TOPIC OF THE PAPER: ARTIFICIAL INTELLIGENCE IN AUDITING Instructions for the Paper Choose a topic that is of interest to you(THE TOPIC SELECTED IS ARTIFICIAL INTELLIGENCE IN AUDITING) . Perhaps a toAcademy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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THE IMPACT OF ARTIFI CIAL INTELLIGENCE
TECHNOLOGIES ON AUDI T EVIDENCE
Shaher Falah Al -Aroud, Al Isra University
Technologies of Artificial Intelligence (AI) are critical for future of the auditing
profession. These technologies are actually vital tools that provide the auditing professionals
with the means necessary for increasing the effectiveness and efficiency of their jobs. The aim of
this study is to examine the effect of artificial intelligence technologies on audit evidence, from
the point of view of certified auditors in IT companies in Jordan. Descriptive research design
was adopted in the study among 314 au ditors. Structured questionnaire was used to obtain the
information needed for the study. The Findings of the study showed include that expert system
has a significant effect on the audit evidence. Neural network technology has not significant
effect on th e audit evidence. The study recommended increased interest in artificial intelligence
technologies by audit offices operating in Jordan because of its scientific importance in
improving the collection of audit evidence .
Keywords: Artificial Intelligence T echnologies, Audit Evidence .
The AI science is a technical science that, by simulation of the human intelligence,
expands, extends, and develops research in order to establish theory, methods, technology, and
application systems. In brief, it is computer system that has the ability to transform the human
wisdom into productive work via technology. By applying AI methods, the user can greatly
improve the classical information transmission process by virtue of improving the transmission
speed, reducing the transmission cost, and overcomi ng a series of bottlenecks in problems.
(Griffin , 2016; AI Topics , 2016).
The bulky data provided by a large number of data sources and the almost unlimited
computing power of cloud computing break the bottleneck that restricts development of AI and
enabl e implementation of the deep learning algorithm. In addition, deep learning enables
implementation of various machine learning applications and expands the scope of the AI
research. Deep learning has already been involved in numerous applications. In this respect,
artificial intelligence can be thought of as a ‘container’ of the human wisdom. Hence,
development of the deep learning algorithms and methods will expand this ‘container’ to an
extent that the humans cannot predict (Demski , 2007; Greenman, 201 7).
Accounting is one of the business fields in which the Information Technology (IT)
techniques have been widely applied. Albeit IT was first applied in the fundamental accounting
systems, financial modelling software soon later proved to be of highly -benefi cial use in the
analytical facets of accounting. However, the pace of IT adoption by the accounting profession
was regarded as slow owing to the conservative approach of its early adopters. By the late 1990s,
this profession was compelled to computerize it s processes and operations as a way of enhancing
their efficiency, eventually, to confront the competition and reduce the expenses (Manson et al. ,
1997 ; 2001). Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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Currently, the IT tools are commonly employed in a broad range of tasks, extending from
simple tasks like arithmetic computations to sophisticated ones like statistical analysis and
flowcharting. Those tools encompass the logit models; checklists; toolkits; expert systems;
encircling purposely -developed programs and standard software packages; audit enquiry
software that can perform in -depth analysis and testing of data; internal control templates that are
frequently utilized for identification of weaknesses and strengths of systems; and integrated audit
monitoring modules, which are programmed routi nes that continually monitor real data and their
processing circumstances (Omoteso, 2012).
The audit profession has substantially changed over time because of technological change.
Many changes in this profession have already been witnessed. They include a n increase in the
number and sophistication of the auditing rules, numerous changes in the standards of
professional ethics, an improved quality of the audit work, growing competition among the audit
firms, reduced audit fees, and provision of new services to the customers (e.g., financial and
computing advices). Additionally, this profession has witnessed development of new audit types
and services. These factors have together made the auditing profession more and more
competitive than ever before. Accordi ngly, the new methods and tools provided by the IT and AI
have been widely adopted by auditors. They made available more suitable and timely
information to facilitate and speed up the auditor’s decision -making process. Consequently, they
improved the audit efficiency and quality (Yaniv, and Bengio 2016; He et al., 2015; Silver et al. ,
2016; Sun & Vasarhelyi, 2016; Vasarhelyi et al., 1998 ).
