ROLE OF MACHINE LEARNING AND BIG DATA MINING IN FINANCIAL DECISIONS

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

20

https://www.theamericanjournals.com/index.php/tajmei

PUBLISHED DATE: - 09-07-2024
DOI: -

https://doi.org/10.37547/tajmei/Volume06Issue07-03

PAGE NO.: - 20-38

ROLE OF MACHINE LEARNING AND BIG
DATA MINING IN FINANCIAL DECISIONS

Shmal kamel Hassan AL- Khafaji

University of Sumer, Iraq

Jasim Idan Barrak

The Faculty of Administration and Economy, University of Karbala, Iraq

INTRODUCTION

Big data mining and machine learning

technology seek to transform the vast sea of

information into sources of deep analysis and
comprehensive understanding through big data

that appears amazingly quickly from multiple
sources, from social media to sensors and

websites.

These

developments

emdiv

enormous challenges and opportunities in

modern business, where understanding big
data becomes crucial for making strategic

decisions with confidence and effectiveness. In

the financial context, big data is reflected in its
crucial role in enabling sound financial

decision-making. Financial decisions are vital to
ensure companies' continued growth and

financial success. It includes accurate financial

analyses, reliable investment estimates,
selection of appropriate financing sources, and

setting future financial goals.
Hence, the financial decision-making process

requires a comprehensive analysis that includes

economic, political, social, and environmental
factors to reach solid and sustainable financial

strategies in light of a changing and volatile
reality. From this point of view, the research

was designed into three papers: The first

included the research methodology and
previous studies, the second dealt with the

theoretical side by presenting the literature on
research variables, and the third reviewed the

RESEARCH ARTICLE

Open Access

Abstract

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

21

https://www.theamericanjournals.com/index.php/tajmei

practical side.

The First Topic
Research methodology
1.Research Problem

The accounting field faces many challenges

related to big data, which is considered one of

the most pressing. These include rapid changes
in the financial and economic environment, the

phenomenon of financial corruption, the effects
of globalization, a lack of knowledge of the latest

developments in Information Technology, and
other challenges. We must plan quickly to

overcome these obstacles and achieve the goals
of the accounting profession by empowering

accountants and taking advantage of the
possibilities of Information Technology. The

quality of accounting information is a vital issue
today, especially after financial crises that

negatively affected users of financial reporting.

Information is essential in decision-making,
strategic policy development, and company

planning. Despite the abundance of data in this
era, extensive data analysis is a big challenge for

accountants and decision-makers who need
help processing and using this data effectively.

Therefore, analyzing big data and its role in
accounting is an important topic that requires

careful discussion and analysis. Moreover, from
the problem of research, the following

questions arise:

The First question:

In the realm of big data,

does the use of mining technology have a
discernible impact on financial decisions?

The Second question:

Does the use of machine

learning technology affect financial decisions

2. Research Importance

The importance of the research lies in

understanding how the purification of extensive
data mining and machine learning can play a

crucial role in improving financial decision-
making processes through the importance of

using big data analytics to examine and analyze
financial statements comprehensively and

accurately, enabling financial analysts and

decision makers to understand trends, patterns,

and factors that affect the financial performance
of the company. Moreover, data mining

highlights the importance of improving
financial decision-making processes, including

providing accurate forecasts about future
financial performance, identifying potential

opportunities and challenges, and improving
financial risk management. Data mining

technology can enhance the ability of

companies to make informed and data-driven
financial decisions, contributing to financial

success and sustainability in business.

3. Research Aims

This research aims to:
A.Measuring the impact of big data mining

technology on financial decisions and making in
companies.
B.Measuring the impact of machine learning

technology as a data mining and machine

learning technology on financial decisions .

4. Research Assumes

A.Data mining technology as a data mining and

machine learning technology positively

influences financial decisions.
B.Machine learning as a data mining and

machine learning technology positively
influences financial decisions .

5. Research Variables

A.Independent variables: machine learning and

big data mining.
B.Dependent variable: financial decisions.

6. Research Methodology

The research adopted the inductive approach in

reviewing and analyzing the literature from

multiple sources, including foreign, Arab, and
local references. These sources included books,

scientific theses, and scientific articles
published in scientific journals and reviewing

websites available on the International

Information Network. Moreover, all these
sources have contributed significantly to

expanding and strengthening the theoretical
side of research.

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

22

https://www.theamericanjournals.com/index.php/tajmei

In the practical aspect, the questionnaire is

designed as a data collection tool. The

questionnaire consists of a number (178) of
questionnaires distributed to a sample of

(accountants, financial analysts, chartered
accountants, financial managers, and IT

experts) working in accounting offices and Iraqi
companies listed on the Iraq Stock Exchange. A

number of (156) valid questionnaire forms

were retrieved for analysis and analyzed using
the advanced smart-pls statistical program for

statistical analysis purposes.

7. Search limits

Spatial boundaries: The spatial

boundaries are represented in a survey of a
sample of (accountants, financial analysts,

financial managers, and information technology
experts) working in accounting offices and Iraqi

companies listed on the Iraq Stock Exchange .
Time limits: A questionnaire was distributed for

the period from 16/5/2024 to 20/6/2024

8. The default model of the theoretical

research framework

In light of the research hypotheses, objectives,

and variables, the default research model can be

formulated as follows:

Figure (1) the hypothetical form of research

The Second Topic
Theoretical Aspect
First: Big data, data mining and machine

learning

1.The concept of big data
The term big data was first proposed by Gartner

in 2008, and although it was noticeable at the
time, the influence of this term dates back to

2001 when the Meta Group first discussed it.
This term expresses a significant increase in the

volume of data in terms of the number, speed,
and variety of their production. As a result, the

search for new solutions to manage this vast

volume has become a necessity in the areas of
storage and analysis to make the most of this

data (quadratic and Dahmon, 2017:25). It can

be noted that the topic of big data has received
significant attention recently by researchers.

