JURNAL
Riset Akuntansi dan Keuangan Indonesia URL : http://journals.
id/index.
php/reaksi/index Enhaching Fraud Detection Through Auditor Religiosity.
Computer Assisted Audit Techniques, and Task Specific Knowledge: The Moderating Impact of Big Data oBentri ,A Ridu Haifitdo z rs in BPKP.
Sumatera Island Betri1.
Ridho Hafidz2 Program Studi Akuntansi.
Fakultas Ekonomi dan Bisnis.
Universitas Muhammadiyah Palembang *betri.
sirajuddin@gmail.
Keywords:
Auditor Religiosity.
Computer Assisted Audit Techniques.
Task Specific Knowledge.
Big Data.
Fraud Detection ABSTRACT This study investigates the impact of Auditor Religiosity.
Computer Assisted Audit Techniques (CAAT.
, and Task Specific Knowledge on Fraud Detection, with Big Data serving as a moderating variable.
Conducted at the Representative Offices of the State Development Audit Agency in Sumatera, the associative research utilized primary data from 220 questionnaires, analyzed via Partial Least Square Structural Equation Modeling (PLS-SEM).
Findings reveal that Auditor Religiosity and Task Specific Knowledge significantly influence Fraud Detection, while CAATs do Additionally.
Big Data does not moderate the effects of Auditor Religiosity and Task Specific Knowledge on Fraud Detection .
omologizer moderato.
However.
Big Data moderates .
the influence of Computer Assisted Audit Techniques on Fraud Detection .
ure moderato.
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Vol.
9 No.
3 Desember 2024
JURNAL Riset Akuntansi dan Keuangan Indonesia
INTRODUCTION
In this era of globalization, governmental their financial and operational integrity amidst increasingly complex economic and administrative Uncertainty in public policies, demands for effective budget management, and the high need for accountability and transparency are primary Governments must ensure robust systems, both financially and in managerial practices, to prevent fraud amid financial instability.
According to Kumaat .
1: .
Fraud Detection can be described as a series of actions aimed at identifying early signs or strong indicators of potential fraudulent activities, while limiting opportunities for perpetrators.
Religiosity, as discussed by Glock & Stark .
and Sari dkk.
, .
, relates to individualsAo levels of conception and commitment to their religion.
Individuals with high religiosity tend to have strict moral standards and strong work ethics, making them better able to identify fraudulent actions.
CAATs, as defined by Lin & Wang .
, are tools, technologies, and software aiding auditors in control testing, data analysis, and auditing.
The use of CAATs can improve audit efficiency and effectiveness, allowing auditors to conduct tests and analyze data more accurately and Lala et al.
, .
suggest that auditors should no longer rely solely on conventional approaches in Fraud Detection, as failure to gather accurate audit evidence can significantly impact the occurrence of fraud.
Task Specific Knowledge, according to Libby .
, pertains to information relevant to audit tasks, aiding auditors in understanding the audited environment and internal conditions, thereby enhancing their assessment quality.
Chen & Zhang .
explain that Big Data refers to large, complex data sets requiring advanced technology for analysis.
Govindan et al.
, .
state that Big Data has the potential to enhance forensic audit functions in Fraud Detection.
Previous studies by Fadilah dkk.
, .
dan Bandiyono, .
find that religiosity affects Fraud Detection by auditors, consistent with the research by Suci dkk.
, .
which asserts the significant influence of religiosity on auditorsAo ability to detect However, research by Afriana .
suggests different results, indicating that religiosity does not influence internal auditorsAo ability to detect fraud.
Findings from Olasanmi .
Atmaja .
Enhaching Fraud Detection.
p-ISSN:1411-6510 e-ISSN :2541-6111 Fauzi dkk.
, .
, dan Samagaio & Diogo .
support the significant impact of CAATs on Fraud Detection.
Research by Widuri & Gautama .
using a qualitative approach confirms that CAATs implementation plays a crucial role in the audit process and benefits Fraud Detection.
Conversely, studies by Choirunnisa & Rufaedah .
dan Kamal .
suggest that information technology usage does not affect Fraud Detection by auditors.
Studies by Yusrianti .
Betri & Kusumawaty .
Lembayung & Chomsatu .
indicate that Task Specific Knowledge influences Fraud Detection by auditors, supported by research by Johnson et al.
, .
Tirta & Sholihin .
Sari .
, dan Muzdalifah & Syamsu .
Syahputra & Afnan .
Handoko dkk.
, .
Bandiyono .
, dan Surono .
have shown that Big Data influences Fraud Detection.
However, research conducted by Sembiring & Widuri .
yielded different results, indicating that Big Data does not affect Fraud Detection.
Religiosity plays a crucial role in enhancing auditor integrity during the audit process.
Moreover, the utilization of Computer-Assisted Audit Techniques (CAAT.
enables auditors to optimize the efficiency and effectiveness of audit This facilitates more accurate and swift data testing and analysis by auditors.
Additionally, the pivotal role of Task Specific Knowledge cannot be overlooked, as it aids auditors in planning and executing audit procedures more systematically, thereby enhancing the quality of their assessments.
Throughout the audit process.
Task Specific Knowledge assists auditors in comprehending and resolving audit tasks, consequently improving the quality of their evaluations.
Furthermore.
Big Data exerts a significant influence, reinforcing the impact of auditor religiosity by providing broader access to relevant data.
This enables auditors to track suspicious transactions that may contravene ethical or integrity principles.
Moreover, in terms of CAATs utilization.
Big Data enhances its effectiveness by enabling rapid and accurate analysis of large data volumes, thereby strengthening auditorsAo ability to identify suspicious patterns or anomalies.
Lastly.
Big Data also reinforces the influence of specific knowledge on audit tasks by providing broader access to relevant industry and business data.
This allows auditors with in-depth knowledge of the audit context to identify suspicious patterns or trends that may go undetected manually.
p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia
LITERATURE REVIEW AND HYPOTHESIS
DEVELOPMENT
Venkatesh et al.
, .
developed the Unified Theory of Acceptance and Use of Technology, which posits that technology usage is influenced by four main factors: performance expectancy, effort expectancy, social influence, and facilitating This theory provides insights into how information technology can aid in Fraud Detection by auditors by understanding the factors that influence technology adoption.
The concept of cognitive dissonance theory, first proposed by Festinger .
, lays a crucial foundation for understanding communication dynamics and social influence.
This theory highlights the inconsistency between cognitive elements, creating psychological discomfort.
Muzdalifah & Syamsu .
underscore that cognitive dissonance theory impacts the change in auditor attitudes to predict intentions, aiming to reduce the arising inconsistency or dissonance.
These cognitive factors guide auditors to ensure that the opinions they express after an audit align with the evidence they uncover during the examination process.
The assumption of this theory is that individuals tend to seek consistency between beliefs, attitudes, and Inconsistency among these components leads to cognitive dissonance, creating psychological Thus, this theory provides insights into how Task Specific Knowledge can influence the Fraud Detection process conducted by auditors.
Vol.
