p–ISSN: 2723 – 6609 e-ISSN: 2745-5254
Vol. 5, No. 11, November 2024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5059
The Influence of E-procurement and Artificial Intelligence
on Fraud Prevention at PT Brinks Solution Indonesia
Leyla Kadrina
Trisakti University, Indonesia
Email: [email protected]
*Correspondence
ABSTRACT
Keywords: fraud, e-
procurement, artificial
intelligence, internal
control.
PT Brinks Solution Indonesia has implemented an e-
procurement system with artificial intelligence to struggle
with fraud in end-to-end processes starting procurement to
payment. The purpose of this research is to examine e-
procurement to reduce fraud. This research uses a
quantitative approach by distributing questionnaires to 100
employees, with different ages and functions. The main
finding suggests e-procurement system has a positive
relationship and is significant to fraud prevention and the
emerging expectation to eradicate fraud in the procurement
process. The research gives knowledge to the stakeholders
to use e-procurement in all procurement to payment
processes to prevent fraud.
Introduction
Information technology has changed the behavior of institutions in buying and
selling goods and services rapidly. In the digital era, PT Brinks Solution Indonesia has
adopted technology for many purposes including procurement and payment processes. E-
procurement is an online system that can simplify the procurement process
(Rotchanakitumnuai, 2013). The potential benefits of e-procurement are greater
transparency and increased accountability and reduce the risk of corruption (Neupane et
al., 2014). Pt Brinks Solution Indonesia has implemented the Hasmicro system, a national
e-procurement system to store and send goods and services electronically. The goal is to
increase effectiveness, transparency, and accountability, and the procurement process is
accessible to all stakeholders (Amalia et al., 2023).
Previous Process
Vol. 5, No. 11, November 2024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5059
The Influence of E-procurement and Artificial Intelligence
on Fraud Prevention at PT Brinks Solution Indonesia
Leyla Kadrina
Trisakti University, Indonesia
Email: [email protected]
*Correspondence
ABSTRACT
Keywords: fraud, e-
procurement, artificial
intelligence, internal
control.
PT Brinks Solution Indonesia has implemented an e-
procurement system with artificial intelligence to struggle
with fraud in end-to-end processes starting procurement to
payment. The purpose of this research is to examine e-
procurement to reduce fraud. This research uses a
quantitative approach by distributing questionnaires to 100
employees, with different ages and functions. The main
finding suggests e-procurement system has a positive
relationship and is significant to fraud prevention and the
emerging expectation to eradicate fraud in the procurement
process. The research gives knowledge to the stakeholders
to use e-procurement in all procurement to payment
processes to prevent fraud.
Introduction
Information technology has changed the behavior of institutions in buying and
selling goods and services rapidly. In the digital era, PT Brinks Solution Indonesia has
adopted technology for many purposes including procurement and payment processes. E-
procurement is an online system that can simplify the procurement process
(Rotchanakitumnuai, 2013). The potential benefits of e-procurement are greater
transparency and increased accountability and reduce the risk of corruption (Neupane et
al., 2014). Pt Brinks Solution Indonesia has implemented the Hasmicro system, a national
e-procurement system to store and send goods and services electronically. The goal is to
increase effectiveness, transparency, and accountability, and the procurement process is
accessible to all stakeholders (Amalia et al., 2023).
Previous Process
Leyla Kadrina
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5060
New Process
However, there are cases of corruption that occur in the procurement process. And
certainly cause losses to the Company. Research on e-procurement has not been carried
out at PT Brinks Solution Indonesia so the implementation of e-procurement is visible
but not accountable (Rae & Subramaniam, 2008). This provides an opportunity to commit
fraud such as corruption and bribery, because the e-procurement system is not
implemented effectively. Therefore, internal audits and internal controls are necessary to
complement fraud prevention. Internal audits are established to monitor organizational
activities and control irregular actions (Zakaria et al., 2016). Internal controls ensure that
the procurement process is carried out according to the Company's procedures. Therefore,
research is needed to measure the effectiveness of E-Procurement and Internal audit on
fraud prevention (Bertot et al., 2010).
The purpose of the research is to be able to help companies assess the performance
of the system that has been implemented and whether it has been effectively and
efficiently used in fraud prevention.