There are many definitions provided by
researchers for this term, considering (Bahga &

Madisetti, 2019, p. 25) that big data is a set of
data whose size, speed, or diversity is so

enormous that its storage, management,
processing, and analysis is a challenge using

traditional database methods and data

processing tools. (Al-Susi 13:2020) indicates
that the data collected from various sources and

forms

in

business

environments

is

characterized by huge quantities, production

speed, diversity in forms and sources, and
continuous development. (Ghattas, 12:2020)

big data is also seen as a complex and extensive
set of data collected and stored across multiple

Machine

learning

technology

The dependent variable

Financial decisions

Big data mining

Effectt

Independent variables

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

23

https://www.theamericanjournals.com/index.php/tajmei

internet platforms and analyzed using various
technologies.
2. Classification of big data
The data are divided into three main

categories,these classifications are explained

below by Arnaud, et al.,2020:4838)).
A-structured or structured data:
Structured data comes in the form of tables or

databases, the style of which is controlled in
advance by the database schema(Yunus,

13:2020).
B-unstructured or unstructured data:
Unstructured data is electronic data that is

difficult to classify easily, forming a significant
part of a large data set. This includes content

written in social media, videos, photos, blogs
and emails. This data created by humans is a

rich source of information and its growth is
unprecedented (meqnani and shabila, 3:2019).
C-semi-structured or semi-structured data:
Semi-structured data is a combination of the

two and refers to data that is close to structured

data, but not arranged in tables or databases.
These data usually appear as text on web pages

or in the minutes of the meeting (Abdullah Al-
Hani, 27:2018).
3.Data mining and machine learning technology
The wide spread of Information Technology, the

ease of access, and the excellent availability

have led to a massive increase in the volume of
data available and stored in databases. As the

proliferation of big data repositories has
become more widespread, many researchers

have begun to explore how to make the most of

this vast amount of data. They sought to develop
techniques, methods, and means of extracting

information and knowledge from this big data
for problem-solving and decision-making

(Abdul Ghaffar, 390:2020), before the advent of
Big Data Processing Technologies, companies

needed help to collect and store such vast
amounts of data. Even with the invention of

processing tools, some can only achieve
comprehensive results. However, they have

slowly shown excellent performance in multiple
areas, such as business model creation and

decision-making. Achieving a balance between
reducing hardware costs, optimizing processing

costs, and achieving added value are the main
goals of these technologies (Rawat& Yadav,

2021, p. 3).
A-data mining techniques (Data Mining) :
The method of data mining and knowledge

exploration, known as Data Mining, has
emerged as a technique aimed at extracting

knowledge from huge amounts of data. This
technique has the ability to answer a variety of

questions, ranging from "what happened?"

Right down to "what's going on?""In the
present, and even "what could happen in the

future?"",

Which

contributes

to

the

interpretation of events and trends based on

historical and current data (Abdul Ghaffar,
391:2020). The technique aims to analyze huge

amounts of data to discover previously
unknown patterns and relationships, and build

models to predict future behavior. This process
seeks to transform data from just accumulated

information into valuable knowledge that can
be exploited to make sustainable decisions. Data

mining seems to have found widespread
acceptance in large companies, as they realized

its value in enhancing competitiveness and

improving

performance(

Asaad

&

Abdulhakim:2021:18).
He points out (Dahiya et al.,2021: 5) data mining

usually refers to the methods necessary to
extract implicit and unknown knowledge, as it is

a form of discovering the knowledge necessary
to solve a variety of problems in a certain scope.

Also known as the process of mass analysis of
huge amounts of data, this data is carefully

examined to discover valuable information that

can be used to improve decision-making
processes. Thus, data mining is a computer-

based information system that examines huge
amounts of data to generate information and

discover deeper knowledge, enabling the
discovery of new connections between the

different components of big data. He defined
(Ali, 164: 2023) data mining is an advanced tool

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

24

https://www.theamericanjournals.com/index.php/tajmei

for data analysis, it also allows the application of
new ideas in the organization of data in a

practical way. According to (Papík &
Papíková,2022:3), data mining is the

exploration of large amounts of data, sorting
these data to extract information and identify

previously unknown relationships. Models are
built to predict behavior, which contributes to

obtaining valuable information that enhances

and improves the decision-making process.
B-machine learning technology (Machine

learning) :
Machine learning is tempting for the business

world in this modern era, as its comprehensive

advantages and diverse applications to business
data offer superior possibilities. Such facilities

allow companies to cope competently with
dynamic challenges in various industrial

sectors. Machine learning has proven its
effectiveness in performing complex business

tasks with high accuracy, compared to humans,
who can need help with large amounts of data

and draw accurate conclusions. In addition,
integrating

multiple

processing

units

contributes to achieving high processing speed
and reducing human bias factors (Canhoto &

Clear, 2020, p. 184).
The accounting profession has experienced a

profound transformation with the widespread
adoption of machine learning in various areas,

including business risk assessment, transaction
analysis, and commercial activities. This

technology has piqued the interest of large
companies and academics alike. Researchers

primarily leverage machine learning to forecast
accounting estimates, identify financial errors,

predict bankruptcy, and detect fraud. It also
fosters the use of inductive reasoning methods

in accounting (Atanasovsky et al., 2020:3). One

of the key ways machine learning revolutionizes
financial accounting is by mitigating common

human errors. Many routine data entry
practices, billing management, and low-level

bookkeeping tasks have been automated with
machine learning technologies. This has

significantly reduced the risk of accounting
information being entered incorrectly and

lightened the practical load on accountants.
While some researchers have expressed

concerns about the decline in job opportunities

in accounting and finance, many feel confident

that this shift will free up the time of finance
specialists, allowing them to focus on value-

added tasks within the company. Machine
learning adds tremendous value in the financial

sectors, as professionals now have more time to
focus on business strategies and improve the

efficiency and effectiveness of existing business
processes (Elmes et al., 2020:4). Likewise, the

established technological basis today offers
enormous opportunities, making very large

accounting operations easily achievable, since
most of the tasks that usually require significant

manual labor can be easily and automatically
automated, or at least using minimal human

effort, through software .Moreover, financial

accounting software is currently heavily
integrated

with

artificial

intelligence

technologies. Any program that does not have
machine learning is considered incomplete.

