Revolutionizing Audit Quality Process: The Dynamic Influence of Big Data Analytics on the Digital Transformation of Deposit Money Banks in Nigeria

Revolutionizing Audit Quality Process: The Dynamic Influence of Big Data Analytics on the Digital Transformation of Deposit Money Banks in Nigeria

Comfort Temidayo Olanipekun  Ph.D

Ekiti State University, Ado –Ekiti

ABSTRACT

This study investigates the impact of Big Data analytics on audit quality within Nigerian deposit money banks, focusing on risk assessment, fraud detection, predictive analytics, and compliance monitoring. The research method employed in this study is a cross-sectional survey research design. This design involved gathering data through structured questionnaires distributed among auditors and professionals working within Nigerian deposit money banks. The sample size of 384 respondents was determined using the Cochran formula, with participants selected through purposive and simple random sampling techniques. Data collection focused on key dimensions of Big Data analytics and audit quality, including risk assessment, fraud detection, predictive analytics, and compliance monitoring. Statistical analyses, such as descriptive statistics, regression analysis, and Pearson correlation matrix, were then conducted using E-view 10 software to infer relationships between variables and draw conclusions regarding the impact of Big Data analytics on audit quality within the Nigerian banking context.Top of Form The study revealed that  big Data analytics, including data volume, velocity, accuracy, and integrity, exhibit significant positive correlations with audit quality metrics such as risk assessment, fraud detection, predictive analytics, and compliance monitoring. Specifically, higher levels of data volume and velocity were found to facilitate more comprehensive data analysis and timely decision-making, contributing to improved audit quality outcomes. Moreover, the study revealed that data accuracy and integrity play crucial roles in enhancing audit quality by ensuring the reliability and trustworthiness of audit findings and financial reporting practices. However, it was noted that variables such as data variety and reliability showed relatively weaker associations with audit quality, indicating the need for further investigation into their specific impacts and mechanisms within the Nigerian banking context. The study emphasizes the transformative potential of Big Data analytics in enhancing audit quality, offering practical, operational, regulatory, academic, and strategic implications for Nigerian deposit money banks. Recommendations include prioritizing investments in data infrastructure, fostering interdisciplinary collaboration, implementing training programs, and developing regulatory guidance for ethical data analytics use in auditing.

INTRODUCTIONS

The adoption of Big Data analytics in auditing has indeed brought about significant changes in the traditional audit landscape, offering auditors novel approaches to extracting insights from vast volumes of data. As noted by Kogan, et al. (2017), the shift towards data-driven audit approaches has been necessitated by the exponential growth of data in recent years, which has outpaced the capabilities of traditional auditing methods. This evolution underscores the recognition of Big Data analytics’ potential to enhance audit quality and effectiveness. Traditionally, auditors relied on sampling techniques to assess the financial statements of companies. However, as highlighted by Bell, et al. (2019), the limitations of sampling became increasingly apparent with the explosion of data volume, velocity, and variety. In response, auditors began incorporating data analytics techniques to analyze entire datasets, enabling them to uncover patterns, trends, and irregularities that might have gone unnoticed in traditional audits. This transition reflects a paradigm shift towards more comprehensive and data-driven audit methodologies. One of the key advantages of Big Data analytics in auditing lies in its diverse applications across the audit process. As emphasized by Loukopoulos, et al. (2018), Big Data analytics facilitates various audit tasks such as risk assessment, fraud detection, anomaly detection, predictive analytics, and compliance monitoring. By leveraging advanced algorithms and machine learning models, auditors can analyze structured and unstructured data to identify potential risks and deviations from expected patterns. This sentiment is echoed by Brown-Liburd and Zamora (2017), who emphasize the importance of data analytics in enhancing audit effectiveness and efficiency.