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5060
New Process
However, there are cases of corruption that occur in the procurement process. And
certainly cause losses to the Company. Research on e-procurement has not been carried
out at PT Brinks Solution Indonesia so the implementation of e-procurement is visible
but not accountable (Rae & Subramaniam, 2008). This provides an opportunity to commit
fraud such as corruption and bribery, because the e-procurement system is not
implemented effectively. Therefore, internal audits and internal controls are necessary to
complement fraud prevention. Internal audits are established to monitor organizational
activities and control irregular actions (Zakaria et al., 2016). Internal controls ensure that
the procurement process is carried out according to the Company's procedures. Therefore,
research is needed to measure the effectiveness of E-Procurement and Internal audit on
fraud prevention (Bertot et al., 2010).
The purpose of the research is to be able to help companies assess the performance
of the system that has been implemented and whether it has been effectively and
efficiently used in fraud prevention.
The Influence of E-procurement and Artificial Intelligence on Fraud Prevention at PT Brinks
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5061
Method
This study uses a quantitative approach to explore the potential of e-procurement
systems, internal controls, and artificial intelligence in reducing corruption in the
procurement process until the payer. A survey was used to collect data using
questionnaires distributed to participants. To select participants, a convenience sample
design is adopted. The participants were employees of PT Brinks Solution Indonesia who
were involved with different processes and functions. A total of 100 paper-based
questionnaires were distributed. The collected data was analyzed using smartPLS.
Measurements will be carried out on a scale of 1-6, where 1=strongly disagree and
6=strongly agree.
Results and Discussion
Validity Test
The validity test is carried out to determine how valid or appropriate a questionnaire
is in measuring the variables to be studied. This study conducted a validity test on 80
employees of PT Brinks Solution Indonesia by comparing the calculated r value and the
table r value. The r-value of the table was obtained based on the degree of freedom (df) =
n-k with alpha 0.05 (5%), where 80-2 or df = 78, and the r-value of the table was obtained
at 0.219.
Independent E-procurement Variable Validity Test
The results of the validity test of the E-procurement variable are presented in the
following table:
Table 1
Results of the Validity Test of the E-Procurement Variable
Variable Statement
Items
r
calculate r Table Information
E-procurement (X1)
X1.1 0.900 0.219 Valid
X1.2 0.894 0.219 Valid
X1.3 0.799 0.219 Valid
X1.4 0.875 0.219 Valid
X1.5 0.842 0.219 Valid
Table 1 above, shows that the value of r calculated on 5 statement items in the
independent variable E-procurement> from the r table is 0.219 and has a positive value,
so it is concluded that the statement item of the independent variable E-procurement is
declared valid.
Internal Control Independent Variable Validity Test
The results of the validity test of the internal control variables are presented in the
following table:
Table 2
Results of the Validity Test of Internal Control Variables
Variable Statement
Items
r
calculate r Table Information
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5061
Method
This study uses a quantitative approach to explore the potential of e-procurement
systems, internal controls, and artificial intelligence in reducing corruption in the
procurement process until the payer. A survey was used to collect data using
questionnaires distributed to participants. To select participants, a convenience sample
design is adopted. The participants were employees of PT Brinks Solution Indonesia who
were involved with different processes and functions. A total of 100 paper-based
questionnaires were distributed. The collected data was analyzed using smartPLS.
Measurements will be carried out on a scale of 1-6, where 1=strongly disagree and
6=strongly agree.
Results and Discussion
Validity Test
The validity test is carried out to determine how valid or appropriate a questionnaire
is in measuring the variables to be studied. This study conducted a validity test on 80
employees of PT Brinks Solution Indonesia by comparing the calculated r value and the
table r value. The r-value of the table was obtained based on the degree of freedom (df) =
n-k with alpha 0.05 (5%), where 80-2 or df = 78, and the r-value of the table was obtained
at 0.219.
Independent E-procurement Variable Validity Test
The results of the validity test of the E-procurement variable are presented in the
following table:
Table 1
Results of the Validity Test of the E-Procurement Variable
Variable Statement
Items
r
calculate r Table Information
E-procurement (X1)
X1.1 0.900 0.219 Valid
X1.2 0.894 0.219 Valid
X1.3 0.799 0.219 Valid
X1.4 0.875 0.219 Valid
X1.5 0.842 0.219 Valid
Table 1 above, shows that the value of r calculated on 5 statement items in the
independent variable E-procurement> from the r table is 0.219 and has a positive value,
so it is concluded that the statement item of the independent variable E-procurement is
declared valid.