Therefore,many accounting tasks such as cost
calculations, receivables management, accounts

payable processing, tax calculations and risk
estimation can be easily automated using

machine learning technologies (Fallatah,
2021:2). The demand for accurate financial

forecasts and accounting estimates has
skyrocketed in recent years. In parallel with this

growth, a large number of transactions are
regularly conducted in enterprises, machine

learning offers the ultimate solution to ensure

smooth

and

accurate

information

processing.this

advanced

technological

advancement in the field of artificial intelligence
has improved conditions in the financial,

banking landscape and the field of account
analysis. Other key benefits of machine learning

include asset valuation and management,
forecasting of stock market behavior,

calculation of related risks and cost reduction
(Aziz & Dowling, 2019:35).

Second: strategic financial decisions

1-The concept of financial decisions
Financial decisions are one of the most

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

25

https://www.theamericanjournals.com/index.php/tajmei

important decisions that the company relies on
in its various activities, as they are aimed at

maximizing the market value of the company.
These decisions include the financing decision,

the investment decision, the dividend decision.
A financial decision is a decision that balances

obtaining funds and owning assets, as financial
decisions are aimed at financing investments

with the highest profit and thereby maximizing

the market value of the company (Nuri,
11:2019). (Halimi, 39:2020) defines financial

decisions as decisions that relate to the financial
aspects of the company, such as choosing to

reinvest excess liquidity in exchange for profit
distribution, choosing between self-financing

and external financing. While(Manisha,2020:1)
indicates that financial decisions are those

decisions that relate to the financial aspects of
the company, such as allocating funds,

managing the financial affairs of the business,
determining the size of investments necessary

to achieve its ultimate goals. These decisions
also include choosing the type of assets that the

company will receive, determining the mode of

financing, determining how the company's
income will be distributed. (Hammo and

Hassan, 144:2021) also show that making
financial decisions means choosing the

appropriate alternative or the best solution
from a set of options available in a specific

period of time. This decision is characterized by
the fact that it corresponds to the specific

problem to be solved, and contributes to the
achievement of the goals set by the company or

the financial decision-maker.
2-Objectives of financial decisions
The success of financial decisions is one of the

main indicators of the company's success,
thereby achieving its main goal of maximizing

its value, by providing the necessary financial
information, forecasting future financial needs,

evaluating sources of financing, monitoring
funds. Financial decision-making is an essential

part of the successful investment of available

financial resources (Nouri, 3:2019).It is through
making these financial decisions that the

financial management department seeks to
(Aziz and logani, 9:2014):

Making a profit and maximizing the market

value of shares: it is one of the main goals of the

owners, and this is related to making the right
financial decisions. When a financial manager

makes sound decisions, this can lead to an
increase in the market value of shares and bring

capital gains to the owners. Conversely, if the
decisions are incorrect, the value of the shares

may decrease, which negatively affects the

owners.Wealth maximization: it aims to
increase the present value of specific

investments or financial actions, not focusing
only on making profits themselves. This goal

depends on the timing of earnings and also the
risk factor. In general, wealth maximization is

an ideal strategic goal, focused on achieving the
current value of angel investments by

approving investment proposals that increase
the market value of securities. In addition,

owners pay special attention to the regular cash
distributions they receive, regardless of their

size, because they form an important part of
their financial guidance.
3-Types of strategic financial decisions
Modern financial management of strategic

financial decisions is divided into three main

categories (Reza et al., 242:2017): financing
decision, investment decision and dividend

decision.
A-The concept of investment decision
Investment decisions are crucial decisions that

companies pay great attention to achieving
their goals and expanding their business.

Among these critical investment decisions are

those related to financial investments, through
which companies seek to achieve the maximum

possible return and increase the value of their
share in the market (Abbas and Hadi, 2020,P.3).

There is a great diversity of opinions about the
concept of investment decisions, and among

these opinions are both (Saini and Shahan,
2019, p. 12), who show that the investment

decision consists of choosing the investment
alternative that is expected to achieve the

highest financial return compared to other
alternatives, based on a comprehensive analysis

of the expected returns and risks associated

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

26

https://www.theamericanjournals.com/index.php/tajmei

with these investments. This decision requires
conducting comprehensive feasibility studies of

potential alternatives, including financial
estimates and various analyses, to assess the

feasibility of alternatives to achieve the invested
goals. It is then that an informed decision is

made that corresponds to the overall strategy
and goals of the investor, and the readiness of

the chosen variant for implementation is

determined by the established methodological
framework based on the characteristics of the

project and its unique needs. , As he sees

(Islamoğlu et al., 2015, p. 531) that investment

decisions represent current contributions that
are added to the invested capital to own assets

that constitute a source of return in the future.
(Sharif,95:2022) defines an investment decision

as the decision that concerns the decision-
makers in using the funds to achieve the

maximum possible benefit in exchange for the
risks to which they may be exposed. He explains

(Bomjan, 2021: 50) that the nature of an
investment decision is unique since it is made

only once, and its impact extends for a long time,

making it an essential part of strategic decisions
that affect the company's future course. The

investment decision is surrounded by many
challenges and problems, such as uncertainty

due to currency fluctuations and difficulty
quantifying some variables.
B-The concept of financing
The term finance goes beyond the concept of

money in general, as it includes the activities

carried out by companies and individuals in the
economy. No individual, company or even a

state can work or continue to live without the
necessary funds to cover its activities.