Financial audit can be defined as the activity made by independent, skilled person for
analyzing the financial and econ omic information that are extracted from examined accounting
documents by using relevant review and verification methods. The objective of this activity is to
issue report that express the auditor’s opinion about reliability of that information so that thi s
information will be known by, and of use for, a third party (R. D. 1636/1990, Account Auditing
The audit areas wherein the expert systems can be employed are diverse and wide. They
almost include every audit task, where judgment of an audit professional is required. In terms of
their nature, the expert systems can be generally classified into three categories: (i) internal, (ii)
external, and (iii) EDP audit expert systems. So far, auditing proved to be the accounting domain
with the highest number of developed expert systems. This served as motive for the researcher to
research into this area and to investigate the extent to which the audit offices in Jordan use AI
techniques in evidence collection.
The knowledge gaps, which are indeed sub -problems that culminated to the research
problem, and which are addressed by this study, are four. First is lack of experience in the time
being in the application of AI methods in the area of audit evidence in Jord an, which is an
application that is still at its beginning, where even though application of automation is extensive,
scope of automation is mainly restricted to financial reporting. It has not yet reached to core
accounting areas like financial analysis a nd audit or made influence that can lead to changes in
the accounting standards. When AI is incorporated into the audit work, it should replace every
single step in the traditional audit work and provide proper decision -making suggestions in order
to profo undly enhance the overall financial work. Thus, whether in terms of its breadth or depth,
application of AI in the audit industry is still in embryo. Complexity of the AI technology and
the lack of experience in its use have created big difficulties for it s adoption and development. Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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Hence, a long way is still ahead to walk for development of AI in the audit area. Second is the
high investment costs and low returns for the firms that are needed to introduce AI into the audit
area. Thereupon, it is highly nec essary to design unique AI system that complies with the
characteristics of the audit profession and firms according to their actual situations. Firstly, the
capital investment is the most important warranty. Secondly, after introduction of the AI
technolo gy, it is necessary to modify the management of the human resources and the daily
operation routine of the firm. Lastly, once intelligent transformation of the audit information
system is accomplished, training should be made, including training on use of the new system
features and training on the information security. Because of the personalized features of the
intelligent systems, the audit profession will need a huge number of resources in early
application of the system and in its subsequent operation, which creates serious challenges to
control of the costs of the enterprises. Considering the high investment costs and slow returns,
many firms may concentrate on short -run profits rather than making strategic modifications.
Hence, they may stop at the ea rly stage of introduction of the AI technology. Third is that the
quality of the professional talents that is improved by application of AI technology in the audit
profession calls for professional talents to manage them, while the present senior accountin g
capabilities in Jordan are limited. Currently, paucity of the Jordanian accounting talents is
alarming; the basic accounting personnel are in surplus whereas the top accounting talents are
sparse. Within this context, integration of AI with the accountin g work creates heightened
demand on the accountants. So, the accounting personnel do not only need professional
knowledge in accounting, but they also need to master the IT and develop skill in use of the
accounting software and data management in order to adapt to the developments and the
associated changes in the work conditions. Lastly, the training programs of the accounting
students in the universities need modification and improvement.
Currently, most of the Jordanian universities offer appropriate co urses in accounting
computerization. However, affected by a number of external and internal factors, those courses
have some problems associated with them like unification of the contents of the courses, lack of
links between the theoretical knowledge and the practice, and difficulty of building a scientific
computerization system. These factors make it difficult to meet the requirements of development
of the profession with time. The university graduates are the main working force of the
accounting profess ion in the future, though, in the time being, the talent training programs in
those universities fail to make parallel adjustments for accounting education reform. Actually the
offered courses and training programs lack IT courses with an AI focus and pay limited attention
to innovation of accounting concepts. This results in lack of market competitiveness among the
university graduates and in their inability to meet the future market demand on accounting
1. To what extent doe s expert system affect the audit evidence from the point of view of certified auditors of
IT companies in Jordan?
2. To what extent does neural network technology affect the audit evidence from the point of view of certified
auditors of IT companies in Jordan ? Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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The general objective is to ascertain the effect of artificial intelligence on the audit
evidence from the point of view of certified auditors of IT companies in Jordan. The specific
objectives of the study included:
(i) To ascertain the effect of expert system on the audit evidence from the point of view of certified auditors of
IT companies in Jordan?
(ii) To ascertain the effect of neural network technology on the audit evidence from the point of view of
certified auditors of IT companies in Jordan?