Internal Control Independent Variable Validity Test
The results of the validity test of the internal control variables are presented in the
following table:
Table 2
Results of the Validity Test of Internal Control Variables
Variable Statement
Items
r
calculate r Table Information
Leyla Kadrina
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5062
Internal Control
(X2)
X2.1 0.701 0.219 Valid
X2.2 0.823 0.219 Valid
X2.3 0.862 0.219 Valid
X2.4 0.832 0.219 Valid
X2.5 0.891 0.219 Valid
X2.6 0.855 0.219 Valid
Based on Table 2, the value of r calculated on 6 statement items in the internal
control variable > from the r of the table is 0.219 and has a positive value, so it is
concluded that the statement items of the independent internal control variable are
declared valid.
Artificial Intelligent Independent Variable Validity Test
The results of the validity test of artificial intelligence variables are presented in the
following table:
Table 3
Results of Artificial Intelligence Variable Validity Test
Variable Statement
Items
r
calculate r Table Information
Artificial
Intelliegent
(X3)
X3.1 0.901 0.219 Valid
X3.2 0.901 0.219 Valid
X3.3 0.896 0.219 Valid
X3.4 0.906 0.219 Valid
X3.5 0.769 0.219 Valid
Based on Table 3, it is known that the value of r calculated on 5 statement items in
the artificial intelligence variable > from the r of the table is 0.219 and has a positive
value, so it is concluded that the statement item of the artificial intelligence independent
variable is declared valid.
Validity Test of Dependent Variables of Fraud Prevention
The validity test of the dependent variable fraud prevention is presented in the
following table.
Table 4
Results of the Validity Test of Fraud Prevention Variables
Variable Statement
Items
r
calculate r Table Information
Fraud prevention
(Y)
Y.1 0.654 0.219 Valid
Y.2 0.652 0.219 Valid
Y.3 0.671 0.219 Valid
Y.4 0.606 0.219 Valid
Y.5 0.657 0.219 Valid
Y.6 0.602 0.219 Valid
The results of the validity test in Table 4, show that the value of r calculation in 6
items of the statement of the fraud prevention variable has a value of r calculation > from
the r of the table of 0.219 and a positive value, so that it is concluded that the statement
item of the dependent variable fraud prevention is declared valid.
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5062
Internal Control
(X2)
X2.1 0.701 0.219 Valid
X2.2 0.823 0.219 Valid
X2.3 0.862 0.219 Valid
X2.4 0.832 0.219 Valid
X2.5 0.891 0.219 Valid
X2.6 0.855 0.219 Valid
Based on Table 2, the value of r calculated on 6 statement items in the internal
control variable > from the r of the table is 0.219 and has a positive value, so it is
concluded that the statement items of the independent internal control variable are
declared valid.
Artificial Intelligent Independent Variable Validity Test
The results of the validity test of artificial intelligence variables are presented in the
following table:
Table 3
Results of Artificial Intelligence Variable Validity Test
Variable Statement
Items
r
calculate r Table Information
Artificial
Intelliegent
(X3)
X3.1 0.901 0.219 Valid
X3.2 0.901 0.219 Valid
X3.3 0.896 0.219 Valid
X3.4 0.906 0.219 Valid
X3.5 0.769 0.219 Valid
Based on Table 3, it is known that the value of r calculated on 5 statement items in
the artificial intelligence variable > from the r of the table is 0.219 and has a positive
value, so it is concluded that the statement item of the artificial intelligence independent
variable is declared valid.
Validity Test of Dependent Variables of Fraud Prevention
The validity test of the dependent variable fraud prevention is presented in the
following table.
Table 4
Results of the Validity Test of Fraud Prevention Variables
Variable Statement
Items
r
calculate r Table Information
Fraud prevention
(Y)
Y.1 0.654 0.219 Valid
Y.2 0.652 0.219 Valid
Y.3 0.671 0.219 Valid
Y.4 0.606 0.219 Valid
Y.5 0.657 0.219 Valid
Y.6 0.602 0.219 Valid
The results of the validity test in Table 4, show that the value of r calculation in 6
items of the statement of the fraud prevention variable has a value of r calculation > from
the r of the table of 0.219 and a positive value, so that it is concluded that the statement
item of the dependent variable fraud prevention is declared valid.