Companies in particular are in dire need of
financing to meet their financial needs and

finance their operations and investments. The
financing decision is an important management

decision that affects the return and risks to
which the company's shareholders are exposed.

Therefore, it is essential for companies to plan

their financial structure when they need funds
to finance their investments and meet their

financial needs (al-Mayah, 2019: 21-20). In
addition, the financing decision is of great

strategic importance in achieving the well-being
of shareholders and ensuring the continuity of

the company. This is done by providing the
necessary funds to cover various investments

and identifying appropriate sources of
financing. It is necessary for the management to

study and analyze the company's financing
needs before making any decisions related to

financing. It must be determined whether the

financing needs can be met through the
company's own capital or through borrowing

from external sources (Thalib et al.,2019: 87),
corporate finance processes play a prominent

role in corporate management and financial
decision-making, as they are considered one of

the main factors influencing financial and
managerial decisions (Sharbati et al.,2014:24),

according to (Zutter &Gitman, 2012: 4) finance
is defined as the "science and art of money

management", as finance is defined as the study
of how individuals, institutions, governments

and companies obtain funds and other financial
assets, as well as how they are spent and

managed (Melicher & Norton, 2013:4), and

according to (Friday, 2016: 24) finance is one of
the areas of knowledge that includes a set of

facts, scientific foundations and theories that
deal with how money is obtained from its

various sources and used effectively by
individuals, entrepreneurs, companies and

governments. As defined by both (Al-Salami and
Al-Sharifi, 2022: 159) defines finance as the set

of decisions related to how to obtain the
necessary funds to finance the company's

investments and determine the optimal
financing mix from borrowed sources of

financing and funds owned in the company. To
cover the company's investments. He also

referred (Galane, 2019: 17) to financing as the

process of raising the necessary capital for the
company in order to finance operational or

investment costs”.

C-The concept of profit distribution decision
Dividend distribution decisions are one of the

most prominent strategic financial decisions
taken by financial managers in companies, as

they receive special attention due to their
importance in balancing the interests of

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

27

https://www.theamericanjournals.com/index.php/tajmei

shareholders and ensuring the sustainability of
the company's growth (Walid and Shaaban,

59:2023). the dividend distribution policy is
based on making a decision comparing two

main options: either distributing profits to
shareholders

or

retaining

them

for

reinvestment within the company. There are
several definitions of the decision to distribute

dividends, where profit in this context is

considered to be the return achieved by the
company during a specific period of time

(buhafs, 58:2021), and both (Dabbash and
Mahmoud, 71:2015) believe that the decision to

distribute dividends is the decision to divide the
profits between distributing them to

shareholders and reinvesting part of them in the
company. This decision is influenced by

previous investment and financing decisions.
The more effective these decisions are, the

greater the company's chances of making
continuous profits. He defined (Vodwal & Negi,

2023:9) dividend distribution decisions as the
steps taken by the company to dispose of the

profits achieved, whether by retaining and

reinvesting them or by distributing them to
shareholders through various forms of

distribution such as cash distribution or
offering new shares, among others. This

decision provides for the payment of additional
financial amounts and their transfer from the

company's activities to shareholders, with the
need to provide the necessary liquidity to fulfill

its financial obligations.
Dividend decisions also refer to the policy

established by the company, which corresponds
to its current nature and decisions, regarding

the distribution of dividends to shareholders in
the form of cash or shares, or withholding part

of the profits to be used in its future decisions
related to expansion, growth and investment.

The decision to distribute dividends is the
prerogative of the company's Board of directors

( hafsi, 2016, 40 ). And from the point of view of
(Haj, 36:2023), the distribution decision is the

decision made by the company as to whether

the profits should be distributed to
shareholders or kept for reinvestment. Such

decisions usually indicate a specific percentage
of the realized profit that should be distributed,

based on which the percentage that should be
reserved for future investment is determined.

The Third Topic
The Practical Side

In the practical aspect of research, a survey form

is designed to test research hypotheses. This

form consists of three main axes:
The first axis includes six questions to measure

data mining technology.
The second axis includes six questions to

measure machine learning technology
The third axis includes three dimensions, and

aims to evaluate strategic financial decisions

collectively, as each of them contains six
questions.
A seven-degree scale was used to express the

sentences of the mentioned axes and
dimensions. The measurements ranged from

one point indicating "notcompletely agree", to
seven points indicating "completely agree", as

shown in the following table:

Table (1) the grades used in the heptagonal scale and their default mean

Response

I totally

agree

ا

agree

Agree to a

somewhat

neutral

I don't agree to

some extent

ا

I don't

agree

I don't quite

agree

ةجردلا

7

6

5

4

3

2

1

Default scale mean = (sum of values for all responses) / (number of scale categories)

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

28

https://www.theamericanjournals.com/index.php/tajmei

As for the heptatonic Likert scale, it consists of seven categories (from 1 to 7).

The default mean of the scale is calculated as follows:(7 + 6 + 5 + 4 + 3 + 2 + 1) / 7 = 4 degrees

Source. By researcher

178 questionnaire forms were distributed and 156 of them were collected from the respondents. The

description of the individuals surveyed follows.
The following are the results of the descriptive statistics (of the responses obtained):

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

29

https://www.theamericanjournals.com/index.php/tajmei

Source: from researcher based on the Excel program

* The data of Table (2) show that the weighted arithmetic mean of this axis is 5.382, which is higher than

the assumed average of the 4-degree scale. The standard deviation also amounted to 1.262, and the

coefficient of difference was 0.275, which indicates a significant convergence of the opinions of the
questionnaire sample on the paragraphs of this dimension.
In general, it can be said that respondents believe that data mining technology contributes significantly

to improving the accuracy of financial forecasts, risk assessment and making strategic financial decisions,
with a difference in the extent of agreement on some items.
The standard deviation is the highest value used among dispersion measures to measure the extent of

statistical variation. The standard deviation reflects how widespread the values are within the data set,

since the dispersion decreases the smaller the standard deviation from the arithmetic mean. This is
usually understood as a consensus of views among the respondents in the questionnaire.