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3 Desember 2024 gain, ultimately resulting in direct losses to others.
In efforts to enhance performance and reputation, organizations may resort to illegal actions, disregarding the consequences and parties affected by their actions.
Detection, as per the KBBI Daring .
, refers to the effort to discover and determine facts, assumptions, or the existence of something.
Karyono .
3: .
defines Fraud Detection as a process of identifying the occurrence of fraud, its perpetrators, victims, and the reasons behind the According to Glock & Stark .
, religiosity is defined as the level of conception about religion and the level of commitment in religious practices.
Similarly.
Sari dkk.
, .
explain that conception refers to an individualAos understanding of their religious aspects, while commitment level relates to a comprehensive understanding in religious life.
Hence, it can be concluded that individuals adopt different approaches to express and embody their Individuals with high religiosity tend to exhibit strict moral standards and strong work ethics, thus being better able to identify fraudulent Ghufron & Risnawati .
elucidate that religion has a binding nature, implying rules and responsibilities to be adhered to by its followers.
Afriana .
explains that high religiosity can enhance an auditorAos independence as they tend to uphold truth and justice steadfastly.
Attribution theory, first proposed by Heider .
, addresses how individuals explain the causes of behavior and events.
According to Fiske & Taylor .
1: .
, this theory discusses how social perception is used to make causal explanations about events.
Its assumption is that individuals tend to explain behavior, whether their own or othersAo, by attributing causes to internal or external factors, known as dispositional and situational Thus, this theory provides insights into how Auditor Religiosity can influence the Fraud Detection process.
The way auditors interpret suspicious behavior will be influenced by religious values, which are dispositional attributions.
According to Lin & Wang .
CAATs are the utilization of various tools, technologies, and software to assist auditors in performing various audit tasks, including control testing, confirmation, financial statement data analysis, as well as continuous auditing and monitoring.
As per Auditing Standard Section 327 in computerized accounting information systems environments, manual testing is impractical for auditors.
Therefore, computer-assisted audit techniques, or CAATs, are necessary to enhance auditor performance and efficiency and improve Fraud Detection Braun & Davis .
emphasize that CAATs enrich the audit process through technology utilization, employing specialized software that enables auditors to achieve audit objectives more According to Betri .
2: .
, fraud is defined as an unlawful act that can be committed by individuals, both internal and external to an organization, with the motive of personal or group According to Libby .
Task Specific Knowledge refers to information stored in memory, including experiences, practical facts, and theoretical concepts related to audit tasks.
Betri.
Ridho Hafidz Vol.
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3 Desember 2024 JURNAL Riset Akuntansi dan Keuangan Indonesia especially in evaluation and assessment processes.
This information encompasses general and specific knowledge relevant to audit tasks.
Sari .
states that Task Specific Knowledge assists auditors in understanding the audited environment and internal conditions, facilitating structured planning and execution of audit procedures.
During audits.
Task Specific Knowledge aids auditors in completing tasks more effectively, enhancing the quality of their Moyes & Hasan .
assert that the success of organizational audits in uncovering fraud, along with auditorsAo audit experience, is a significant element in potential Fraud Detection in each audit cycle and comprehensive risk assessment.
According to Sairolu & Sinanc .
Big Data refers to a vast collection of data with complex and varied structures.
Govindan et al.
, .
assert that Big Data has significant potential in enhancing forensic audit functions for Fraud Detection.
Bandiyono .
cites one of the benefits of Big Data as its ability to assist companies in identifying fraud risks.
Consequently.
Big Data can support internal auditors in detecting fraud by expanding the coverage of essential data sources.
This enables better analytical processes, ultimately enhancing the audit quality in identifying fraudulent activities.
The Influence of Auditor Religiosity on Fraud Detection According to Glock & Stark .
, religiosity is defined as the level of oneAos conception of their religion and the degree of commitment they have in religious practices.
Similarly.
Sari dkk.
, .
explain that the concept of conception refers to an individualAos understanding of the aspects of their religion, while the level of commitment is related to a comprehensive understanding in religious Research conducted by Fadilah dkk.
, .
dan Bandiyono, .
indicate that there is an influence of religiosity on the detection of fraud by These findings align with a study by Suci , .
which demonstrates that religiosity significantly affects auditorsAo ability to detect fraud.
However, there is research with contrasting results to the aforementioned studies, such as the study conducted by Afriana .
which shows that religiosity does not influence the internal auditorsAo ability to detect fraud.
Based on the above review, the researcher proposes a hypothesis:
H1a: Auditor Religiosity has a significant influence on Fraud Detection Enhaching Fraud Detection.
p-ISSN:1411-6510 e-ISSN :2541-6111 The Influence of Computer Assisted Audit Techniques on Fraud Detection CAATs, as defined by Perdana .
0: .
, are approaches that facilitate auditing processes using computers and related technologies.
Zamzami , .
1: .
define CAATs as crucial tools that automate and audit audit data.
The use of CAATs has transformed the auditing approach paradigm in Fraud Detection in the information technology CAATs provide auditors with the ability to delve deeper into data, analyze suspicious patterns and trends, and detect fraud indicators that are difficult to find manually.
Research by Olasanmi .
Atmaja .
Fauzi dkk.
, .
, dan Samagaio & Diogo .
proves that CAATs have a significant impact on Fraud Detection.
This study, supported by research by Widuri & Gautama .
, uses a qualitative approach and shows that the implementation of CAATs plays a crucial role in the audit process and provides benefits to audit results in detecting fraud.
However, research by Choirunnisa & Rufaedah .
dan Kamal .
shows that the use of information technology has no effect on Fraud Detection by auditors.
Based on this, auditors using CAATs as tools in the audit process will find it easier to identify existing anomalies, ultimately improving their examination Based on the above review, the researcher proposes a hypothesis:
H1b: Computer Assisted Audit Techniques has a significant influence on Fraud Detection The Influence of Task Specific Knowledge on the Fraud Detection Task Specific Knowledge, according to Libby .
, refers to information stored in memory, including experience, practical facts, and theoretical concepts related to the performance of audit tasks, particularly in the evaluation and assessment This information encompasses general knowledge and more specific knowledge relevant to audit tasks.
According to Sari .
Task Specific Knowledge can help auditors gain a deeper understanding of the audited environment and internal conditions, facilitating more structured audit planning and execution.
During the audit process.
Task Specific Knowledge assists auditors in understanding and completing audit tasks, enhancing the quality of their assessments.
Research by Yusrianti .
Betri & Kusumawaty .
Lembayung & Chomsatu .
indicates that Task p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia Specific Knowledge influences Fraud Detection by This research is supported by studies by Johnson et al.
, .
Tirta & Sholihin .
Sari .
, dan Muzdalifah & Syamsu .
, which show that Task Specific Knowledge affects auditorsAo ability to detect fraud.
Task Specific Knowledge enhances auditorsAo abilities during the audit process to detect fraud, facilitates more structured audit planning and execution, and assists auditors in understanding and completing audit tasks, thereby improving the quality of their assessments.