SIGNIFICANCE OF THE STUDY
The importance of the study lies in the fact that it examines one of the new methods and
new systems used in the audit process by using audit offices operating in Jordan for artificial
intelligence techniques in the col lection of audit evidence, which is an important element in the
nature of the audit process where its importance in the accreditation of the auditor based on the
composition of his professional opinion is not contrary to the international audit standards o n the
one hand and the legislation adopted on the other. The study takes on additional importance in
First. Theoretical importance: This importance is highlighted by the theoretical and
intellectual enrichment that may contribute by tracking theo retical literature and previous studies
of the key variables related to artificial intelligence in the collection of audit evidence (expert
systems, neural networks) in IT companies in Jordan and in a form that is an integrated
conceptual framework for the se concepts and the methodology of their study. This importance
also highlights the ability to make a modest contribution by tracking theoretical literature and
previous studies of key variables in the form that is the conceptual and procedural framework o f
the study. In addition, the study will bring results to audit offices operating in Jordan in a way
that helps them to take advantage of artificial intelligence techniques in collecting audit evidence.
Second: Practical importance: the practical importanc e of this study comes from what it
can offer to decision makers in the audit offices operating in Jordan and the possibility of
benefiting from its results in a way that helps them to know the importance of artificial
intelligence technologies in this way in a way that does not conflict with international audit
standards, and this study is important in that it will address a topic related to the extent to which
audit offices use Artificial Intelligence techniques to collect audit evidence in these companies .
THEORETICAL FRAMEWOR K AND PREVIOUS STUDI ES
The Correlation between Artificial Intelligence and Audit
The AI literature is quite voluminous. It ranges from algorithmic essays (e.g.,
Courbariaux et al ., 2016) to broad set of applications in varying researc h areas (Zhang et al.,
2015; Silver et al. , 2016). However, research into AI in auditing is limited. Moreover, the
overwhelming majority of the ‘now -aged’ publications are centered on the expert systems. These
systems have been often advocated as systems w ith potential for use in tax planning and in the
audit process. Gillett (1993) developed audit expert system (AES) to help auditors in tailoring
the audit programs and described the initial steps of the long execution process ( Vasarhelyi et al.,
1998 ). Furthermore, during the period 1989 -2005, six volumes of book series were published Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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that covered variety of the expert system applications and discussed the added values which
these systems lent to accounting and auditing ( Vasarhelyi et al., 1998 ).
Stud y conducted by Issa et al. (201 6), Research Ideas for Artificial Intelligence in
Auditing: The Formalization of Audit and Workforce Supplementation. This paper proposes
various areas of AI -related research to examine where this emerging technology is most
promising. Moreover, this paper raises a series of methodological and evolutionary research
questions aiming to study the AI -driven transformation of today’s world of audit into the
assurance of the future. Bai (2017) , this paper introduces the present sit uation of the application
of artificial intelligence in the field of audit services in the four major international accounting
firms, analyzes the impact of artificial intelligence on the audit industry and the relevant auditing
practitioners, and regulato rs who are responsible for the industry regulations. To take an in -depth
analysis of the coping strategies.
Study of Kokina & Davenport (2017) provides an overview of the emergence of artificial
intelligence in accounting and auditing and discuss the curre nt capabilities of cognitive
technologies and the implications these technologies will have on human auditors and the audit
process itself. We also provide industry examples of artificial intelligence implementation. The
same context Omoteso (2012) via the application of artificial intelligence in auditing: Looking
back to the future. discussed the significance of auditors’ use of artificial intelligent systems in
arriving at audit judgements. Specifically, it reviewed research efforts on the use of expert
systems and neural networks in auditing and the implications thereof.
Gusai (2019) , this study aimed to study the importance of artificial learning in accounting
and auditing areas and measure the decree of forthcomings regarding artificial intelligence in
accounting. Conclusion This study AI paves way for a better and conducive environment in the
field of accounting and auditing. Development in the field of AI can definitely be a great help to
Greenman (2017) exploring the Impact of Artificial Intelligence on the Accounting
Profession. AI is a vital tool that will provide these professionals with the needed tools to
increase the efficiency and effectiveness of their occupations. The repetitive tasks of
bookkeeping or process -driven as signments are more likely to be replaced with an automated
technology than the higher value specialties that involve professional judgment. Many believe
that the younger generation of accountants need to understand and be prepared to work alongside
artific ial intelligence.