Table (4) respondents ' response to the investment dimension

The coefficient of variation is the ratio of the

standard deviation to the mean. The level of

dispersion around the mean decreases as the

coefficient of variation decreases. This reflects
the degree of variation in individual answers

relative to the average responses of the

respondents.
The coefficient of difference in the order of

paragraphs was used because it reflects the
importance of each paragraph. The lower the

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

30

https://www.theamericanjournals.com/index.php/tajmei

coefficient of difference, the more it indicates
the convergence of the opinions of the

respondents in the questionnaire, and therefore
the paragraph is assumed to be of greater

importance.
* It is clear from the data in Table (9) that this

dimension has an arithmetic mean of 5.229,
which is higher than the default average of 4

degrees. The standard deviation was 1.304, and
the coefficient of difference was 0.249, which

indicates a significant convergence of the
opinions of the questionnaire sample about this

dimension.

* The third axis - the second dimension: - financing

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

31

https://www.theamericanjournals.com/index.php/tajmei

Table (6) respondents ' response to the distribution of profits

Source: from the researcher based on the Excel program

Table (6) shows that this dimension has a

weighted arithmetic mean of 5.160, which is

higher than the default mean of 4 degrees, with
a standard deviation of 1.292 and a coefficient

of variation of 0.250, which indicates a
significant convergence of the opinions of the

respondents.

Testing

research

hypotheses

and

interpreting results
Encoding of variable paragraphs

To facilitate the statistical analysis of the data,

the variable paragraphs and their dimensions

were simplified by symbols , which are as
follows in the table below

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

32

https://www.theamericanjournals.com/index.php/tajmei

Variable name

dimensioning

code

Data mining

A6

A5

A4

A3

A2

A1

Machine learning

B6

B5

B4

B3

B2

B1

Strategic financial
decisions

Investment

E6

E5

E4

E3

E2

E1

Funding

F6

F5

F4

F3

F2

F1

Distribution of profits

G6

G5

G4

G3

G2

G1

Results of the research hypothesis test

In this part, the researcher will analyze the

research hypotheses that address:
The first hypothesis: data mining technology as

one of the big data analysis techniques

positively affects financial decisions.
The second hypothesis : machine learning

technology as one of the big data analysis
technologies

positively

affects

financial

decisions .The first hypothesis " data mining

technology as one of the data mining and
machine learning technologies positively affects

strategic financial decisions "
The path shown in the figure below is illustrated

for the purpose of hypothesis testing:

Figure (2) the course and results of the first hypothesis test

Route

Original sample

(Bata)

Standard deviation

(STDEV)

T statistics

P values

Data mining - > strategic financial decisions

0.641

0.068

9.449

0.000

Source: from the researcher's preparation based on the Smart-Pls program

The above table shows the following:
- In the Social Sciences, the lowest acceptable

error rate is 0.05, and it can be seen from table
(3-21) above that the p-Value was 0.000, which

is much less than the accepted error value
- The track coefficient of 0.641 indicates that

there is a strong positive relationship between
data mining technology and strategic financial

decisions.

- The value of T 9.449 indicates that the path

coefficient differs from zero significantly.
- Accordingly, the first sub-hypothesis of the

research is accepted.
The table below shows the values of both R-

square and F-square:- R-square: shows the

amount of interpretation of the model. And F-
square: shows the extent of the effect for the

independent variable.

Table (9) the coefficients of interpretation and influence of the first sub-hypothesis

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

33

https://www.theamericanjournals.com/index.php/tajmei

Route

R-square

F-square

Data mining - > strategic financial decisions

0.411

0.698

Source: prepared by the researcher based on the Smart-Pls program

From the intersection of the R-square value and

the F-square value in the previous table (18),
with the explanations associated with these

values, the following is noted:
- It shows that the data mining technique

explains 41.1% of the variation in strategic
financial decisions, and this explanation is

considered average, since the coefficient of
interpretation R-square ranges between 0.19

and 0.67.
- It also shows that data mining technology

affects

69.8%

of

strategic

financial

decisions,and this impact is considered

significant, as the value of F-square exceeded
the barrier of 0.35.
This finding is consistent with the findings of

Changpetch& Reid,2021, that data mining

contributes significantly to supporting the
decision-making process and promotes the

activation of connectivity between different
departments in the company, as well as allows

them to optimize the use of data resources. In
addition, data mining promotes effective

planning through the improvement and
development

of

established

accounting

information systems. It also contributes to
understanding the company's ability to grow

and follow developments in the market.
The second hypothesis " machine learning

technology as a data mining and machine
learning technology positively influences

strategic financial decisions ."
The path shown in the figure below is designed

to test the hypothesis:

Route

Original

sample

(Bata)

Standard

deviation

(STDEV)

T statistics

P values

Machine learning - > strategic financial decisions

0.529

0.080

6.589

0.000

Source: from the numbers of the researcher based on the Smart-Pls program

The above Table shows the following in the

Social Sciences: the minimum acceptable error
ratio is 0.05,. It is shown from Table (19) above

that the p - Value was 0.000, which is much
lower than the acceptable error value path

coefficient of 0.529, indicates a positive,
moderate to strong relationship between

machine learning technology and strategic
financial decisions.- A standard deviation of

0.080 indicates that the estimate is relatively
accurate.- The value of T 6.589 indicates that the

path coefficient differs from zero significantly.
Thus, the second sub-research hypothesis is

accepted.The table below shows the R-square
and F-square values: the R-square value shows

the amount of interpretation of the model. And
the F-square value shows the amount of

influence of the independent variable.