Based on the above review, the researcher proposes a H1c: Task Specific Knowledge has a significant influence on Fraud Detect The Influence of Auditor Religiosity on Fraud Detection with Big Data As a Moderating Variable Religiosity, as defined by Lestari & Indrawati .
, reflects the internalization of religious values through adherence to and comprehension of religious teachings, manifested in everyday behavior.
Additionally.
Big Data, as elucidated by Sairolu & Sinanc .
, refers to vast collections of data characterized by complex and varied structures.
Hipgrave .
underscores the potential of Big Data in expediting fraud investigation through data Both Religiosity and Big Data influence the Fraud Detection process.
Big data reinforces the influence of auditor religiosity by providing broader access to relevant data, enabling auditors to trace suspicious transactions that may contravene ethical or integrity principles.
Previous research conducted by Syahputra & Afnan .
Handoko dkk.
Bandiyono .
, dan Surono .
has demonstrated that Big Data exerts an influence on Fraud Detection.
The findings of this study indicate that the utilization of Big Data plays a significant role in the Fraud Detection process.
However, contrary to the findings of research conducted by Sembiring & Widuri .
, which suggest that Big Data does not influence Fraud Detection.
Based on the above review, the researcher proposes a hypothesis:
H2a: Big Data Moderates the Influence of Auditor Religiosity on Fraud Detection The Influence of Computer Assisted Audit Techniques on Fraud Detection with Big Data As a Moderating Variable The utilization of Computer Assisted Audit Techniques (CAAT.
has become crucial in Vol.
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3 Desember 2024 enhancing the efficiency and effectiveness of audit processes.
Braun & Davis .
elucidate that CAATs enrich the audit process through the utilization of specialized technology that empowers auditors to attain audit objectives.
According to Zamzami dkk.
, .
1: .
CAATs play a significant role in automating and auditing audit Conversely.
Sairolu & Sinanc .
refer to large, complex, and varied datasets.
Hipgrave .
highlights the potential of Big Data in expediting fraud investigations through data integration.
Big Data can moderate the influence of Computer Assisted Audit Techniques on Fraud Detection by strengthening or weakening the relationship between the two.
CAATs are tools and techniques used by auditors to analyze data automatically and identify potential fraud or anomalies.
In the context of big data.
CAATs become more effective as they can process large volumes of data quickly and accurately.
By harnessing big data, auditors can integrate CAATs into their audit processes to identify suspicious patterns or trends that may go undetected manually.
Previous research conducted by Syahputra & Afnan .
Handoko dkk.
Bandiyono .
, dan Surono .
has shown that Big Data influences Fraud Detection.
The findings of this research indicate that the utilization of Big Data plays a crucial role in the Fraud Detection process.
However, contrary to the results of research conducted by Sembiring & Widuri .
, which show that Big Data does not have an influence on Fraud Detection.
Based on the above review, the researcher proposes a hypothesis:
H2b: Auditor Religiosity Moderates the Influence of Computer Assisted Audit Techniques on Fraud Detection The Influence of Task Specific Knowledge on the Fraud Detection with Big Data As a Moderating Variable Task Specific Knowledge, as elucidated by Sari .
and Libby .
, refers to a specialized understanding of specific tasks, particularly within the context of audit tasks.
This knowledge assists auditors in comprehending the environment and internal conditions of the audited entity, enabling them to plan and execute more directed audit Conversely.
Sairolu & Sinanc .
refer to a large dataset that exhibits complex and varied structures.
Hipgrave .
highlights the potential of Big Data in expediting fraud Betri.
Ridho Hafidz Vol.
9 No.
3 Desember 2024 investigations through data integration.
Big Data can moderate the influence of Task Specific Knowledge on Fraud Detection by reinforcing or attenuating the relationship between them.
Task specific knowledge entails the auditorAos understanding of the industry, business, or processes being audited.
Big data can enhance the influence of task-specific knowledge by providing broader access to relevant industry and business data.
Auditors with robust task-specific knowledge can utilize big data to identify patterns or trends in data that may indicate Previous research conducted by Syahputra & Afnan .
Handoko dkk.
, .
Bandiyono .
, dan Surono .
has demonstrated that Big Data influences Fraud Detection.
These research findings indicate that the utilization of Big Data plays a crucial role in the Fraud Detection process.
However, in contrast to the findings of Sembiring & Widuri .
, which indicate that Big Data has no influence on Fraud Detection.
Based on the above review, the researcher proposes a hypothesis:
H2c: Big Data Moderates the Influence of Task Specific Knowledge on Fraud Detection RESEARCH METHODS This study employs an associative causal research design to investigate the impact of Auditor Religiosity.
Computer Assisted Audit Techniques (CAAT.
, and Task Specific Knowledge on Fraud Detection, with Big Data acting as a moderating variable.
Primary data were collected via questionnaires distributed to the Representative Offices of the State Development Audit Agency (BPKP) in Sumatera.
The studyAos population consists of 876 auditors, with a sample size of 276 auditors determined using SlovinAos formula at a 5% significance level, through probability sampling with simple random sampling methods.
The research adopts a Structural Equation Modeling (SEM) approach, utilizing Partial Least Squares (PLS) methodology via Smart PLS 4 This choice is made over CovarianceBased SEM (CB SEM) methods, such as AMOS or LISREL, due to several considerations.
The datasetAos non-normal distribution aligns with PLS SEMAos robustness to deviations from normality, whereas CB SEM requires stricter assumptions.
Additionally, the complex model, involving multiple variables and interaction effects, is better suited for PLS SEM, which can manage intricate models with Enhaching Fraud Detection.
p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia latent variables and smaller sample sizes effectively.
Although CB SEM could yield comparable results with larger datasets.
PLS SEMAos flexibility and efficiency align better with the researchAos objectives and constraints.
In summary, employing Smart PLS 4 for SEM analysis ensures rigorous evaluation of both the outer and inner models.
This includes assessments of convergent validity, discriminant validity, and internal consistency reliability, as well as analyses of R Square, effect sizes (F Squar.
Q Square, and significance testing .
-tests and MRA).
This methodological choice supports the robustness and validity of the studyAos findings, adhering to best practices in empirical auditing research.
RESULTS AND DISCUSSION
The characteristics of the respondents in this study consist of gender, age, highest education level, functional auditor position (JFA), and length of employment.
There were 120 male respondents .
55%) and 100 female respondents .
45%).
The majority of respondents were aged between 21 and 40 years, with the highest number in the age range of 31-40 years .
09%).
In terms of highest education level, most respondents had a D4/S1 educational background .
18%), followed by D3 .
91%) and S2 .
91%).
The most common functional auditor position was Auditor Pertama .
09%) and Auditor Muda .
64%).
The majority of respondents .
82%) had been employed for more than 10 years.
Table 1: Statistics Descriptive Theoritical Actually Variables Range Median Range Mean
Std
6 to 30
9 to 30
CAATs
17 to 85
38 to 85
TSK
14 to 70 14 to 70 18 to 90 32 to 89 11 to 55 22 to 55 Source: Primary Data Processed, 2024 Based on the table above, the average score of respondents regarding the indicators of the Auditor Religiosity construct reaches 26.
