The Impact of Artificial Intelligence (AI) on the Accounting System of

Saudi Companies

RANDA ABD ELHAMIED MOHAMMED HAMZA1, NASARELDEEN HAMED AHMED

ALNOR1*, EBRAHIM MOHAMMED AL-MATARI1,2, ZAKIA SEID BENZERROUK3,

ABDELWHAB MUSA ELGALI MOHAMED3, MOHAMED YOUCEF BENNACEUR1,

AHMED HESHAM MOAWED ELHEFNI3, MONA M. ELSHAABANY1

Jouf University,

Jouf University,

Abstract: - As a major player in the world market, Saudi Arabia has seen substantial adoption of artificial

intelligence AI) technology in its commercial environment. This study intends to thoroughly examine the

specific effects of AI on Saudi business accounting systems. This paper offers comprehensive knowledge of the

consequences of AI application in the accounting sector through a thorough examination of the body of existing

research findings is the ability of AI to process enormous volumes of data quickly and accurately, allowing for

improved financial analysis, risk assessment, and forecasting. This facilitates wiser and more strategic business

decisions. AI also simplified accounting processes and decreased the need for human labor, saving Saudi

enterprises money. As a result, resource allocation was optimized and overall financial performance was

enhanced.

Key-Words: - Artificial intelligence (AI), accounting system, accounting, financial data, analysis, forecasting,

technology, integration, commercial environment, knowledge, Saudi Arabia.

Received: August 9, 2023. Revised: November 27, 2023. Accepted: December 26, 2023. Published: January 5, 2024.

[1]. The fast-growing economy of Saudi Arabia is

not immune to the impact of this revolution, [2], [3].

The incorporation of artificial intelligence (AI) has

sparked a paradigm change, especially in the field of

accounting, changing the traditional boundaries of

financial management and reporting for businesses

complex effects of AI on Saudi Arabian enterprises'

accounting procedures, business processes, and

decision-making frameworks, as it continues to

technologies, this study explores the profound

implications of artificial intelligence integration and

its impact on the accounting system, [6].

This study aims to evaluate how artificial

intelligence (AI) is currently being used in Saudi

companies' accounting systems and to determine the

extent of its adoption, [7]. In addition, the

technical constraints, [9]. And analyze how the use

of artificial intelligence (AI) affects the duties and

responsibilities of accounting workers in Saudi

companies, noting any changes in the skills or job

tasks required of them, [10]. We examine the

financial effects of incorporating artificial

intelligence (AI) into the accounting systems of

Saudi businesses, considering revenue growth, cost

reductions, and return on investment, [11]. In

addition, the possible dangers of integrating

accounting systems, [12]. To determine the best

techniques and approaches for integrating artificial

intelligence (AI) into accounting systems for Saudi

companies to address economic events while

considering ethical, regulatory, and technological

aspects, [13]. Our research offers suggestions for

improving the integration and application of

artificial intelligence (AI) in Saudi Arabian

enterprises' accounting systems, while accounting

for infrastructural needs, skill development

Several advantages have resulted from the

incorporation of artificial intelligence (AI) into

accounting systems, which has completely changed

how financial data are handled, processed, and

evaluated, [16]. Artificial Intelligence reduces the

need for manual intervention by streamlining

repetitive operations such as data entry,

reconciliation, and reporting, [17]. This automation

reduces the possibility of human error while

simultaneously increasing efficiency, [18]. Large

datasets may be analyzed more accurately and

consistently using artificial intelligence (AI)

algorithms, reducing the possibility of errors in data

analysis and financial reports, [19]. This feature is

very important for preserving the accuracy of

financial records and guaranteeing adherence to

legal requirements, [20]. Artificial intelligence (AI)

financial planning, [22]. Accounting systems using

artificial intelligence (AI) capabilities can find

trends and abnormalities in financial data, which can

be used to identify possible fraud or other financial

irregularities, [23]. Artificial Intelligence (AI) can

reduce the likelihood of fraudulent acts and offer

early warnings by continuously monitoring

transactions and financial activities, [24]. Artificial

intelligence (AI) can help firms become more

proactive and produce more accurate forecasts using

efficiency and automating jobs related to traditional

accounting processes, [27]. Businesses can devote

resources to other vital areas of growth and

innovation because of their potential for cost

savings, [28]. Finally, artificial intelligence (AI) can

provide individualized financial insights tailored to

the unique requirements of companies, [29].