Table(11) coefficients of interpretation and effect of the second hypothesis

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

34

https://www.theamericanjournals.com/index.php/tajmei

Route

R-square

F-square

Machine learning - > strategic financial decisions

0.28

0.389

Source: from the numbers of the researcher based on the Smart-Pls program

Our research findings,

as evidenced by the

significant values of R-square and F-square in
the table above (20), underscore the profound

impact of machine learning. We discovered that
machine learning accounts for 28% of the

variation in strategic financial decisions, a
substantial influence given the R-square

interpretation coefficient's range of 0.19-0.67.

Furthermore, machine learning's effect on audit
quality is a staggering 38.1%, a finding of

utmost importance as the F-square value
exceeded 0.35.
This result is consistent with the findings of

Aziz & Dowling, 2019 that machine learning
technology machine learning provides the

ultimate solution to ensure smooth and
accurate information processing. This advanced

technological

advancement

in

artificial

intelligence has improved financial, banking,
and account analysis conditions. Other key

benefits of machine learning include asset
valuation and management, predicting stock

market behavior, calculating related risks, and
reducing costs, which lead to strategic financial

decisions.

CONCLUSIONS

1-The use of data mining and machine learning

technology in accounting is a new stage that
enhances the development of this field, as it

contributes to reducing the effort expended and
errors in financial reporting.
2-The advantages of data mining and machine

learning technology, such as efficiency,

accuracy, and speed, enhance accountants'
capabilities and develop their skills, improving

their professional performance.
3-There is no need to worry about the

replacement of accountants with data mining

and machine learning technology. Companies
will still rely on accountants who are proficient

in data analysis and interpretation, and can

provide valuable consulting. This emphasizes
the security of their job roles and the

importance

of

staying

updated

with

technological advancements.
4-Data mining and machine learning technology

allow companies to conduct transparent,
secure, and analyzable digital transactions. This

facilitates the preparation and submission of

financial reports to decision makers and
reduces the need for traditional analysis.
5-The use of data mining and machine learning

technology in accounting in Iraq faces many
challenges, including accountants' lack of

experience and adequate training to use these
technologies effectively.
6-Data mining technology positively influences

strategic financial decisions by carefully

examining operations, improving efficiency, and
understanding the various dimensions of

operations, which contributes to improving the
company's overall performance.
7-Machine learning technology, with its ability

to analyze data quickly and accurately,

significantly

impacts

strategic

financial

decisions. This not only helps to detect

problems, save time, and reduce costs but also
enables the prediction of risks and optimization

of the planning and decision-making process.
This optimistic view of the future of accounting

can inspire the audience to embrace technology.

RECOMMENDATIONS

1-Companies should adopt the concept of big

data and integrate it into their philosophy and
strategy in the short and long term, as these

data contribute to achieving integration
between them and the Accounting Information

System and are considered a successful
alternative in light of continuous technological

development.
2-Banks, in particular, stand to gain significant

value from big data. They can increase its value

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

35

https://www.theamericanjournals.com/index.php/tajmei

by effectively processing and generating
information from it. This necessitates the

development of efficient mechanisms to process
the data, ensuring that the benefits far outweigh

the costs of collection and analysis.
3-Work should be done to integrate various

data sources into the accounting information
system so that text, voice, and image data are

gradually linked with traditional data. This will
allow the system to deal with large amounts of

data and control them effectively.
4-It is imperative for companies to invest

serious efforts in understanding the nature and

characteristics of big data. By mastering the

techniques of collecting and analyzing big data,
they can obtain accurate, fast, relevant, efficient,

effective, flexible and reliable information,
enhancing their capabilities for making

strategic financial decisions.
5-Seminars and workshops should be organized

in universities and specialized centers to

discuss the topic of big data and how to benefit
from it in the development of the Accounting

Information System, which reflects positively

on the performance of companies in general and
provides new insights for analysis and

analysis

—

processing big data in various sectors

and linking it to other variables such as

electronic disclosure or cloud accounting. Or
artificial intelligence.

REFERENCES
Arabic sources:
1.

Yunus, Ehab. (2020). "The Big Data

Revolution and the mobilization of tax

revenues is the case of Egypt", Journal of the
Faculty of Economics and political science

(21(1) 7-36

2.

Maqnani, Sabrina; and shabeela, presenter.

(2019). "The role of big data in supporting
sustainable development in the Arab

countries", Journal of information and
Technology Studies, 1(14), 2-14

3.

Bassiouni, Haitham Mohammed Abdel

Fattah, and Ashour, Ihab Mohammed Kamel.

(2021). "The interactive impact of big data

and the characteristics of the Audit
Committee and its reflection on the

disclosure of future information: Applied
evidence from companies listed on the

Egyptian Stock Exchange". Journal of
financial and commercial research, P2 , 569-

614.

4.

Karaty, Hana; and dahmoun, Osama. (2017).

"Huge employment in tech companies and
the privacy of user data". (Unpublished

master's thesis). University of May 8, 1945,
qalba, Algerian Republic.

5.

Susi, India .(2020). "The extent of readiness

of the health sector in the Gaza Strip to apply

big data and link it to business intelligence",
master thesis, Faculty of Economics and

Administrative Sciences, Islamic University
- Gaza, Palestine.

6.

Diver, believer. (2020). "The impact of the

optimal use of big data in enhancing
competitive advantage (digital marketing as

an intermediate variable is an applied study
on the Palestinian telecom group in Tel",

master thesis, Faculty of Economics and

Administrative Sciences, Islamic University
- Gaza, Palestine.

7.

Hajjaj, Ismail Mohamed Ahmed. (2022).

"The impact of big data analysis using the
Accounting

Information

System

on

improving the quality of financial
reporting". Scientific journal of commercial

research (Menoufia University), 46(3), 67-
108. doi: 10.21608/sjsc.2022.110292.1080

8.