03, which significantly exceeds the theoretical median value These results indicate that the majority of respondents have a significant level of religiosity, p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia exceeding the median value used as a benchmark in the theoretical range.
Similarly, the average score of respondents regarding the indicators of the Computer Assisted Audit Techniques construct reaches 70.
32, which significantly exceeds the theoretical median value of 51.
These results indicate that the majority of respondents have a much higher perception regarding the Computer Assisted Audit Techniques construct in the audit process compared to the theoretical median value.
Furthermore, the average score of respondents regarding the indicators of the Task Specific Knowledge construct reaches 61.
63, which significantly exceeds the theoretical median value This finding indicates that the majority of respondents have adequate knowledge to handle audit tasks.
Additionally, the descriptive statistical test results indicate that the average score of respondents regarding the indicators of the Big Data construct reaches 68.
92, which significantly exceeds the theoretical median value of 54.
This indicates that the majority of respondents have a higher perception regarding the Big Data construct.
Finally, the average score of respondents regarding the indicators of the Fraud Detection construct 75, which significantly exceeds the theoretical median value of 33.
This finding indicates that the majority of respondents have a high awareness and ability in Fraud Detection.
OUTER MODEL
The initial results of the outer model analysis include a comprehensive evaluation of the validity and reliability of constructs.
Figure 1: The Initial Measurement Model Source: Primary Data Processed, 2024 Based on the outer model above, it can be observed that all outer loading values of those indicators exceed the threshold of 0.
Additionally, the Average Variance Extracted (AVE) values also surpass 0.
50, indicating that Vol.
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3 Desember 2024 all indicators meet the validity criteria.
All outer loading values on the manifest variables to the dimensional constructs, as well as the outer loading values from the dimensional constructs to the Auditor Religiosity variable, all exceed 0.
The AVE calculated for each dimensional construct and the Auditor Religiosity variable also exceed the threshold of 0.
50, indicating convergent validity on the Auditor Religiosity variable and its Table 2: Correlation Values Among Construct Dimensions.
AVE.
CR, and CA
Composite Indicator INA KPA KK AVE
CronbachAos Alpha
IIA
923 Reliability KPA SR of AVE Source: Primary Data Processed, 2024 Furthermore, the Fornell & Larcker .
criteria test revealed that each correlation between latent variables resulted in values lower than the square root of the Average Variance Extracted (AVE) of each related construct.
Therefore, it is concluded that the Religiosity Auditor latent variable meets the criteria for discriminant validity.
Table 3: Cross Loading Indicator RA_1 RA_2 RA_3 RA_4 RA_5 RA_6 INA
KPA
Description Valid Valid Valid Valid Valid Valid Source: Primary Data Processed, 2024 The subsequent test, comparing the outer loading and cross-loading values, found that each outer loading value of the indicators onthe respective constructs consistently exceeded all cross-loading values from other constructs.
This indicates that discriminant validity on the Religiosity Auditor variable has been met.
The final test regarding internal consistency reliability showed that the CronbachAos Alpha values for each dimensional construct revealed numbers above 0.
70, while the Composite Reliability values for each dimensional construct also exceeded 0.
Therefore, it can be acknowledged that the Religiosity Auditor variable Betri.
Ridho Hafidz Vol.
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3 Desember 2024 JURNAL Riset Akuntansi dan Keuangan Indonesia and its manifestations demonstrate an adequate level of reliability.
By evaluating the outer model measurement on the Religiosity Auditor latent variable, it can be concluded that all dimensional constructs and Religiosity Auditor variables show a level of validity and reliability that meets the rule of thumb criteria.
Figure 2: The Initial Measurement Model Source: Primary Data Processed, 2024 Based on the above outer model, it can be exceed the threshold of 0.
Additionally, the Average Variance Extracted (AVE) values also 50, indicating that all indicators can be considered to meet the validity requirements.
All outer loading values on the manifest variables to the dimensional constructs, as well as the outer loading values from the dimensional constructs to the CAATs variable, all exceed the threshold of The calculated AVE for each dimensional construct and the CAATs variable also exceeds the threshold of 0.
50, depicting the fulfillment of convergent validity on the CAATs variable and its Table 4: Correlation Values Among Construct Dimensions Construct
ATU
PEU
ATU
PEU
SR of AVE p-ISSN:1411-6510 e-ISSN :2541-6111 Furthermore, in the Fornell & Larcker .
criteria test, there was a redundancy issue where the correlation value between indicators, thus it was concluded that the square root value of the AVE of the PEU construct was smaller than the correlation value between latent constructs, which was the most significant value of 0.
860 < 0.
This indicates that the Computer Assisted Audit Techniques latent variable does not meet the discriminant validity criteria.
Therefore, the step taken to address this issue is to remove indicators that have high correlations with other indicators measuring different dimensions.
After that, a revision was made, so there was a total of 1 model revision resulting in 2 tests.
With the first revision model being the final model as convergent validity, discriminant validity, and internal consistency reliability have been met.
Below are the details of the final model:
Figure 3: The Final Measurement Model Source: Primary Data Processed, 2024 The results of the revision on the measurement instrument of CAATs variable yielded parameter estimations that attained validity and reliability levels meeting the rule of thumb standard.
All outer loading values on the manifest variables to the dimensional constructs, as well as the outer loading values from the dimensional constructs to the CAATs variable, exceeded the threshold of 0.
Source: Primary Data Processed, 2024 Enhaching Fraud Detection.
p-ISSN:1411-6510 e-ISSN :2541-6111 Vol.
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3 Desember 2024 JURNAL Riset Akuntansi dan Keuangan Indonesia Table 5: Correlation Values Among Construct Dimensions.
AVE.
CR, and CA
Construct
ATU
PEU
ATU
PEU
SR of AVE
AVE
Composite Reliability CronbachAos Alpha Source: Primary Data Processed, 2024 Additionally, the Average Variance Extracted (AVE) values calculated for each dimensional construct and the CAATs variable also surpassed the threshold of 0.
50, indicating that convergent validity on the CAATs variable and its manifestations has been fulfilled.
Furthermore, in the Fornell & Larcker .
criteria test, it was revealed that each correlation between latent variables resulted in values lower than the square root of the Average Variance Extracted (AVE) of the respective related Therefore, it was concluded that the CAATs latent variable meets the criteria for discriminant validity.
Table 6: Cross Loading Indicator PEU
ATU
Description CAATs_1 Valid CAATs_2 Valid CAATs_3 Valid CAATs_4 Valid CAATs_5 Valid CAATs_6 Valid CAATs_7 Valid CAATs_8 Valid CAATs_9 Valid CAATs_12 Valid CAATs_13 Valid CAATs_14 Valid CAATs_15 Valid CAATs_17 Valid Source: Primary Data Processed, 2024 The subsequent test involved comparing the outer loading values and cross-loading, where it was found that each indicatorAos outer loading value consistently surpassed all cross-loading values from other constructs.