The Influence of E-procurement and Artificial Intelligence on Fraud Prevention at PT Brinks
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5063
Reliability Test
The reliability test was carried out to measure the level of reliability or consistency
of respondents' answers to the questions in the questionnaire. The variable is said to be
reliable if it gives a Cronbach Alpha value > 0.60. The results of the reliability test are
presented in the following table:
Table 5
Instrument Reliability Test Results
Variable Cronbach
Alpha
Alpha
Value Information
E-procurement (X1) 0.906 0.60 Reliable
Internal control (X2) 0.908 0.60 Reliable
Artificial intelliegent (X3) 0.923 0.60 Reliable
Fraud prevention (Y) 0.669 0.60 Reliable
The results of the reliability test in Table 4.5 show that the Cronbach alpha value of
the independent variables E-procurement, internal control, and artificial intelligence as
well as the dependent variable fraud prevention has a Cronbach alpha value of > 0.60.
This means that the three independent variables and dependent variables used are declared
reliable.
Classical Assumption Test
Normality Test
According to (Ghozali, 2016), the normality test aims to test whether, in the
regression model, the perturbating or residual variable has a normal distribution. The
normality test of the study was carried out using graph analysis which was detected by
looking at the spread of data (points) on the diagonal joints of the graph or the histogram
of the residual (Manjah et al., 2019). The results of the normality test of the histogram
and P-plot chart analysis are presented in the following figure:
Figure 1 Histogram Chart
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5063
Reliability Test
The reliability test was carried out to measure the level of reliability or consistency
of respondents' answers to the questions in the questionnaire. The variable is said to be
reliable if it gives a Cronbach Alpha value > 0.60. The results of the reliability test are
presented in the following table:
Table 5
Instrument Reliability Test Results
Variable Cronbach
Alpha
Alpha
Value Information
E-procurement (X1) 0.906 0.60 Reliable
Internal control (X2) 0.908 0.60 Reliable
Artificial intelliegent (X3) 0.923 0.60 Reliable
Fraud prevention (Y) 0.669 0.60 Reliable
The results of the reliability test in Table 4.5 show that the Cronbach alpha value of
the independent variables E-procurement, internal control, and artificial intelligence as
well as the dependent variable fraud prevention has a Cronbach alpha value of > 0.60.
This means that the three independent variables and dependent variables used are declared
reliable.
Classical Assumption Test
Normality Test
According to (Ghozali, 2016), the normality test aims to test whether, in the
regression model, the perturbating or residual variable has a normal distribution. The
normality test of the study was carried out using graph analysis which was detected by
looking at the spread of data (points) on the diagonal joints of the graph or the histogram
of the residual (Manjah et al., 2019). The results of the normality test of the histogram
and P-plot chart analysis are presented in the following figure:
Figure 1 Histogram Chart
Leyla Kadrina
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5064
Figure 2 Normal Probability Plot
The results of Figure 1, show that the histogram graph gives a distribution pattern
that deviates to the right, meaning that the data is normally distributed, while Figure 2
shows a p-plot graph showing points following and spreading around the diagonal line
and following the direction of the diagonal line, so it is concluded that the regression
model meets the assumption of normalit (Joseph et al., 2015). In addition to the histogram
and p-plot graph tests, this study also conducted a Kolmogorov-Smirnov One-sample test
with the following test criteria:
a. The data is normally distributed, if the sig value > alpha level of 0.05 or
b. The data is not normally distributed, if the sig value < alpha level 0.05.
The Kolmogorov-Smirnov test One-sample test is presented in the following table:
Table 6
Results of the One-Sample Kolmogorov-Smirnov Test
The results of the normality test in Table 6, show the Asymp value. Sig. (2-tailed)
> 0.05, which is 0.200 > 0.05, so it is concluded that the residuals in the study have been
distributed normally (Tong et al., 2014).
Multiple Regression Analysis
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5064
Figure 2 Normal Probability Plot
The results of Figure 1, show that the histogram graph gives a distribution pattern
that deviates to the right, meaning that the data is normally distributed, while Figure 2
shows a p-plot graph showing points following and spreading around the diagonal line
and following the direction of the diagonal line, so it is concluded that the regression
model meets the assumption of normalit (Joseph et al., 2015). In addition to the histogram
and p-plot graph tests, this study also conducted a Kolmogorov-Smirnov One-sample test
with the following test criteria:
a. The data is normally distributed, if the sig value > alpha level of 0.05 or
b. The data is not normally distributed, if the sig value < alpha level 0.05.
The Kolmogorov-Smirnov test One-sample test is presented in the following table:
Table 6
Results of the One-Sample Kolmogorov-Smirnov Test
The results of the normality test in Table 6, show the Asymp value. Sig. (2-tailed)
> 0.05, which is 0.200 > 0.05, so it is concluded that the residuals in the study have been
distributed normally (Tong et al., 2014).