Artificial Intelligence (AI)-powered accounting

systems can provide personalized recommendations

and tactics to maximize financial performance,

such as data entry, reconciliations, and financial

reporting are becoming automated by artificial

intelligence (AI), [27]. Analyze how this automation

affects accounting procedures' overall productivity,

accuracy, and efficiency, [31]. Third, we examined

the applications of artificial intelligence (AI) in risk

management and fraud detection, [32]. To examine

how well artificial intelligence (AI) systems detect

fraud and reduce financial risk in businesses, [33].

Fourth, we examine how artificial intelligence (AI)

might help with financial analysis and data-driven

decision-making, [34]. Examine how artificial

intelligence (AI)-driven tools and algorithms

improve accounting systems' financial insights,

predictions, and strategic planning, [35]. Fifth,

consider the moral ramifications of using artificial

intelligence (AI) in accounting software, [36].

the talents and skills that are becoming increasingly

important in the context of artificial intelligence

(AI) integration and consider any possible effects on

the accounting industry, [39]. Seven, consider the

difficulties in implementing and adopting artificial

intelligence (AI) in accounting systems, [40].

Evaluate obstacles, including expenses, intricacy of

technology, and reluctance to modify, and suggest

approaches for effective incorporation and

acceptance, [41]. Eight studies examined how

intelligence (AI) affects accounting systems'

financial forecasting accuracy, [43]. Analyze how

well artificial intelligence (AI)--based financial

forecasting models perform compared to traditional

techniques and determine what influences increased

or decreased financial forecasting and planning

accuracy, [44]. Ten studies examine how artificial

intelligence (AI) improves accounting auditing

procedures and methods, [45]. To examine the use

of artificial intelligence (AI) to identify financial

accounting systems, corporate governance

procedures, and financial information disclosure,

[48].

on companies’ accounting systems? This question is

divided into the following sub-questions: How does

the use of artificial intelligence in data analysis and

forecasting support the accounting system in Saudi

companies? How does the integration of artificial

intelligence in integration with other systems

support the accounting system in Saudi companies?

accountants may need to acquire to stay relevant and

provide value, there is an urgent need for more

research, such as, [38].

The world economy has recently peaked, and to

sustain competitive advantages and additional

economic growth, businesses must implement the

newest artificial intelligence-based technology, [51].

This also holds for accounting systems, which are

driven by market forces to implement the newest

technology as well as the newest practices, [52].

Arabia, [54].

In the accounting industry, artificial intelligence

(AI) is used for data analysis and forecasting, [55].

Using artificial intelligence (AI), past data can be

analyzed to identify patterns and trends that can be

utilized to predict future financial performance. This

facilitates the creation of more precise forecasts and

well-informed business decisions, [56]. By

evaluating numerous scenarios and offering insights

into the possible effects of various tactics on a

company's financial health, artificial intelligence

hazards, [58]. By reliably extracting data from

invoices, receipts, and other financial documents,

artificial intelligence (AI)-powered optical character

recognition (OCR) technology can reduce manual

data input errors and save time, [59]. Accounting

professionals can take proactive steps in real-time

using artificial intelligence (AI) algorithms to

discover trends and abnormalities in financial data

that may indicate fraud or other unexpected activity,

[60]. Accountants can gain important insights from

unstructured accounting data including emails,

published financial reports, and client conversations

using natural language processing (NLP). This

Artificial Intelligence (AI) has the potential to

mitigate non-compliance and related fines by

automatically monitoring transactions and financial

operations. This helps to ensure compliance with

accounting standards and regulations, [63]. Finally,

artificial intelligence (AI)-powered decision support

tools help accountants make well-informed data-

driven judgments by providing real-time insights

and recommendations based on complex financial

data, [64].

for forecasting and data analysis.