This indicates that discriminant validity on the CAATs variable has been met.
The final test was internal consistency Referring to the data listed in the table, it can be observed that the CronbachAos Alpha values for each dimensional construct exceed 70, while the Composite Reliability values for each dimensional construct also exceed 0.
Therefore, it can be acknowledged that the CAATs variable and its manifestations exhibit an adequate level of reliability.
By evaluating the outer model measurement on the CAATs latent variable, which has been revised once, it can be concluded that all dimensional constructs and CAATs variables in the first revision demonstrate validity and reliability levels that meet the rule of thumb criteria.
Figure 4: The Initial Measurement Model Source: Primary Data Processed, 2024 Betri.
Ridho Hafidz Vol.
9 No.
3 Desember 2024 p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia Based on the outer model above, it can be observed that all the outer loading values of those indicators surpass the threshold of 0.
Additionally, the Average Variance Extracted (AVE) values also exceed 0.
50, indicating that all the indicators can be considered to meet the validity requirements.
All the outer loading values on the manifest variables to the dimensional constructs, as well as the outer loading values from the dimensional constructs to the task specific knowledge variable, all exceed the value of 0.
The calculated AVE for each dimensional construct and the task specific knowledge variable also surpass the threshold of 0.
50, depicting the fulfillment of convergent validity on the task specific knowledge variable and its manifestations.
constructs to the Task Specific Knowledge variable, exceeded the threshold of 0.
Figure 5: The Final Measurement Model Source: Primary Data Processed, 2024 Table 8: Correlation Values Among Construct Dimensions.
AVE.
CR, and CA
Table 7: Correlation Values Among Construct Dimensions Auditor
Construct
Technical Capacity Knowledge Auditor Capacity Technical SR of AVE
Construct
SR of AVE
AVE
Composite Reliability CronbachAos Alpha Source: Primary Data Processed, 2024 Source: Primary Data Processed, 2024 Furthermore, in the Fornell & Larcker .
criteria test, there was redundancy issue where the correlation values between indicators.
It was concluded that all correlation values between latent constructs were greater than the square root of the AVE of each related construct.
This indicates that the Task Specific Knowledge latent variable does not meet the criteria for discriminant validity.
Therefore, the step taken to address this issue was to remove indicators with high correlations with other indicators measuring different dimensions.
After that, a revision was made, resulting in a total of 1 model revision, thus there were 2 rounds of testing.
The first revision model is considered the final model because convergent validity, discriminant validity, and internal consistency reliability have been met.
Below are the details of the final model:
The revision results on the instrument measuring the Task Specific Knowledge variable yielded parameter estimates that met the validity and reliability standards according to the rule of All outer loading values on the manifest variables to the dimensional constructs, as well as the outer loading values from the dimensional Enhaching Fraud Detection.
Additionally, the Average Variance Extracted (AVE) values calculated for each dimensional construct and the Task Specific Knowledge variable also exceeded the threshold of 0.
50, indicating that convergent validity on the Task Specific Knowledge variable and its manifestations had been fulfilled.
In the Fornell & Larcker .
criteria test, it was revealed that each correlation between latent variables produced values lower than the square root of the Average Variance Extracted (AVE) of each related construct.
Therefore, it was concluded that the Task Specific Knowledge latent variable met the criteria for discriminant validity.
Table 9: Cross Loading Indicator Technical Knowledge Auditor Capacity Keterangan TSK_1 Valid
TSK_3
Valid
TSK_4
Valid
TSK_6
Valid
TSK_8
Valid
TSK_9
Valid
TSK_14
Valid Source: Primary Data Processed, 2024 p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia In the subsequent test, comparing the outer loading and cross-loading values, it was found that each outer loading value of the indicators on the respective constructs consistently exceeded all cross-loading values from other constructs.
This indicated that discriminant validity on the Task Specific Knowledge variable had been met.
In the final test regarding internal consistency reliability, the data showed that the CronbachAos Alpha values for each dimensional construct revealed numbers 70, while the Composite Reliability values for each dimensional construct also exceeded 0.
Therefore, it can be concluded that the Task Specific Knowledge variable and its manifestations showed an adequate level of reliability.
By evaluating the outer model measurement on the Task Specific Knowledge latent variable, which had been revised once, it can be concluded that all dimensional constructs and Task Specific Knowledge variables in the first revision showed a level of validity and reliability that met the rule of thumb criteria.
Vol.
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3 Desember 2024 Indicators BD_3.
BD_7, and BD_13 are the cause of the invalidity of the Big Data variable.
the first revision stage, there was a redundancy issue where there was a significant correlation between indicator BD_15 .
and BD_14 .
, with a correlation value of 0.
This finding indicates redundancy between these indicators measuring different dimensions.
The step taken to address this issue is to remove indicators that have high correlations with other indicators measuring different dimensions.
After the revision, a second revision was performed, resulting in a total of three The second revision model is considered the final model because convergent validity, discriminant validity, and internal consistency reliability have been met.
Here are the details of the final model:
Figure 7: The Final Measurement Model Source: Primary Data Processed, 2024 Figure 6: The Initial Measurement Model The results of the revision on the measurement instrument of the Big Data variable yielded parameter estimates that met the rule of thumb standards for validity and reliability.
All outer loading values on the manifest variables to the dimensional constructs, as well as the outer loading values of the dimensional constructs to the Big Data variable, exceeded the threshold of 0.
Source: Primary Data Processed, 2024 Based on the outer model analysis above, it is concluded that some manifest variables have outer loading values below 0.
ndicated in gra.
on the Big Data variable, indicating the invalidity of some dimensions.
This indicates the need for revision of the Big Data variable measurement Table 10: Correlation Values Among Construct Dimensions.
AVE.
CR, and CA
Construct
Value Variety Velocity Veracity Volume Value Variety Velocity Veracity Volume SR of AVE
AVE
Composite Reliability CronbachAos Alpha Source: Primary Data Processed, 2024 Betri.
Ridho Hafidz Vol.
9 No.
3 Desember 2024 p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia Additionally, the Average Variance Extracted (AVE) values calculated for each dimensional construct and the Big Data variable also exceeded the threshold of 0.
50, indicating that convergent validity on the Big Data variable and its manifestations has been met.
Furthermore, in the Fornell & Larcker .
criteria test, it was revealed that each correlation between latent variables produced a value lower than the square root of the Average Variance Extracted (AVE) of the respective related constructs.
Therefore, it can be concluded that the Big Data latent variable meets the criteria for discriminant validity.
Table 11: Cross Loading Indicator Volume Variety Variety Veracity Value Description BD_1 Valid
BD_2
Valid
BD_4
Valid
BD_5
Valid
BD_6
Valid
BD_8
Valid
BD_9
Valid
BD_10
Valid
BD_11
Valid
BD_12
Valid
BD_14
Valid
BD_16
Valid
BD_17
Valid
BD_18
Valid The next test involved comparing the outer loading values and cross loading values, which found that each indicatorAos outer loading value on the respective construct consistently exceeded all cross loading values from other constructs.