Multiple Regression Analysis
The Influence of E-procurement and Artificial Intelligence on Fraud Prevention at PT Brinks
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5065
Multiple regression analysis was carried out to determine the relationship between
independent variables E-procurement, internal control, and artificial intelligence to the
dependent variable of fraud prevention in 80 employees of PT Brinks Solution Indonesia.
Hasillregression in the study is presented in the following regression table 7:
Table 7
Multiple Regression Results
The regression results in Table 4.9 show the model of multiple regression equations
in the study as follows:
Y = a +b1X1 + b2X2 + b3X3 + e
Fraud perevention = 6.606 + 0.334 E procurement + 0.263 Internal control + 0.281
Artificial intelligence, The above equation means that:
1. A constant of 6,609 indicates that if the E-procurement, internal control, and artificial
intelligence variables are 0 (unchanged), then PT Brinks Solution Indonesia's fraud
prevention will have a value of 6,609.
2. The regression coefficient of the E-procurement variable (X1) of 0.334 shows a
positive direction. This means that E-procurement has a positive relationship with
event fraud, where every increase in E-procurement is 1 percent while other
independent variables are fixed, then PT Brinks Solution Indonesia's event fraud will
experience an increase of 33.4 percent.
3. The regression coefficient of the internal control variable (X2) of 0.263 indicates a
positive direction. This means that internal control has a positive relationship with
event fraud, where every increase in internal control is 1 percent while other
independent variables are fixed, then PT Brinks Solution Indonesia's event fraud will
experience an increase of 26.3 percent
4. The regression coefficient of the artificial intelligence variable (X3) of 0.281 indicates
a positive direction. This means that artificial intelligence has a positive relationship
with event fraud, where every increase in artificial intelligence is 1 percent while other
independent variables are fixed, then event fraud in PT Brinks Solution Indonesia
employees will increase by 28.1 percent.
Partial T Test
The t-test was carried out to find out whether E-procurement, internal control, and
artificial intelligence partially affected PT Brinks Solution Indonesia's preventing fraud.
Acceptance and rejection of hypotheses will be carried out with the following criteria:
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5065
Multiple regression analysis was carried out to determine the relationship between
independent variables E-procurement, internal control, and artificial intelligence to the
dependent variable of fraud prevention in 80 employees of PT Brinks Solution Indonesia.
Hasillregression in the study is presented in the following regression table 7:
Table 7
Multiple Regression Results
The regression results in Table 4.9 show the model of multiple regression equations
in the study as follows:
Y = a +b1X1 + b2X2 + b3X3 + e
Fraud perevention = 6.606 + 0.334 E procurement + 0.263 Internal control + 0.281
Artificial intelligence, The above equation means that:
1. A constant of 6,609 indicates that if the E-procurement, internal control, and artificial
intelligence variables are 0 (unchanged), then PT Brinks Solution Indonesia's fraud
prevention will have a value of 6,609.
2. The regression coefficient of the E-procurement variable (X1) of 0.334 shows a
positive direction. This means that E-procurement has a positive relationship with
event fraud, where every increase in E-procurement is 1 percent while other
independent variables are fixed, then PT Brinks Solution Indonesia's event fraud will
experience an increase of 33.4 percent.
3. The regression coefficient of the internal control variable (X2) of 0.263 indicates a
positive direction. This means that internal control has a positive relationship with
event fraud, where every increase in internal control is 1 percent while other
independent variables are fixed, then PT Brinks Solution Indonesia's event fraud will
experience an increase of 26.3 percent
4. The regression coefficient of the artificial intelligence variable (X3) of 0.281 indicates
a positive direction. This means that artificial intelligence has a positive relationship
with event fraud, where every increase in artificial intelligence is 1 percent while other
independent variables are fixed, then event fraud in PT Brinks Solution Indonesia
employees will increase by 28.1 percent.
Partial T Test
The t-test was carried out to find out whether E-procurement, internal control, and
artificial intelligence partially affected PT Brinks Solution Indonesia's preventing fraud.
Acceptance and rejection of hypotheses will be carried out with the following criteria:
Leyla Kadrina
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5066
a. If the value of sig. ≤ α (0.05) and t calculate > t table, then the hypothesis is accepted.
b. If the value of sig. ≥ α (0.05) and t calculate < t table, then the hypothesis is rejected.