Al-Rubaie, Kholoud Hadi Abboud and

Ahmed, Iyad Abbas Rashid. (2020).

"Indicators of the effectiveness of the
strategic decision and its impact on the

quality of banking service : an applied
research in a sample of Iraqi banks". Journal

of the Baghdad College of Economic
Sciences University, Vol. 2020, P. 60, PP.

257-312.

9.

Wehdan, Mohamed Ali, & Abdelhadi, Iman

Fathi. (2019). "The role of Big Data in
activating the requirements of financial

inclusion (analytical study)". Trade and

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

36

https://www.theamericanjournals.com/index.php/tajmei

Finance,

39

(2),

245-276.

doi:

10.21608/caf.2019.256255

10.

Yusuf, Jamal Ali Muhammad. (2018). "A

proposed

input

for

assessing

the

importance of accounting development
under the big data environment". The

accounting thought, 22(4), 1225-1272. doi:
10.21608/atasu.2018.39108

11.

Abdul Ghaffar, Nurhan Mr. Mohammed.

(2020). "Using the data mining method to
support the information content of future

financial statements and the impact of this
on enhancing the efficiency of investment

decisions in the Egyptian Financial Market

(applied study)". Journal of financial and
Business Research, 21 (first issue), 376-465.

doi: 10.21608/jsst.2020.26049.1018

12.

Ali,

Heba

Jamal

Hashim.

(2023).

"Implications of the use of data mining

techniques in predicting the opinion of the
external auditor and their impact on the

fairness of financial statements: an applied
study". Scientific journal of financial and

commercial studies and research, 4 (2),

155-204. doi: 10.21608/cfdj.2023.289980

13.

Al-Jubouri, Murad Musa Abd. (2020). "The

impact of Information System quality

characteristics on the effectiveness of
strategic decision : a case study at the

Hawija Technical Institute". Journal of the
University of Kirkuk for administrative and

Economic Sciences, vol. 10, p. 2, pp. 58-80.

14.

Haj Sharif. (2023). "Profit distribution

decisions and their impact on the market
value of the enterprise

–

a case study of the

Saidal complex during the period 2019-
1999", PhD thesis, Abdul Ghani Hariri).

15.

Dabash a., & Mahmoud J. (2015). "The

impact of the cash flow statement on

financial decision - making-the case study of
commercial banks in the state of Jijel ".

Journal of economic and financial research,
2(2), 65-83.

16.

Buhafs, Samiha. (2018). The impact of

accounting information on financial
decision - making in the Algerian economic

institution (PhD thesis, Mohamed Khidr
University, Biskra, accounting specialty).

17.

Yenenep, bin Beli Walid, Shaaban, Makhlouf,

& Sara (supervisor). (2023). "The impact of

the financial structure on the decision to
distribute profits in the economic

enterprise: a case study of the Algerian
electricity production enterprise-Jijel unit

(runs)", PhD thesis, Jijel University

18.

Al-Salami, Hossam Hussein Shia, and Al-

Sharifi, Karar Abbas Hussein, (2022) . "The

impact of the financing structure under the
capture theory on the company's value : an

analytical study of a sample of industrial

companies listed on the Iraq stock exchange
for the period (2004 - 2019) ". Iraqi Journal

of Administrative Sciences, volume (18),
issue (73).

19.

Hishan, Saleh Juma Fadel, and Falconer,

Mohammed Khalil. (2022). "The impact of
the application of the data mining method

on achieving the quality of financial reports
from the point of view of Iraqi external

auditors". Tikrit Journal of administrative

and Economic Sciences, vol. 18, P. 59 , 84-99.

20.

Al-Miah , Shaima Shaker Mahmoud , (2019).

"Measuring financing requirements and

their role in improving the market value of
the bank : an applied study in a sample of

banks listed on the Iraqi securities market
for the period 2005-2017", master thesis,

Faculty of management and economics,
Karbala University, Iraq

21.

Bomjan, mercy .(2021)."The role of the

outputs of the financial accounting system

in making economic decisions case study of
Biopharm Pharmaceuticals for the period

2018-2020". Master thesis / Faculty of
economic, commercial and Management

Sciences/ Arab bin Mehidi University /
Umm El Bouaghi / Algeria .

22.

M. Nasser Talib Sharif. (2022). "Accounting

analysis using financial ratios and its

importance in supporting investment
decisions is an analytical study of a sample

of Iraqi industrial companies". Journal of

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

37

https://www.theamericanjournals.com/index.php/tajmei

business economics of Applied Research, 3
(3).

23.

My pursuit, tenderness and charge,

toxicity.(2019)."The impact of the cost of

capital on investment decision

–

making in

the economic enterprise-a case study of the

ibash foundation for public works in the
period from (2014-2016)". Master thesis /

Faculty of economic, commercial and
Management Sciences/ Ahmed Derayah

University / Adrar/ Algeria.

24.

Abbas, Yasser and Hadi Alaa. (

۲۰۲۰

).

"Financial analysis indicators and their
impact on making investment decisions for

the national insurance company Applied
Research", Journal of accounting and

Financial Studies, Iraq, Vol. 15, issue 53, P

105.

25.

Aziz, & logani. (2014). "The role of internal

accounting audit in improving the financial

decision-making process - a case study of
azaba metal packages company", PhD thesis,

Biskra University-Mohammed Khidr.

26.

Nuri khanem. (2019). "Data mining portal-

based financial information systems and
their impact on the success of financial

decisions". Field magazine (seventh issue.
(11) .

27.

Zahra Hammou, & Nour Hassan. (2021).

"The role of banking governance in
rationalizing financial decisions: a survey

study of the opinions of a sample of

employees at Rafidain bank, Mosul and
Bartella

branches".

Entrepreneurship

Journal of Finance and business, 140-155.

28.