Li & Zheng (2018) , this paper focus on how to use artificial intelligence to avoid
accounting fraud and to generate positive impact on accounting information quality, this article
analyzed how artificial intelligence effect the accountin g personnel. the article underline that in
the big picture of artificial intelligence, accounting personnel should improve its own seven
aspects of abilities and become a comprehensive qualified personnel.
Luo et al. (2018) , this paper takes the applicatio n of artificial intelligence in the
accounting industry as the research object, analyzes the impact of artificial intelligence on the
development of accounting industry, and puts forward relevant suggestions for its existing
Chukwudi al. (201 8). The aim of this study is to examine the effect of artificial
intelligence on the performance of accounting operations among accounting firms in South East
Nigeria. The result of the study showed that Expert system has a significant effect on the
performanc e of accounting function of accounting firms in South East Nigeria. It was concluded Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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that, the application of artificial intelligence positively influences the performance of accounting
As contained in the statement of object ives, it is logical to identify relationship between
(artificial intelligence and audit evidence) that now form the bases of the hypotheses of the study.
The resultant hypotheses formulated in order to carry out this research are as follow;
H01: Expert sys tem has no significant effect on the audit evidence from the point of view of certified
auditors of IT companies in Jordan.
H02: neural networks has no significant effect on the audit evidence from the point of view of
certified auditors of IT companies in Jordan.
Research design is very crucial to actualize the research objectives (Bhatti et al, 2012 ).
This study applied a quantitative research design. Quantitative research design will enable the
researcher to test the relationship bet ween the research variables. It will also enable the
researcher to unvaryingly determine if one concept or idea is better than the others. It can also
respond to questions on the relationships that exist among measured variables with the aim of
elucidating , envisaging, as well as controlling phenomena (Sekaran & Bougie, 2016). Thus,
quantitative research design is an appropriate method for this study since it permits testing the
relationship between variables with the use of statistical approaches. This is in line with the main
objective of this study that focus. Thus, quantitative research design is an appropriate method for
this study since it permits testing the relationship between variables with the use of statistical
approaches. (Sekaran & Bougie, 2016 ). This is in line with the main objective of this study that
to examine the extent to which audit offices in Jordan use artificial intelligence technologies to
collect audit evidence, from the point of view of certified auditors in IT companies in Jordan
Therefore, the specific question quantitative research also permits to carry out analysis using
large sample to generalize the results among a set of population. Population and sample of the
Population and Sampling
Sekaran & Bougie (20 16) define po pulation as the entire group of people, events, or
things of interest that the researcher wishes to investigate. The population size of this study
consists of (582) licensed auditors and practitioners and exercises the audit function of the 220
IT companie s registered in the Association of Information Technology Companies in Jordan. As
stated by Sekaran & Bougie (2016), “the level of aggregation of the data collected during the
subsequent data analysis stage” is known as a unit of analysis. Therefore, the unit of analysis is
individual based, means that data was collected from licensed auditors and practitioners is the
unit of analysis of the study. There are two types of sampling methods which arenon -probability
and probability samplings. Th e researchers in this study opted probability sampling method
which is inferred as simple random sampling technique. By that, each aspect pertaining to the
selected population may be represented in the sample (Zikmund et al., 2013).
As recommended by Krejc ie & Morgan (1970), the appropriate sample size for a
population size of 582 is 274. In order to lessen sample size error and putting into consideration
the occurrence of non -response by some respondents, the sample size was increased by as Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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suggested by Ba rlett, et al. (2001). Therefore, the sample size of this study had become by
(274+40=314). Hence, 314 questionnaires were distributed to the sample, eleven of them were
excluded because they were not filled completely or correctly so (303) questionnaires w ere valid.
Instrument for Data Collection
The survey instrument is designed by adapting related items from past related studies of
the variables being investigated. Structured questionnaire was used to obtain data for the study.
The questionnaire was divid e into two sections. Section (A) information on Artificial
Intelligence Technologies while section B, information on Audit Evidence, The questionnaire
items relating to the study objectives were structured in Likert scale is a five points.
Data Analysis Te chniques
The main goal of this study is to test the research hypotheses in line with the study’s
conceptual framework. As this study is quantitative in nature, it intends to empirically justify the
proposed theoretical frame by analysing of the relationshi ps between variables. two major
analyses were involved. The first is descriptive analysis and test the research hypotheses by were
used within the program (SPSS Statistical Package for Social Science.