The t-value of the table with a significance level of 0.05 and degrees of freedom
(df) is df = n-k-1= 80-3-1 = 76, so the t-value of the table is obtained of 1.991. The results
of the partial t-test are presented in the following table:
Table 8
Test t
Based on the results of the t-test above, then:
1. Hypothesis 1 = It is suspected that E-procurement has a significant positive effect on
PT Brinks Solution Indonesia's event fraud
The results of the study in Table 4.11, show that the significance value of the E-
procurement variable (X1) < the critical significance value (α = 5%) is 0.000 < 0.05 and
the t calculation of the > t table is 3,790 > 1,991, this shows that E-procurement has a
significant effect on fraud prevention. The regression coefficient of 0.334 indicates a
positive direction. This means that E-procurement has a significant positive effect on PT
Brinks Solution Indonesia's event fraud. Based on these results, the first hypothesis
proposed by the author was declared accepted.
2. Hypothesis 2 = It is suspected that internal control has a significant positive effect on
PT Brinks Solution Indonesia's event fraud
The significance value of the internal control variable (X2) < the critical
significance value (α = 5%) was 0.000 < 0.05 and t calculated > t table was 3.669 > 1.991,
this shows that internal control has a significant effect on fraud prevention. The regression
coefficient of 0.263 indicates a positive direction. This means that internal control has a
significant positive effect on PT Brinks Solution Indonesia's event fraud. Based on these
results, the second hypothesis proposed by the author was declared accepted.
3. Hypothesis 3 = It is suspected that artificial intelligence has a significant positive effect
on PT Brinks Solution Indonesia's prevention of fraud
The significance value of the artificial intelligence variable (X3) < the critical
significance value (α = 5%) was 0.004 < 0.05 and the t calculation of the > t table was
2.977 > 1.991, which shows that artificial intelligence has a significant effect on event
fraud. The regression coefficient of 0.281 indicates a positive direction. This means that
artificial intelligence has a positive effect on PT Brinks Solution Indonesia's event fraud.
Based on these results, the third hypothesis proposed by the author was declared accepted.
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5066
a. If the value of sig. ≤ α (0.05) and t calculate > t table, then the hypothesis is accepted.
b. If the value of sig. ≥ α (0.05) and t calculate < t table, then the hypothesis is rejected.
The t-value of the table with a significance level of 0.05 and degrees of freedom
(df) is df = n-k-1= 80-3-1 = 76, so the t-value of the table is obtained of 1.991. The results
of the partial t-test are presented in the following table:
Table 8
Test t
Based on the results of the t-test above, then:
1. Hypothesis 1 = It is suspected that E-procurement has a significant positive effect on
PT Brinks Solution Indonesia's event fraud
The results of the study in Table 4.11, show that the significance value of the E-
procurement variable (X1) < the critical significance value (α = 5%) is 0.000 < 0.05 and
the t calculation of the > t table is 3,790 > 1,991, this shows that E-procurement has a
significant effect on fraud prevention. The regression coefficient of 0.334 indicates a
positive direction. This means that E-procurement has a significant positive effect on PT
Brinks Solution Indonesia's event fraud. Based on these results, the first hypothesis
proposed by the author was declared accepted.
2. Hypothesis 2 = It is suspected that internal control has a significant positive effect on
PT Brinks Solution Indonesia's event fraud
The significance value of the internal control variable (X2) < the critical
significance value (α = 5%) was 0.000 < 0.05 and t calculated > t table was 3.669 > 1.991,
this shows that internal control has a significant effect on fraud prevention. The regression
coefficient of 0.263 indicates a positive direction. This means that internal control has a
significant positive effect on PT Brinks Solution Indonesia's event fraud. Based on these
results, the second hypothesis proposed by the author was declared accepted.
3. Hypothesis 3 = It is suspected that artificial intelligence has a significant positive effect
on PT Brinks Solution Indonesia's prevention of fraud
The significance value of the artificial intelligence variable (X3) < the critical
significance value (α = 5%) was 0.004 < 0.05 and the t calculation of the > t table was
2.977 > 1.991, which shows that artificial intelligence has a significant effect on event
fraud. The regression coefficient of 0.281 indicates a positive direction. This means that
artificial intelligence has a positive effect on PT Brinks Solution Indonesia's event fraud.
Based on these results, the third hypothesis proposed by the author was declared accepted.