This indicates that discriminant validity on the Big Data variable has been met.
The final test was internal consistency reliability.
Referring to the data listed in the table 4, it can be observed that the CronbachAos Alpha values for each dimensional construct reveal figures above 0.
70, while the Composite Reliability values for each dimensional construct also show figures exceeding 0.
Therefore, it can be acknowledged that the Big Data variable along with its manifestations demonstrate an adequate level of reliability.
By evaluating the outer model measurement of the Big Data latent variable, which has been revised twice, it can be concluded that all dimensional constructs and the Big Data variable in the third revision exhibit levels of validity and reliability that meet the rule of thumb criteria.
Enhaching Fraud Detection.
Figure 8: The Initial Measurement Model Source: Primary Data Processed, 2024 Based on the outer model above, it can be observed that all outer loading values of those indicators exceed the threshold of 0.
Additionally, the Average Variance Extracted (AVE) values also surpass 0.
50, indicating that all indicators meet the validity criteria.
All outer loading values on the manifest variables to the dimensional constructs, as well as the outer loading values from the dimensional constructs to the Fraud Detection p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia variable, all exceed 0.
The AVE calculated for each dimensional construct and the Fraud Detection variable also exceed the threshold of 0.
50, indicating convergent validity on the Fraud Detection variable and its manifestations.
Table 12: Correlation Values Among Construct Dimensions Construct
Kak
Kak
KeCTan
KDK
SKT
KeCTan
KDK
SKT
SR of AVE Vol.
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3 Desember 2024 Therefore, the step taken to address this issue is to remove indicators that have high correlations with other indicators measuring different dimensions.
After that, a revision was made, so there was a total of 1 revision to the model, resulting in 2 testing phases.
The first revised model is considered as the final model because convergent validity, discriminant validity, and internal consistency reliability have been fulfilled.
Below is the detail of the final model:
Source: Primary Data Processed, 2024 Furthermore, in the Fornell & Larcker .
criteria test, there was redundancy issue where the correlation values between indicators showed that the square root of the AVE of the KDK construct was smaller than the correlation values between latent constructs (SKT) which were significant, 935 < 0.
Likewise, the square root of the AVE of the KDK construct was smaller than the correlation values between latent constructs (KeCTa.
which were significant, namely 0.
< 0.
And the square root of the AVE of the KDK construct was equal to the correlation values between latent constructs (Ka.
, which is 0.
This indicates that the Fraud Detection latent variable does not meet the criteria for discriminant validity.
Figure 9: The Final Measurement Model The results of the revision on the measurement instrument of Fraud Detection variable yielded parameter estimations that meet the levels of validity and reliability according to the rule of thumb standard.
All outer loading values on the manifest variables to the dimensional constructs, and the outer loading values of the dimensional constructs to the Fraud Detection variable, exceed the threshold of 0.
Table 13: Correlation Values Among Construct Dimensions.
AVE.
CR, and CA
Indicator Kak
KeCTan
KDK
SKT
Kak
KeCTan
KDK
SKT
SR of AVE
AVE
Composite Reliability CronbachAos Alpha Source: Primary Data Processed, 2024 Additionally, the Average Variance Extracted (AVE) values calculated for each dimensional construct and the Fraud Detection variable also exceed the threshold of 0.
50, indicating that convergent validity on the Fraud Detection variable and its manifestations has been fulfilled.
In the Fornell & Larcker .
criteria test, it was revealed that each correlation between latent variables yields a value lower than the square root of the Average Variance Extracted (AVE) of each related construct.
Therefore, it is concluded that the Fraud Detection latent variable meets the criteria for discriminant Betri.
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Table 14: Cross Loading Indica-
p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia
KDK
SKT
KAk
KeCTan
Description PF_2 Valid
PF_3
Valid
PF_4
Valid
PF_5
Valid
PF_6
Valid
PF_7
Valid
PF_8
Valid
PF_9
Valid
PF_10
Valid
PF_11
Valid Source: Primary Data Processed, 2024 The subsequent test involves comparing the outer loading values and cross-loading values, where it was found that each outer loading value of the indicators on the respective constructs consistently exceeds all cross-loading values from other constructs.
This indicates that discriminant validity on the Fraud Detection variable has been The final test is internal consistency Referring to the data listed in the table, it can be observed that the CronbachAos Alpha values for each dimensional construct are above 0.
while the Composite Reliability values for each dimensional construct also exceed 0.
Therefore, it can be acknowledged that the Fraud Detection variable along with its manifestations demonstrate adequate levels of reliability.
By evaluating the outer model measurement of the Fraud Detection latent variable, which has been revised once, it can be concluded that all dimensional constructs and the Fraud Detection variable in the first revision demonstrate levels of validity and reliability that meet the rule of thumb criteria.
INNER MODEL
From the table above, it can be seen that the Adjusted R Square value reaches 0.
This result indicates that 97.
4% of the variance in the Fraud Detection variable can be explained by the variance in the Auditor Religiosity (X.
Computer Assisted Audit Techniques (X.
Task Specific Knowledge (X.
, and Big Data (X.
Conversely, the remaining 2.
6% is influenced by other factors outside the scope of this study.
This interpretation implies that the higher the contribution of these three exogenous variables to the endogenous variable, the stronger the relationship in the structural equation.
Referring to the rule of thumb criteria adopted from Bollen .
and Hair et , .
, it can be concluded that this model is categorized as a strong model, with a value of 926 exceeding the threshold of 0.
67 and 0.
75, as recommended by these criteria.
Effect Size F2 Hair et al.
, .
2: .
explained that the effect size F2 facilitates the assessment of the contribution of an exogenous construct to the R2 value of the predictor latent variable.
Values of F2 at 0.
15, and 0.
35 indicate small, medium, or large effects, respectively, of a predictor construct on an endogenous construct.
The F2 test was conducted using Smart PLS 4 with the following results:
Table 16: Effect Size F2
Fraud Detection Auditor Religiosity Computer Assisted Audit Techniques Task Speciflc Knowledge Big Data
AR_BD
CAATs_BD TSK_BD Source: Primary Data Processed, 2024 R Square The coefficient of determination (R Square Adjuste.
test was conducted using Smart PLS 4, resulting in the following data:
Table 15: R Square Fraud Detection R Square R Square Adjusted Source: Primary Data Processed, 2024 Enhaching Fraud Detection.
Based on the test results, the effect size f2 values are as follows: Auditor Religiosity .
>0.
Computer Assisted Audit Techniques .
000, <0.
, and Task Specific Knowledge .
>0.
This indicates that X1 has a large effect size f2, while X2 and X3 do not have significant effect sizes f2.
Additionally, the effect size f2 for the Big Data variable is 0.
000 (<0.
, indicating that X4 does not have a significant effect size f2.
Furthermore, the effect size f2 for the moderating p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia effects are as follows: RA_BD is 0.
004 (<0.
for M1.
CAATs_BD is 0.
019 (>0.
for M2, and TSK_ BD is 0.
000 (<0.
for M3.
