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 to

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THE IMPACT OF ARTIFI CIAL INTELLIGENCE

TECHNOLOGIES ON AUDI T EVIDENCE

Shaher Falah Al -Aroud, Al Isra University

ABSTRACT

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 .

INTRODUCTION

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

Regulation).

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.

PROBLEM STATEMENT

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

professionals.

RESEARCH QUESTIONS

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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RESEARCH OBJECTIVES

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

two ways:

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

human efforts.

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

problems.

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

functions.

RESEARCH HYPOTHESIS

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 METHODOLOGY

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

study.

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:

Table 1

MEANS AND STANDARD DEVIATIONS FOR EXPERT SYSTEMS

Items rank Items Mean Standard

Deviation

Degree of

Importance

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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8

Expert systems are used to collect audit evidence by

reformulating them in the form of computer -hosted

software

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

8

The expert system is used to collect audit evidence to

be encrypted in a program and stored in the system's

knowledge base

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

Table 2

MEANS AND STANDARD DEVIATIONS FOR COLLECT AUDIT EVIDENCE

Items rank Items Mean Standard

Deviation

Degree of

Importance

3

Neural network technology is used to complete the

collection and practical implementation of integrated

electronic audit evidence.

3.835 0.6874 High

4

Neural networks are used to store information about

the collection of evidence for the collection of links

and communications

3.729 0.6967 High

7

Electronic processing units are available for the

collection of neurons that make information available

to users

8

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

2

Neural networks are used to process information on

audit evidence and provide solutions to complex

problems in parallel

3.564 0.6804 High

6

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

1

Neural networks allow the user to enter instructions

and information related to audit evidence to obtain

accounting information

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

9 1528 -2635 -SI-24-2-626

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.

Hypotheses Testing

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.

Hypothesis Two

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

Table 3

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

Table 4

RESULTS OF REGRESSION ANALYSIS FOR NEURAL NETWORK TECHNOLOGY AND

AUDIT EVIDENCE

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.

CONCLUSIONS

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.

RECOMMENDATIONS

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

process.

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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