looking for the benefits of implementing ML in
auditing, data governance frameworks should be
reinforced, model interpretability improved, and
training provided for auditors. Tackling these
problems will enable the fair usage of ML in auditing,
which is bettering audit quality in addition to keeping
the right ethical and legality standards.
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