To facilitate more precise and efficient financial

operations, artificial intelligence (AI) is increasingly

being utilized to combine accounting systems with

other systems, [66]. Artificial intelligence (AI) can

be used to automatically consolidate and integrate

data from many sources, including customer

relationship management (CRM), enterprise

resource planning (ERP), and other operational

systems, [67]. Artificial Intelligence (AI) can map

and normalize data from several formats using

human data input, [59]. This simplifies the

integration of accounting data with other systems,

[69]. The AI's ability to continuously process and

analyze financial data makes real-time reporting and

analysis possible, [70]. Organizations can obtain

real-time insights into financial patterns, anomalies,

and key performance indicators (KPIs) by

combining accounting systems with artificial

intelligence (AI)-powered analytics tools. This

allows for more informed decision-making and

strategy formulation, [71]. By examining and

recognizing patterns and trends in past financial

data, artificial intelligence (AI) can increase

intelligence (AI) can assist in the early detection of

any fraudulent activity by detecting anomalies and

suspicious activities in financial transactions, [74].

Through the integration of artificial intelligence

(AI)-driven fraud-detection systems with accounting

systems, entities can optimize their risk-mitigation

strategies and guarantee the accuracy and safety of

fiscal information, [63]. Finally, artificial

intelligence (AI) can automate regular accounting

workflows and operations, including financial

systems, [64].

The results of previous studies have shown that

artificial intelligence is used in integration with

other systems, such as, [55], [66], [76]. In light of

the above discussion, writers can make the

following assumption:

H1: Artificial intelligence (AI) supports accounting

data analysis and forecasting.

This study used a survey design with a sample of the

study population to evaluate the hypotheses. The

sample members provided data on accountants,

auditors, and employees of the Saudi companies. A

useful research technique for gathering information

of Saudi businesses comprised our sample, and data

were gathered and distributed via Google Forms.

Given that the study was conducted in English, the

questionnaire was translated into language

procedure was used to select a sample of desired

respondents. Specifically, after random selection,

213 out of 375 issued questionnaires— 56.8% of the

total) were returned. The completed surveys were

discarded, and those that remained were utilized for

the data analysis phase.

respondents was coded and assessed using “the

Statistical Package for Social Sciences” (SPSS

version 22). Descriptive and inferential statistics

were used to analyze the data. Several marks were

awarded for closed-answer questions. The

descriptive statistics included means, frequencies,

percentages, and inferential statistics. Pearson’s

investigation. they are making use of a survey. The

data were cleansed to guarantee dependability and

accuracy. As a vital tool in research and decision-

making, statistical analysis provides an impartial,

methodical way to examine and understand data.

They are crucial in many fields such as scientific

research, corporate data analytics, and

policymaking.

83.6% of the total) did not have fellowships. Then

came the others, where 13 people, or 6.1% of the

total were present. The American Fellowship came

next, with eight members (3.8% of the total) in that

group. The seven recipients of the British

Fellowship and the Arab Fellowship together

accounted for 3.3% of the total for each.

participants (13.1%). Others came next and

numbered 16 people (7.5%). Finally, information

technology was found in 11 participants (5.2%). The

information displays the frequency and percentage

breakdown of workers in Panel F’s job positions. Of

the sampled members, 142 worked as accountants,

accounting for 58.2% of the workforce. The cashier

department, which employed 47 people and

represented 19.3% of the sample, followed. The

next in line was the department head, with 31

%, or 63 people, had five–ten years of experience. A

total of 23.9% of the participants (51 individuals)

with 11–15 years of experience came next. Then

came 48 participants, or 22.5% of the total, who had

fewer than five years of experience. 16.4% of the

participants (35 individuals) with 16–20 years of

experience came next. Finally, 16.5% of participants

had more than 20 years of experience.