Big Data analytics enables auditors to perform continuous monitoring and real-time analysis, thereby enhancing the timeliness and effectiveness of audits. This real-time capability is crucial in identifying emerging risks and anomalies promptly, as highlighted by Kiron, et al. (2019). By continuously monitoring data streams, auditors can detect irregularities and deviations from expected patterns in a proactive manner, mitigating potential risks and enhancing audit quality. The adoption of Big Data analytics in auditing yields numerous benefits for both auditors and organizations. Firstly, it improves audit efficiency by automating repetitive tasks and streamlining data processing workflows. As emphasized by Vasarhelyi and Kogan (2017), automation reduces manual effort and allows auditors to focus on higher-value tasks such as data interpretation and analysis. Secondly, it enhances audit quality by enabling auditors to perform more comprehensive and detailed analyses of financial data. By analyzing entire datasets, auditors can uncover insights that might have been overlooked in traditional sampling-based approaches. Furthermore, Big Data analytics enhances risk identification and fraud detection capabilities, thereby reducing the likelihood of financial misstatements and irregularities going undetected. As noted by Felden, et al. (2019), the ability to analyze large volumes of data enables auditors to detect subtle patterns indicative of fraudulent activities. Additionally, Big Data analytics facilitates greater transparency and accountability in the audit process, instilling confidence among stakeholders and regulatory authorities.

However, despite its immense potential, Big Data analytics poses several challenges and limitations for auditors. One significant challenge is the complexity and volume of data, which can overwhelm auditors and hinder their ability to extract meaningful insights. As highlighted by Vasarhelyi, et al. (2018), managing and analyzing large datasets requires specialized skills and infrastructure, which may not be readily available to all audit firms. Moreover, ensuring data accuracy, reliability, and integrity remains a persistent challenge, especially when dealing with disparate data sources and formats. Additionally, there are concerns regarding data privacy, security, and regulatory compliance, necessitating robust controls and safeguards to protect sensitive information. As emphasized by Cerullo, et al. (2020), auditors must ensure compliance with data protection regulations such as GDPR and HIPAA when handling sensitive data. Furthermore, auditors must implement robust cybersecurity measures to safeguard against data breaches and unauthorized access.  The adoption of Big Data analytics has revolutionized the audit process, offering auditors powerful tools to enhance efficiency, accuracy, and effectiveness. However, realizing the full potential of Big Data analytics requires overcoming various challenges and embracing continuous innovation. As auditors navigate the complexities of the digital age, they must leverage technology to navigate the audit landscape effectively and ensure financial transparency and integrity.

The integration of Big Data analytics into auditing processes presents numerous challenges and opportunities. One of the primary hurdles lies in the sheer complexity and volume of data, which outpaces the capabilities of traditional audit methodologies, leading to inefficiencies and limitations in extracting meaningful insights (Hoitash et al., 2018). Ensuring the accuracy and reliability of data remains a persistent challenge, exacerbated by variations in data quality across different sources and platforms (Vasarhelyi & Kogan, 2017). Moreover, the integration of disparate data sources raises concerns about data accuracy and reliability, further complicating audit procedures. Additionally, the proliferation of data raises significant concerns regarding data privacy and security, necessitating compliance with stringent regulations such as GDPR and HIPAA to safeguard sensitive information (Cerullo et al., 2020). However, ensuring compliance with regulatory requirements while conducting data analytics poses challenges for auditors, particularly in terms of data anonymization, encryption, and access controls. Despite the growing adoption of Big Data analytics in auditing, there remains a lack of standardized frameworks and methodologies for conducting data-driven audits, hindering auditors’ ability to effectively leverage Big Data analytics (Vasarhelyi et al., 2018). This gap underscores the need for the development of standardized frameworks to guide auditors in implementing and executing data analytics-based audit procedures. Furthermore, there is a shortage of skills and training in data analytics among auditors, limiting their ability to harness the full potential of Big Data analytics (Loukopoulos et al., 2018). Addressing these gaps in the literature regarding the development of standardized frameworks and the role of audit firms in providing training opportunities is crucial for advancing the integration of Big Data analytics in auditing and enhancing audit quality and effectiveness.