Validity of the Instrument
The questionnaire was proper ly designed and a conduct of a pre -test on every question
contained in the questionnaire was carried out to ensure validity. The researcher subjected the
instrument to face and content validity by giving it to five experts and specialists in artificial
int elligence and accountants, who studied the instrument thoroughly to ensure they are in line
with the objectives of the study.
Reliability of the Instrument
Procedurally, the researchers pre tested thirty (30) copies of the test instrument before the
actua l survey for the study. The responses obtained from the pre -study survey were subjected to
the Cronbach Alpha’s internal consistency test via SPSS (statistical package for social sciences) .
Based on the inter -item correlation of Twelve (16) items on the q uestionnaire the result of the
reliability test is 0.88. Since the item on the questionnaire were uniformly scaled and in
accordance to the Sekaran & Bougie ( 20 16), benchmark of Cronbach’s alpha should be 0.700 or
above. The raw Alpha Coefficient of 0.88 s hows that the items on the questionnaire are
internally consistent, hence they are reliable.
METHOD FOR DATA ANAL YSIS
First: Results of Descriptive Statistics
Means and standard deviations to the extent to which audit offices in Jordan use expert
systems technology to collect audit evidence and the following tables show the results from the
point of view of the study sample members, as follows:
MEANS AND STANDARD DEVIATIONS FOR EXPERT SYSTEMS
Items rank Items Mean Standard
4 Application software based on knowledge bases is used
in a particular area of expertise 3.776 0.7299 High Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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Expert systems are used to collect audit evidence by
reformulating them in the form of computer -hosted
3.564 0.6804 High
2 Expert systems are used to extract knowledge to resolve
problems with the collection of audit evidence 3.905 0.6835 High
7 Advanced software languages are us ed to improve the
collection of audit evidence 3.670 0.8221 High
1 Expert systems are used to be able to advise and make
the right decisions regarding audit evidence 3.917 0.6212 High
6 Knowledge and control of the search for audit evidence
are represented within databases 3.694 0.6730 High
7 The expert system is used as a hierarchical frame that
reflects the accounting knowledge set for audit evidence 3.858 0.6006 High
The expert system is used to collect audit evidence to
be encrypted in a program and stored in the system's
3.752 0.7385 High
Total 3.767 High
Table 1 indicates that the total mean of the extent to which audit offices in Jordan use the
technology of expert systems in the collection of audit evidence, from the point of view of
certified auditors in IT companies in Jordan, have reached a high level of (3.767). The standard
deviations of the terms covered by this variable indicate the extent to which the values of this
variable are dispersed from the means of all items, noting that they are low and indicate that the
responses of the sample study are very similar and consisting.
Second: Means and Standard Deviations to the Extent that Audit Offices in Jordan Use
Neural Network Technology to Collect Audit Evidence
MEANS AND STANDARD DEVIATIONS FOR COLLECT AUDIT EVIDENCE
Items rank Items Mean Standard
Neural network technology is used to complete the
collection and practical implementation of integrated
electronic audit evidence.
3.835 0.6874 High
Neural networks are used to store information about
the collection of evidence for the collection of links
3.729 0.6967 High
Electronic processing units are available for the
collection of neurons that make information available
Neural networks are used in mathematical models of
audit guides formulated in diagrams that mimic the
qualities found in computer systems
3.588 0.8351 High
Neural networks are used to process information on
audit evidence and provide solutions to complex
problems in parallel
3.564 0.6804 High
Neural networks contribute to providing solutions
and recommendations to the user in a clear and
accurate picture about the evidence of auditing
3.905 0.6835 High
Neural networks allow the user to enter instructions
and information related to audit evidence to obtain
3.670 0.8221 High
5 Neural networks can explain the steps of collecting 3.917 0.6212 High Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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audit evidence to reach the solution and the reasons
behind this solution
Total .37.3 High
Table 2 indicates that the total means of the extent to which audit offices in Jordan use
neural network technology to collect audit evidence, from the point of view of certified auditors
in IT companies in Jordan, have reached a high level of (3.739). The standard deviations of the
terms covered by this variable indicate the extent to which the values of this variable are
dispersed from mean of all items, noting that they are low and indicate th at the responses of the
sample study are very similar and consisting.
The data collected from the Sample was analyzed. Inferential statistic of regression
analysis was used in testing the study hypotheses at 5% level of significance. The decision will
be, Reject H0 if the p -value is less than 0.05.
Expert system has no significant effect on the audit evidence from the point of view of
certified auditors of IT companies in Jordan.