This shows that M1 and M3 do not have significant effect sizes f2, while M2 has a medium effect size f2.
Q2 Predictive Relevance Table 17: Q2 Predictive Relevance Fraud
Detection RMSE
MAE
QA_predict Source: Primary Data Processed, 2024 Based on the table above, it can be seen that the value of Q2 prediction reaches 0.
This result indicates that the model has strong predictive A Q2 prediction value approaching 1 indicates that the model has high predictive ability.
Significance Test Table 18: Path Coefficient, t-statistics significance, dan p-value Original Sample (O) Statistics P Values Result Ha1.
Ha1.
Ha1.
Ha2.
CAATs_BD -> PF Ha2.
TSK_BD ->
Ha2.
Konstrulk
RA -> PF
CAATs -> PF
TSK -> PF
BD -> PF
RA_BD
Source: Primary Data Processed, 2024 Based on the original sample value of 0.
7%), there is a positive and significant influence of Auditor Religiosity on Fraud Detection, as evidenced by the t-statistic value of 66.
126, which is greater than the t-table value of 1.
Additionally, the obtained p-value of 0.
000 indicates statistical significance (< 0.
Therefore, based on the research hypothesis.
H01.
a is rejected and Ha1.
a is accepted, indicating that Auditor Vol.
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3 Desember 2024 Religiosity has a significant influence on Fraud Detection.
Based on the original sample value of 0.
3%), there is a positive but not significant influence of Computer Assisted Audit Techniques on Fraud Detection, as evidenced by the t-statistic value of 0.
160, which is less than the t-table value of 1.
Additionally, the obtained p-value of 0.
436 indicates no statistical significance (> 0.
Therefore, based on the research hypothesis.
H01.
b is accepted and Ha1.
b is rejected, indicating that Computer Assisted Audit Techniques do not have a significant influence on Fraud Detection.
Based on the original sample value of 0.
5%), there is a positive and significant influence of Task Specific Knowledge on Fraud Detection, as evidenced by the t-statistic value 793, which is greater than the t-table value Additionally, the obtained p-value of 036 indicates statistical significance (< 0.
Therefore, based on the research hypothesis.
H01.
c is rejected and Ha1.
c is accepted, indicating that Task Specific Knowledge has a significant influence on Fraud Detection.
Based on the original sample value of -0.
(-1.
6%), there is a negative influence of Big Data on the impact of Auditor Religiosity on Fraud Detection.
However, this influence is not significant, as evidenced by the t-statistic value of 1.
055, which is less than the t-table value of 1.
Additionally, the obtained p-value of 0.
146 indicates no statistical significance (> 0.
Therefore, based on the research hypothesis.
H02.
a is accepted and Ha2.
a is rejected, indicating that Big Data does not moderate the influence of Auditor Religiosity on Fraud Detection.
Based on the original sample value of 0.
2%), there is a positive and significant influence of Big Data on the impact of Computer Assisted Audit Techniques on Fraud Detection, as evidenced by the t-statistic value of 2.
362, which is greater than the t-table value of 1.
Additionally, the obtained p-value of 0.
009 indicates statistical significance (< 0.
Therefore, based on the research hypothesis.
H02.
b is rejected and Ha2.
b is accepted, indicating that Big Data Betri.
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Based on the original sample value of -0.
(-0.
1%), there is a negative influence of Auditor Religiosity on the impact of Task Specific Knowledge on Fraud Detection.
However, this influence is not significant, as evidenced by the t-statistic value of 0.
088, which is less than the t-table value of 1.
Additionally, the obtained p-value of 0.
465 indicates no statistical significance (> 0.
Therefore, based on the research hypothesis.
H02.
c is accepted and Ha2.
c is rejected, indicating that Big Data does not moderate the influence of Task Specific Knowledge on Fraud Detection.
DISCUSSION
H1a: Auditor Religiosity has a significant influence on Fraud Detection Based on the data analysis, it is confirmed that Auditor Religiosity has a significant influence on Fraud Detection.
Attribution theory, proposed by Heider .
, is used as the framework to explain the research findings.
This theory considers how individuals explain behavior and events, either through dispositional attributions .
elated to the individua.
or situational attributions .
elated to the contex.
In this context, auditors with higher religiosity tend to have more positive dispositional attributions towards ethical behavior and are more likely to perceive unethical behavior as wrong, thereby increasing their likelihood of detecting In synthesis, attribution theory suggests that auditor religiosity influences the dispositional and situational attributions used by auditors in understanding behaviors and events associated with accounting fraud.
Higher auditor religiosity can enhance more positive dispositional attributions and more accurate situational attributions, thereby improving auditorsAo ability to detect accounting Religious auditors may be more sensitive to indications of fraud because they are more inclined to believe that ethical behavior should always be maintained, regardless of whether the situation is supportive or not.
Therefore, religiosity not only affects auditorsAo intrinsic motivation but also their assessment and reaction to potentially fraudulent These findings are consistent with the research conducted by Fadilah dkk.
, .
Enhaching Fraud Detection.
p-ISSN:1411-6510 e-ISSN :2541-6111 and Bandiyono, .
, which demonstrated the influence of religiosity on fraud detection by This research aligns with the study by Suci dkk.
, .
, which showed that religiosity significantly impacts auditorsAo ability to detect H1b: CAATs has a significant influence on Fraud Detection Based on the data analysis, it is confirmed that CAATs (Computer Assisted Audit Technique.
do not have a significant influence on Fraud Detection.
This is due to the fact that, although auditors have a positive perception of using Excel as an audit tool, the capabilities and complexity of Excel may not be sufficient to detect more sophisticated fraud.
The Unified Theory of Acceptance and Use of Technology (UTAUT) is used as the framework to explain the relationship between the use of CAATs and fraud UTAUT considers four main constructs:
performance expectancy, effort expectancy, social influence, and facilitating conditions.
In this context, although auditors have positive perceptions .
igh performance and effort expectanc.
, feel supported by colleagues .
ocial influenc.
, and have access to the necessary resources .
acilitating condition.
, the use of Excel remains inadequate for detecting more sophisticated fraud.
This is due to ExcelAos limitations in analyzing highly complex data or detecting subtler fraud patterns, which often require more specialized and advanced audit tools.
Furthermore.
UTAUT helps explain that, although auditors perceive benefits and ease of use with Excel, and there is social support and adequate conditions, these factors do not automatically translate into increased effectiveness in fraud detection.
This indicates that, beyond technology adoption, the quality and suitability of the audit tool for specific tasks such as fraud detection are also crucial.
These findings are consistent with the research conducted by Choirunnisa & Rufaedah .
and Kamal .
However, studies by Olasanmi .
Atmaja .
Fauzi dkk.
, .
, dan Samagaio & Diogo .
, which show that CAATs do not have a significant impact on Fraud Detection, present different results.
The study by Widuri & Gautama .