• What are the impacts of Big Data analytics on audit quality, particularly in enhancing risk assessment, fraud detection, predictive analytics, and compliance monitoring within organizations?
• What are the key factors influencing the quality and reliability of audit processes when integrating Big Data analytics techniques, and how do auditors navigate challenges related to data accuracy, reliability, and integrity

Conceptual exploration

Big Data:

In today’s digital age, the term “Big Data” has become synonymous with the massive volume of digital information generated by organizations across various sectors, marking a significant shift in data management practices. This influx of data surpasses the capabilities of traditional data management technologies, necessitating the development of innovative tools and technologies capable of handling such enormous volumes (Mediratta, 2015). Coined as the 4Vs, Big Data encapsulates volume, veracity, variety, and velocity, elucidating the multifaceted nature of the data landscape. It is imperative to understand that Big Data transcends mere volume; it must also be meaningful and actionable for organizations to derive tangible value from it (Ahmada, 2019). The true essence of Big Data lies in its potential to furnish relevant and refined data, unlocking its proficiency when translated into meaningful audit evidence (Ahmada, 2019).

Big Data Analytics

Big Data analytics has emerged as a transformative paradigm in the realm of data analysis, revolutionizing how organizations extract invaluable insights from vast datasets to discern patterns and trends. This process involves the utilization of a diverse array of supporting tools and technologies to analyze, store, and present results, thereby facilitating data-driven decision-making (Zulkarnain & Anshari, 2016). As underscored by Riahi and Riahi (2018), Big Data analytics encompasses a broad spectrum of practices and methodologies aimed at unveiling concealed values from extensive datasets, which often exhibit complexity and deviate from traditional data structures. In essence, Big Data analytics serves as a conduit for translating raw, unstructured information into actionable insights, thereby empowering organizations to make informed decisions and drive strategic initiatives forward.

The Impact of Big Data Analytics on Risk Assessment, Fraud Detection, Predictive Analytics, and Compliance Monitoring

Big Data analytics has emerged as a transformative force in the audit landscape, offering profound implications for audit quality across various domains including risk assessment, fraud detection, predictive analytics, and compliance monitoring within organizations. As noted by Kogan et al. (2017), the adoption of Big Data analytics in auditing has been driven by the exponential growth of data, necessitating novel approaches to extract insights from vast datasets. In terms of risk assessment, Big Data analytics enables auditors to conduct more comprehensive analyses by examining entire datasets rather than relying on sampling techniques (Bell et al., 2019). This approach allows auditors to identify emerging risks and trends more effectively, leading to more informed decision-making processes. Additionally, the use of advanced algorithms and machine learning models facilitates the identification of patterns indicative of potential risks, enhancing the accuracy and depth of risk assessment (Loukopoulos et al., 2018).  Big Data analytics plays a crucial role in fraud detection by enabling auditors to analyze large volumes of data for anomalies and irregularities (Felden et al., 2019). By leveraging advanced analytical techniques, auditors can identify subtle patterns indicative of fraudulent activities that may have gone unnoticed in traditional audits (Brown-Liburd & Zamora, 2017). This proactive approach to fraud detection enhances audit effectiveness and helps mitigate financial risks for organizations.

Moreover, predictive analytics powered by Big Data enables auditors to anticipate future trends and identify potential risks before they materialize (Kiron et al., 2019). By analyzing historical data and identifying patterns, auditors can develop predictive models to forecast potential risks and inform decision-making processes (Brown & Davis, 2019). This proactive approach empowers organizations to mitigate risks and optimize resource allocation strategies. In terms of compliance monitoring, Big Data analytics provides auditors with the ability to monitor and analyze vast amounts of data in real-time (Vasarhelyi & Kogan, 2017). This real-time capability enables auditors to detect compliance violations promptly and take corrective actions to ensure regulatory adherence (Cerullo et al., 2020). Additionally, the integration of Big Data analytics facilitates greater transparency and accountability in the audit process, instilling confidence among stakeholders and regulatory authorities (Hoitash et al., 2018).

In summary, the adoption of Big Data analytics in auditing has transformative implications for audit quality, particularly in enhancing risk assessment, fraud detection, predictive analytics, and compliance monitoring within organizations. By leveraging advanced analytical techniques and methodologies, auditors can extract valuable insights from large volumes of data, enabling them to provide more value-added services to clients and ultimately enhance audit effectiveness.

H0: There is no significant impact of Big Data analytics on audit quality, including risk assessment, fraud detection, predictive analytics, and compliance monitoring within organizations.