Table 3 shows the linear regression result of expert system and audit evidence from the
point of view of certified auditors of IT companies in Jordan. The result which sort to reveal the
effect of expert system on audit evidence, revealed that there is a strong positive relationship
between expert system and audit evidence (R -coefficient=0.906). The R square, the coefficient
of determination, shows that 84% of the varia tion in audit evidence can be explained by expert
system no autocorrelation as Durbin - Watson (0.722) is less than 2. The extent to which expert
system affect audit evidence with the .906 value indicates a positive significance between expert
system and au dit evidence which is statistically significant (F –statistics=709.457; t=24.767) and
p=0.000 < 0.05.Therefore, the null hypothesis is rejected and the alternate hypothesis accepted
accordingly, hence expert system has a significant effect on the audit evid ence.
Neural networks has no significant effect on the audit evidence from the point of view of
certified auditors of IT companies in Jordan.
Table 4 shows the linear regression result of neural network technology and audit
evidence from the point of view of certified auditors of IT companies in Jordan. The result which
sort to reveal the not effect of neural network technology on audit evidence, revealed that there is
not strong positive relationship between expert system and audit evidence (R - coefficient =
0.081). The R square, the coefficient of determination, shows that 06.0 % of the variation in
audit evidence can be explained by expert system. The extent to which neural network
RESULTS OF REGRESSION ANALYSIS FOR EXPERT SYSTEM AND AUDIT EVIDENCE
R R square F-statistic Sig t-test
.906 0.847 709.457 0.000 24.767
RESULTS OF REGRESSION ANALYSIS FOR NEURAL NETWORK TECHNOLOGY AND
R R square F-statistic Sig t-test
0.081 0.006 0.525 0.470 0.724 Academy of Accounting and Financial Studies Journal Volume 24, Special Issue 2, 2020
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technology affect audit evidence with the 0.081value indicates not positive significance between
neural network technology and audit evidence which is statist ically significant (F –
statistics=0.525; t=0.724) and p=0.470. Therefore, the null hypothesis is rejected and the
alternate hypothesis accepted accordingly, hence neural network technology has not significant
effect on the audit evidence.
Even though Artificial Intelligence is promising now and the future, most Researchers
and organizations are oblivious to adopt the skills and knowledge that it demands. From the
researcher’s experience, there is the need to assess the impact of Artificial Intel ligence on audit
evidence. Therefore, the purpose of this study is to explore the impact of Artificial Intelligence
on audit evidence in order to the acquisition to skills and knowledge.
This present study takes a contemporary issue on the integration of A rtificial Intelligence
in audit evidence which tries to look at the universal questions raised by investigators or
researchers. The two fundamental questions addressed in this study to what extent does expert
system affect the audit evidence from the point of view of certified auditors of IT companies in
Jordan. And To what extent does neural network technology affect the audit evidence from the
point of view of certified auditors of IT companies in Jordan. This is the research problem
addressed by this stu dy. The information from this study will help the researchers on vistaget a
Impact of Artificial Intelligence in audit evidence from the point of view of certified auditors in
IT companies in Jordan. In this study, the primary source was a Questionnaire co nducted on
certified auditors in IT companies in Jordan.
Based on the findings of the study, it is recommended Researchers that:
1. Increased interest in Artificial Intelligence technologies by audit offices operating in Jordan because it is
practically important in improving the collection of audit evidence.
2. Emphasize the need to use sophisticated software languages and encrypt them in a program and save them
in the system's knowledge base to improve the collection of audit evidence.
3. Emphasiz e the importance of using neural networks in mathematical models of audit guides formulated in
diagrams that mimic the qualities found in computer systems.
4. Audit offices operating in Jordan must provide electronic processing units for the collection of aud it
evidence in the form of neurons that make information available to users.
5. Focus on training auditors to keep pace with technological advances in AI applications in collecting audit
guides, representing knowledge, and controlling the search for such evidence within databases.
6. Importance of relying on the use of smart software to develop the process of collecting and reformulating
audit evidence in the form of computer -embraced software for its role in improving the quality of the audit
7. Make b etter use of neural networks especially with regard to providing solutions and the reasons behind
this solution and recommendations to the user in a clear and accurate form about the audit guides.
8. Pay more attention to giving auditors many opportunities to develop and practice the application of
artificial intelligence methods because of their importance in improving the collection of audit evidence.
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