My papilloma is noble. (2020). "Financial

information systems and their role in the

financial decision-making process", PhD
thesis University of gardaia . 39 :

Foreign sources:
1.

Bahga, A., & Madisetti, V. (2019). "Big Data

analytics: A hands-on approach".

2.

Arnaud, É., Elbattah, M., Gignon, M., &

Dequen, G. (2020, December). "Deep

learning to predict hospitalization at triage:

Integration of structured data and
unstructured text". In 2020

IEEE

International Conference on Big Data (Big
Data) (pp. 4836-4841). IEEE.

3.

Anderson, A. (2015). "Statistics for big data

for dummies". John Wiley & Sons.

4.

Abawajy, J. (2015). "Comprehensive

analysis of big data variety landscape".
International journal of parallel, emergent

and distributed systems, 30(1), 5-14.

5.

Gil, D.,

Johnsson, M., Mora, H., & Szymański,

J. (2019). "Review of the complexity of
managing big data of the internet of things".

Complexity, 2019.

6.

Lee, I. (2017). "Big data: Dimensions,

evolution, impacts, and challenges".

Business horizons, 60(3), 293-303.

7.

Barker, R., & Schulte, S. (2017).

"Representing the market perspective: Fair
value measurement for non-financial assets.

Accounting", Organizations and Society, 56,
55-67.

8.

John K.,&William R., (2015),"Consequences

of Big Data and Formalization on

Accounting and Auditing Standards",
Accounting Horizons, Volume 29, Issue 2,

June, PP.377-380.

9.

Rawat, R., & Yadav, R. (2021). "Big data: Big

data analysis, issues and challenges and

technologies". In IOP Conference Series:
Materials Science and Engineering (Vol.

1022, No. 1, p. 012014). IOP Publishing.

10.

Asaad, R. R., & Abdulhakim, R. M. (2021).

"The Concept of Data Mining and
Knowledge

Extraction

Techniques".

Qubahan Academic Journal, 1(2), 17-20.

11.

Papík, M., & Papíková, L. (2022). "Detecting

accounting fraud in companies reporting

under US GAAP through data mining".

International

Journal

of

Accounting

Information Systems, 45, 100559.

12.

Jakhar, D., & Kaur, I. (2020). "Artificial

intelligence, machine learning and deep
learning: definitions and differences".

THE USA JOURNALS

THE AMERICAN JOURNAL OF MANAGEMENT AND ECONOMICS INNOVATIONS (ISSN- 2693-0811)

VOLUME 06 ISSUE07

38

https://www.theamericanjournals.com/index.php/tajmei

Clinical and experimental dermatology,
45(1), 131-132.

13.

Helm, J.M., Swiergosz, A.M., Haeberle, H.S.,

Karnuta, J.M., Schaffer, J.L., Krebs, V.E.,... &

Ramkumar, P.N. (2020). "Machine learning
and artificial intelligence: definitions,

applications, and future directions". Current
Reviews in Musculoskeletal Medicine, 1-8.

14.

Aziz, S., & Dowling, M. (2019). "Machine

learning and AI for risk management (pp.
33-50)". Springer International Publishing.

15.

Changpetch, P., & Reid, M. (2021). "Data

mining techniques: Which one is your

favorite?". Journal of Education for
Business, 96(3), 143-148.

16.

Vodwal, S., & Negi, V. (2023). "Applications

of Machine Learning and Determinants of
Dividend Decision: Evidence from Indian

Firms". Indian Journal of Finance, 17(5), 8-

24.

17.

Galane , O. M., (2019) . " Factors That

Influence Debt Financing Of Municipal

Owned Entities : A case Study Of The City Of
Johannesburg " . Master Thesis , Accounting

Sciences , University Of South Africa .

18.

Melicher, R. W., & Norton, E. A. (2013).

"Introduction

to

finance:

markets,

investments, and financial management".

John Wiley & Sons.

19.

Gitman, L. J., & Zutter, C. J. (2012). "

Principles of Managerial Finance Thirteen

edition ". England: Pearson Education
Limited.

20.

Thalib, F., Herdiyana, H., & Wahid, A. (2019).

" Tax and not tax on capital structure of real

estate

and

property

company

".

International Journal of Economics and

Financial Issues, 9(2), 87.

21.

Islamoğlu, M., Apan, M., & Ayvalı, A. (2015).

" Determination of factors affecting

individual investor behaviours: A study on
bankers ". International Journal of

Economics and Financial Issues, 5(2), 531-
543.

22.

Manisha. (2020). "Financial Decisions:

Concept and Factors Influencing It,

Financial Decisions: Concept and Factors
Influencing

It".

Available:

https://www.yourarticlelibrary.com/fin
ancial-

management/financialdecisions/financial-
decisions-conceptand-factors-influencing-

it/71186

23.

Fallatah, R. (2021). "Literature review

–

machine learning in accounting and
assurance". Academy of Accounting and

Financial Studies Journal, 25(6), 1-14.

24.

Elmes, A., Alemohammad, H., Avery, R.,

Caylor, K., Eastman, J. R., Fishgold, L., ... &

Estes, L. (2020). "Accounting for training
data error in machine learning applied to

earth observations". Remote Sensing, 12(6),
1034.

25.

Atanasovski, Atanasko & Bozhinovska

Lazarevska, Zorica & Trpeska, Marina.

(2020)." A conceptual framework for
understanding

emerging

technologies

shaping the accounting and assurance
profession

of

the

future".60-72

.10.47063/EBTSF.2020.0005.

26.

Canhoto, A. I., & Clear, F. (2020). "Artificial

intelligence and machine learning as
business tools: A framework for diagnosing

value destruction potential". Business
Horizons, 63(2), 183-193.

27.

Dahiya, N., Tyagi, L., & Sharma, N. (2021).

"Data mining techniques for Big data:
Purpose,

Techniques

and

Practical

Implications". International Journal of

Recent Research Aspects, 8(3).