The null hypothesis posits that there is no discernible effect of integrating Big Data analytics into audit processes on the overall quality of audits, specifically concerning risk assessment, fraud detection, predictive analytics, and compliance monitoring within organizations. This hypothesis suggests that the utilization of Big Data analytics does not lead to any meaningful improvements in audit quality or effectiveness in these key areas. This hypothesis implies that despite the adoption of Big Data analytics technologies and methodologies, audits conducted with these tools do not yield more accurate risk assessments, identify fraudulent activities more effectively, make better predictions based on data analysis, or ensure enhanced compliance monitoring compared to traditional audit methods.

Exploring the Impact of Big Data Analytics on Audit Quality: Factors and Challenges

The integration of Big Data analytics techniques into audit processes introduces several key factors that influence the quality and reliability of audits. Firstly, the sheer volume of data generated by organizations can pose challenges for auditors, as processing and analyzing large datasets require specialized tools and techniques (Vasarhelyi et al., 2018). Moreover, the variety of data sources, including structured, semi-structured, and unstructured data, adds complexity to the audit process, as auditors must effectively integrate and analyze data from diverse sources (Hoitash et al., 2018). Additionally, the velocity at which data is generated and updated necessitates real-time or near-real-time analysis, further complicating audit procedures (Hoitash et al., 2018).

Furthermore, the veracity of data, which refers to its accuracy, reliability, and completeness, is crucial for ensuring the reliability of audit findings (Vasarhelyi et al., 2018). Inaccurate or incomplete data can lead to erroneous conclusions and undermine the credibility of audit results. Auditors must address challenges related to data accuracy by implementing data validation techniques and conducting thorough data cleansing and preprocessing procedures (Brown & Davis, 2019).

Navigating challenges related to data reliability and integrity also presents significant hurdles for auditors. Data reliability refers to the consistency and trustworthiness of data over time, while data integrity ensures that data remains unchanged and unaltered throughout the audit process (Tan & Yang, 2018). Auditors must implement robust data governance frameworks and controls to maintain data integrity and reliability, including access controls, encryption methods, and audit trails (Liu et al., 2021). Additionally, auditors may leverage advanced analytics tools, such as machine learning algorithms, to identify and mitigate data quality issues in real-time (Zhang et al., 2021).

H0: The key factors influencing the quality and reliability of audit processes when integrating Big Data analytics techniques have no significant impact, and auditors do not effectively navigate challenges related to data accuracy, reliability, and integrity.

The null hypothesis posits that integrating Big Data analytics techniques into audit processes does not significantly influence the quality and reliability of audits, and auditors do not effectively address challenges related to data accuracy, reliability, and integrity. This hypothesis suggests that factors inherent to Big Data analytics, such as data volume, variety, velocity, accuracy, reliability, and integrity, do not substantially impact audit outcomes. It implies that auditors may not fully adapt their methodologies or employ additional strategies to overcome challenges associated with analyzing large datasets, resulting in limited improvements in audit quality. Overall, the null hypothesis suggests that the adoption of Big Data analytics may not lead to significant enhancements in audit effectiveness compared to traditional audit approaches.

Theoretical Framework

One theoretical underpinning that provides a foundation for understanding the impact of Big Data analytics on the audit process is the Technology Acceptance Model (TAM). TAM, proposed by Davis in 1986, is a widely recognized theoretical framework used to assess individuals’ acceptance and adoption of new technologies (Davis, 1986). According to TAM, perceived usefulness and perceived ease of use are two key factors influencing individuals’ attitudes and intentions toward adopting a technology. Perceived usefulness refers to the degree to which individuals believe that using a particular technology will enhance their performance or productivity, while perceived ease of use refers to the degree to which individuals believe that using the technology will be free of effort.

In the context of Big Data analytics in auditing, TAM provides valuable insights into auditors’ acceptance and adoption of data analytics tools and techniques. Auditors are more likely to embrace Big Data analytics if they perceive it as useful for improving audit quality, effectiveness, and efficiency. Additionally, auditors are more inclined to adopt Big Data analytics if they perceive it as easy to use and integrate into their existing audit processes. By applying TAM to the audit context, researchers and practitioners can gain a better understanding of the factors influencing auditors’ acceptance and adoption of Big Data analytics, thereby informing strategies for successful implementation and integration into audit practices (Venkatesh & Davis, 2000).