, using a qualitative approach, also contrasts with this, indicating that the implementation of CAATs plays a crucial role in Fraud Detection.
p-ISSN:1411-6510 e-ISSN :2541-6111 JURNAL Riset Akuntansi dan Keuangan Indonesia H1c: Task Specific Knowledge has a significant influence on Fraud Detection Based on the data analysis, it is confirmed that Task Specific Knowledge has a significant influence on Fraud Detection.
This is attributed to two main factors: first, auditors with specialized knowledge in audit tasks tend to be more competent in identifying and analyzing signs of fraud.
Second, in-depth knowledge of audit procedures and fraud detection techniques allows auditors to more accurately evaluate and interpret suspicious data.
In the context of cognitive dissonance theory proposed by Festinger .
, to reduce this dissonance, individuals tend to seek consistency through changes in their attitudes, beliefs, or Auditors with specific knowledge of audit tasks have a strong knowledge base to detect When faced with information or data that is inconsistent with their understanding of how financial processes should occur, they experience cognitive dissonance.
To reduce this dissonance, auditors are more likely to delve deeper and conduct more thorough examinations to identify the source of these inconsistencies, ultimately enhancing their ability to detect fraud.
These findings support previous research by Yusrianti .
Betri & Kusumawaty .
Lembayung & Chomsatu .
, which showed that Task Specific Knowledge influences Fraud Detection by auditors.
This research is supported by studies by Johnson et al.
, .
Tirta & Sholihin .
Sari .
, dan Muzdalifah & Syamsu .
, and is consistent with cognitive dissonance theory.
H2a: Big Data Moderates the Influence of Auditor Religiosity on Fraud Detection Based on the analysis results, it is confirmed that Big Data does not moderate the influence of Auditor Religiosity on Fraud Detection.
This is attributed to two main factors: first, although Big Data offers significant potential to enhance data analysis capabilities, its effective use in fraud detection requires specialized tools, skills, and deep understanding that are not possessed by all auditors.
Second, religiosity factors are more intrinsic and related to the personal values of auditors, which may not be directly influenced by sophisticated data analysis tools like Big Data.
Attribution theory by Heider .
and UTAUT by Venkatesh et al.
can help explain why Big Data does not moderate the influence of auditor religiosity on Vol.
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3 Desember 2024 fraud detection.
Religiosity is a dispositional factor that influences the personal values and ethics of auditors, while Big Data is a technical tool.
Although Big Data can enhance data analysis capabilities, the religious values of auditors tend to influence how they perceive and react to fraud situations more than just the tools they use.
Therefore, while Big Data can assist in fraud detection, the influence of auditor religiosity on fraud detection is more intrinsic and not significantly moderated by the use of Big Data.
H2b: Auditor Religiosity Moderates the Influence of CAATs on Fraud Detection Based on the analysis results, it is confirmed that Big Data moderates and strengthens the influence of Computer Assisted Audit Techniques (CAAT.
on Fraud Detection.
This is attributed to two main factors: first.
Big Data provides volume, variety, velocity, veracity, and value of data, enabling CAATs to analyze data on a much larger and more complex scale.
Second, the integration of Big Data with CAATs allows auditors to detect patterns and anomalies that may not be visible with traditional audit techniques or smaller datasets.
UTAUT by Venkatesh et al.
, .
can explain the relationship between these variables.
With the existence of Big Data, auditors can conduct data analysis on a larger scale and at a faster pace.
This enables CAATs to identify more complex and hidden fraud patterns that may not be detected with smaller datasets.
The integration of Big Data and CAATs also enables auditors to perform predictive analysis and identify potential risks before fraud occurs.
This enhances auditorsAo ability to proactively detect and prevent fraud.
Thus.
Big Data not only enhances the effectiveness of CAATs but also transforms auditorsAo approach from reactive to proactive in fraud detection.
H2c: Auditor Religiosity Moderates the Influence of Task Specific Knowledge on Fraud Detection Based on the analysis results, it is confirmed that Big Data does not moderate the influence of Task Specific Knowledge on Fraud Detection.
This is attributed to two main factors: first, although Big Data offers significant potential in data analysis, its effectiveness in detecting fraud largely depends on auditorsAo ability to utilize the data effectively.
Auditors with specific knowledge about audit tasks may not yet be fully skilled or experienced in using Betri.
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Second.
Task Specific Knowledge tends to focus more on technical skills and knowledge directly related to the audit process, which may not be fully enhanced solely by the presence of Big Data without adequate data analysis skills support.
By utilizing UTAUT by Venkatesh et al.
, .
and cognitive dissonance theory by Festinger .
as the conceptual framework, we can understand that although auditors possess strong Task Specific Knowledge, the success of Big Data utilization in fraud detection largely depends on how comfortable and competent they feel in using the technology.
Performance and effort expectations, social influence, as well as facilitating conditions all play crucial roles in determining the extent to which Big Data can influence fraud Discomfort or lack of skills in using Big Data may hinder the potential of this technology, even though auditors have adequate technical CONCLUSION Based on the data analysis, several key conclusions and practical recommendations regarding fraud detection in auditing emerge.
The study confirms that Auditor Religiosity significantly influences Fraud Detection.
Auditors with higher levels of religiosity are more likely to adhere to strong ethical standards, which enhances their ability to detect fraudulent activities.
This finding emphasizes the importance of integrating auditorsAo personal values and beliefs into their professional Organizations should thus foster an environment that supports and respects auditorsAo ethical standards and personal values.
Conversely.
Computer Assisted Audit Techniques (CAAT.
do not have a significant influence on Fraud Detection in this study.
Although CAATs are intended to improve audit efficiency Enhaching Fraud Detection.
p-ISSN:1411-6510 e-ISSN :2541-6111 and accuracy, their effectiveness in detecting fraud may be limited by factors such as data quality and auditorsAo proficiency with these tools.
It is essential for organizations to ensure that auditors receive comprehensive training in CAATs and that these techniques are properly implemented to maximize their potential benefits.
Task-Specific Knowledge, however, does significantly impact Fraud Detection.
Auditors with specialized knowledge in their audit tasks are better equipped to identify and analyze fraudulent This underscores the necessity for ongoing professional development and targeted training programs to keep auditors updated on the latest fraud detection techniques and best practices.
The role of Big Data in this context is nuanced.
The study finds that Big Data does not moderate the relationship between Auditor Religiosity and Fraud Detection or between Task-Specific Knowledge and Fraud Detection.
However.
Big Data does moderate the influence of CAATs on Fraud Detection.
This suggests that while Big Data itself may not directly enhance fraud detection, its interaction with CAATs can be beneficial.
Organizations should therefore focus on improving their capabilities to manage and analyze Big Data effectively, leveraging it to enhance the functionality of CAATs.
In conclusion, to enhance fraud detection capabilities, organizations should invest in programs to improve auditorsAo task-specific knowledge and ethical awareness.
Additionally, adopting and effectively integrating CAATs into the audit process, along with creating an ethical work environment that aligns with auditorsAo personal values, can significantly boost fraud detection Addressing these recommendations will help organizations overcome challenges associated with Big Data and optimize the use of auditorsAo attributes and technological tools in their fraud detection practices.
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3 Desember 2024
REFERENCE