The Influence of E-procurement and Artificial Intelligence on Fraud Prevention at PT Brinks
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5067
Simultaneous Test f
The f test was carried out to prove whether the E-procurement, internal control, and
artificial intelligence variables simultaneously affected the fraud of PT Brinks Solution
Indonesia. The conditions for acceptance or rejection of a hypothesis are as follows:
a) If the significance value (F-statistic) < 0.05 and F calculates > F table, then H4 is
accepted, namely E-procurement, internal control, and artificial intelligence
simultaneously have a significant effect on event fraud at PT Brinks Solution
Indonesia.
b) If the significance value (F-statistic) > 0.05 and F calculates < F table, then H0 is
accepted, namely E-procurement, internal control, and artificial intelligence
simultaneously do not have a significant effect on event fraud in PT Brinks Solution
Indonesia.
The value of the f table at the significance level of 0.05 and the degree of freedom
(df) is df = n-k-1 = 80-3-1 = 76, so the f value of the table is 2.72. The results of the
multiple regression F test can be seen in the following table:
Table 9
Statistical Test Results f
Based on the results of the f test above, then:
Hypothesis 4 = E-procurement, internal control, and artificial intelligence
simultaneously have a significant effect on event fraud in PT Brinks Solution Indonesia.
The results of the F test in Table 4.12, show that the significance value of F-statistic
is smaller than alpha (0.05) which is 0.000 < 0.05 and has an F value calculated > table F
of 44,183 > 2.72, this shows that E-procurement, internal control, and artificial
intelligence simultaneously have a significant effect on event fraud in PT Brinks Solution
Indonesia. Based on these results, the fourth hypothesis proposed by the author was
accepted.
Conclusion
E-procurement, as a transparency solution in the procurement of goods and
services, brings significant positive changes in improving governance, transparency, and
accountability. Through digital platforms, E-Procurement creates an open environment,
eliminates ambiguity, and creates a verified digital track record. The data showed
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5067
Simultaneous Test f
The f test was carried out to prove whether the E-procurement, internal control, and
artificial intelligence variables simultaneously affected the fraud of PT Brinks Solution
Indonesia. The conditions for acceptance or rejection of a hypothesis are as follows:
a) If the significance value (F-statistic) < 0.05 and F calculates > F table, then H4 is
accepted, namely E-procurement, internal control, and artificial intelligence
simultaneously have a significant effect on event fraud at PT Brinks Solution
Indonesia.
b) If the significance value (F-statistic) > 0.05 and F calculates < F table, then H0 is
accepted, namely E-procurement, internal control, and artificial intelligence
simultaneously do not have a significant effect on event fraud in PT Brinks Solution
Indonesia.
The value of the f table at the significance level of 0.05 and the degree of freedom
(df) is df = n-k-1 = 80-3-1 = 76, so the f value of the table is 2.72. The results of the
multiple regression F test can be seen in the following table:
Table 9
Statistical Test Results f
Based on the results of the f test above, then:
Hypothesis 4 = E-procurement, internal control, and artificial intelligence
simultaneously have a significant effect on event fraud in PT Brinks Solution Indonesia.
The results of the F test in Table 4.12, show that the significance value of F-statistic
is smaller than alpha (0.05) which is 0.000 < 0.05 and has an F value calculated > table F
of 44,183 > 2.72, this shows that E-procurement, internal control, and artificial
intelligence simultaneously have a significant effect on event fraud in PT Brinks Solution
Indonesia. Based on these results, the fourth hypothesis proposed by the author was
accepted.
Conclusion
E-procurement, as a transparency solution in the procurement of goods and
services, brings significant positive changes in improving governance, transparency, and
accountability. Through digital platforms, E-Procurement creates an open environment,
eliminates ambiguity, and creates a verified digital track record. The data showed
Leyla Kadrina
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5068
increased vendor participation, stimulated healthy competition, and reduced chances of
collusion. The implications of the transformation of E-Procurement on fraud handling are
very real for PT Brinks. The high level of transparency leads to increased vendor
participation, increased accountability, and strengthened risk management. E-
procurement helps uncover collusion practices through open access to data, creating a
basis for further preventive and enforcement actions. Internal control also has a significant
effect on the level of fraud. Likewise, the use of Artificial Intelligence has a positive
influence on fraud prevention.