As shown in Table 2, all the means of the

variables obtained a high degree of agreement

greater than 3.900 and low standard deviations of

0.869, 0.853, and 1.191.

Table 2. Descriptive statistics of the variable’s

indicators

Two terms frequently used in the field of research,

outcomes. The reliability study's findings show that

the research instrument is a reliable predictor of the

attitudes and views Saudi enterprises have about

artificial intelligence and the accounting system.

The factor loadings for several items show that each

one is a strong indicator of the underlying construct

it was intended to evaluate, with high factor

loadings and statistically significant F values. The

impact of artificial intelligence on Saudi enterprises,

as indicated by Cronbach's alpha, has similarly high

internal consistency reliability, indicating a high

degree of consistency among the various parts in

evaluating these constructions. Saudi companies'

contribute to the measurement of the underlying

construct, [81]. Eliminating indicators with low

factor loadings can enhance the construct validity of

the measurement tool and dependability of the

factor solution's dependability, [82]. Otherwise, if

the validity and reliability rates of these variables

are less than 0.6, it is possible that they will not be

able to effectively direct the researcher to the core of

the relationship that you are trying to evaluate.

An indicator was excluded from the study if the

that internal consistency reliability is considered

acceptable if Cronbach's alpha value is at least 0.7,

[83]. The results of the analysis showed in Table 3

that with a value of 0.892 and 0.781, respectively,

both for using artificial intelligence in data analysis

and forecasting, and the use of artificial intelligence

in integration with other systems possess Cronbach's

alpha values far higher than this threshold.

Likewise, composite reliability values of 0.9 or

greater are thought to indicate a trustworthy

opinions regarding the advancement of artificial

intelligence and its potential effects on the system.

High composite reliability values, good factor

loadings, statistically significant p-values, and good

internal consistency reliability show that the

instrument can assess these constructs in a

legitimate and trustworthy way.

usually used to determine the extent to which the

research variables are related by analyzing all

possible correlations between the variables, [85].

Table 4 displays the average variance extracted

(AVE) for the two variables. Artificial intelligence

has an impact on Saudi business accounting

systems. The percentage of variance in a given

construct that can be explained by the metrics used

to evaluate it is represented by the commonly used

AVE index of construct-dependability. For a single

The correlation coefficient was used to measure the

linear relationship between the strength and

one measure increases, the other decreases

accordingly. Furthermore, a nearly zero correlation

score indicates little to no linear relationship

between the variables.

Correlation coefficient interpretation also

requires careful consideration of the study

objectives and the data context. To obtain

significant conclusions, a correlation analysis should

be performed in conjunction with other statistical

whereas the P value of 0.000 and the T statistic of

38.683 show that there is a statistically significant

relationship between the use of AI in data analysis

and forecasting.

In addition, in Table 6, the R2 value of 0.715

indicates that 71.5% of the variance in the

accounting system in Saudi companies can be

approximation of the range of the conceivable beta

coefficient values.

Table 6. Hypotheses Testing

and analytical roles. This may result in increased

productivity and decreased human error.

This study and other studies have numerous

contributions that can be considered. One of the

contributions that did not receive appropriate

scholarly attention was the analysis of how artificial

intelligence (AI) affected Saudi Arabian enterprises'

of the few empirical studies that explicitly address

the effect of artificial intelligence (AI) on Saudi

enterprises' accounting systems. Using techniques

such as cost-benefit analysis, moderating factors,

causation and correlation, measurement and metrics,

and case studies, this study contributes in this way.

Ascertain how Artificial intelligence (AI) and

affects Saudi corporations' accounting systems.

concentrate on other variables that can improve and

clarify the artificial intelligence and accounting

system relationship of Saudi enterprises as

moderators and mediators. Other academics may