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5068
increased vendor participation, stimulated healthy competition, and reduced chances of
collusion. The implications of the transformation of E-Procurement on fraud handling are
very real for PT Brinks. The high level of transparency leads to increased vendor
participation, increased accountability, and strengthened risk management. E-
procurement helps uncover collusion practices through open access to data, creating a
basis for further preventive and enforcement actions. Internal control also has a significant
effect on the level of fraud. Likewise, the use of Artificial Intelligence has a positive
influence on fraud prevention.
The Influence of E-procurement and Artificial Intelligence on Fraud Prevention at PT Brinks
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5069
Bibliography
Amalia, R., Alfan, M. A., Aliefia, M., bin Md Radzi, M. S. N., & Kurniawan, F. (2023).
Digitalization of the Public Procurement System in Indonesia: Challenges and
Problems. Yuridika, 38(3), 499–518.
Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of
transparency: E-government and social media as openness and anti-corruption tools
for societies. Government Information Quarterly, 27(3), 264–271.
Ghozali, I. (2016). Desain penelitian kuantitatif dan kualitatif: untuk akuntansi, bisnis,
dan ilmu sosial lainnya.
Joseph, O. N., Albert, O., & Byaruhanga, J. (2015). Effect of internal control on fraud
detection and prevention in district treasuries of Kakamega County. International
Journal of Business and Management Invention, 4(1), 47–57.
Manjah, N., Othman, R., & Yusoff, H. (2019). Internal Control System in Cooperative
Society. In Organizational Auditing and Assurance in the Digital Age (pp. 276–
301). IGI Global.
Neupane, A., Soar, J., Vaidya, K., & Yong, J. (2014). Willingness to adopt e-procurement
to reduce corruption: Results of the PLS Path modeling. Transforming Government:
People, Process and Policy, 8(4), 500–520.
Rae, K., & Subramaniam, N. (2008). Quality of internal control procedures: Antecedents
and moderating effect on organisational justice and employee fraud. Managerial
Auditing Journal, 23(2), 104–124.
Rotchanakitumnuai, S. (2013). The governance evidence of e‐government procurement.
Transforming Government: People, Process and Policy, 7(3), 309–321.
Tong, Y., Wang, M., & Xu, F. (2014). Internal control, related party transactions and
corporate value of enterprises directly controlled by Chinese central government.
Journal of Chinese Management, 1, 1–14.
Zakaria, K. M., Nawawi, A., & Salin, A. S. A. P. (2016). Internal controls and fraud–
empirical evidence from oil and gas company. Journal of Financial Crime, 23(4),
1154–1168.
Solution Indonesia
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5069
Bibliography
Amalia, R., Alfan, M. A., Aliefia, M., bin Md Radzi, M. S. N., & Kurniawan, F. (2023).
Digitalization of the Public Procurement System in Indonesia: Challenges and
Problems. Yuridika, 38(3), 499–518.
Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of
transparency: E-government and social media as openness and anti-corruption tools
for societies. Government Information Quarterly, 27(3), 264–271.
Ghozali, I. (2016). Desain penelitian kuantitatif dan kualitatif: untuk akuntansi, bisnis,
dan ilmu sosial lainnya.
Joseph, O. N., Albert, O., & Byaruhanga, J. (2015). Effect of internal control on fraud
detection and prevention in district treasuries of Kakamega County. International
Journal of Business and Management Invention, 4(1), 47–57.
Manjah, N., Othman, R., & Yusoff, H. (2019). Internal Control System in Cooperative
Society. In Organizational Auditing and Assurance in the Digital Age (pp. 276–
301). IGI Global.
Neupane, A., Soar, J., Vaidya, K., & Yong, J. (2014). Willingness to adopt e-procurement
to reduce corruption: Results of the PLS Path modeling. Transforming Government:
People, Process and Policy, 8(4), 500–520.
Rae, K., & Subramaniam, N. (2008). Quality of internal control procedures: Antecedents
and moderating effect on organisational justice and employee fraud. Managerial
Auditing Journal, 23(2), 104–124.
Rotchanakitumnuai, S. (2013). The governance evidence of e‐government procurement.
Transforming Government: People, Process and Policy, 7(3), 309–321.
Tong, Y., Wang, M., & Xu, F. (2014). Internal control, related party transactions and
corporate value of enterprises directly controlled by Chinese central government.
Journal of Chinese Management, 1, 1–14.
Zakaria, K. M., Nawawi, A., & Salin, A. S. A. P. (2016). Internal controls and fraud–
empirical evidence from oil and gas company. Journal of Financial Crime, 23(4),
1154–1168.