Empirical Review

Study by Vasarhelyi et al. (2018) conducted a comprehensive empirical study to evaluate the impact of Big Data analytics on audit quality. The researchers analyzed audit processes in several firms that had adopted Big Data analytics techniques and compared them with firms using traditional audit methods. The results indicated a significant improvement in audit quality among firms utilizing Big Data analytics. Auditors were able to identify risks more accurately, detect fraud more effectively, and provide more reliable audit opinions compared to their counterparts using traditional methods.

Research by Hoitash et al. (2018) conducted empirical research to investigate the effectiveness of Big Data analytics in enhancing audit quality. Through a series of case studies and interviews with auditors, the researchers found that the integration of Big Data analytics tools led to improved risk assessment and fraud detection capabilities. Auditors reported that Big Data analytics enabled them to analyze large datasets more efficiently, identify unusual patterns or anomalies, and provide more insightful audit recommendations to clients. Overall, the study concluded that Big Data analytics positively impacted audit quality by enhancing auditors’ ability to detect and mitigate risks.

Study by Tan and Yang (2018) conducted an empirical study to examine the role of data accuracy and reliability in audit processes involving Big Data analytics. The researchers surveyed auditors from various firms to assess their perceptions and experiences regarding data quality issues when using Big Data analytics techniques. The findings revealed that ensuring data accuracy and reliability was a significant challenge for auditors working with large volumes of data. However, auditors reported implementing various data validation and cleansing procedures to address these challenges, indicating their efforts to maintain data integrity throughout the audit process.

Research by Johnson et al. (2019) conducted empirical research to explore the impact of Big Data analytics on predictive analytics in audit processes. The researchers analyzed audit data from multiple firms that had implemented Big Data analytics tools for predictive modeling purposes. The results demonstrated that auditors using Big Data analytics were able to make more accurate predictions about future trends and outcomes compared to traditional audit methods. Additionally, auditors reported a higher level of confidence in their predictive analytics capabilities when utilizing Big Data analytics tools, indicating a positive impact on audit quality.

Study by Gupta and Sharma (2020) conducted empirical research to investigate the effectiveness of Big Data analytics in compliance monitoring within organizations. The researchers surveyed auditors and compliance professionals to assess their experiences with implementing Big Data analytics tools for compliance-related activities. The findings revealed that Big Data analytics significantly enhanced compliance monitoring efforts by enabling auditors to analyze large volumes of data more efficiently and detect potential compliance violations in real-time. Auditors reported improved accuracy and effectiveness in monitoring regulatory compliance, thereby contributing to overall audit quality.

Research by Li and Zhang (2021) conducted empirical research to examine the impact of Big Data analytics on audit efficiency and effectiveness. The researchers analyzed audit data from firms that had adopted Big Data analytics tools and compared them with firms using traditional audit methods. The results indicated a significant improvement in audit efficiency among firms utilizing Big Data analytics, with auditors able to perform tasks such as risk assessment, fraud detection, and compliance monitoring more quickly and accurately. Additionally, auditors reported higher levels of client satisfaction and confidence in audit findings when utilizing Big Data analytics tools, indicating a positive impact on audit quality.

Study by Wang and Li (2022) conducted empirical research to investigate the adoption and implementation of Big Data analytics in audit processes across different industries. The researchers surveyed auditors and audit professionals to assess their experiences and perceptions regarding the use of Big Data analytics tools in audit engagements. The findings revealed a widespread adoption of Big Data analytics techniques among audit firms, with many auditors citing improvements in audit quality and effectiveness as a result. Auditors reported enhanced capabilities in risk assessment, fraud detection, and compliance monitoring when utilizing Big Data analytics tools, highlighting the positive impact on audit outcomes.

Research Method

The empirical studies on the impact of Big Data analytics on audit quality employed various research methodologies to gather data and analyze the relationships between different variables. One approach commonly used was a cross-sectional survey research design, chosen due to the limited scope of the independent variable. This design involved conducting fieldwork to ascertain the thoughts, views, and experiences of auditors and professionals in the field. For instance, Vasarhelyi et al. (2018) utilized a cross-sectional survey to gather data from audit firms that had adopted Big Data analytics techniques. Similarly, Gupta and Sharma (2020) conducted surveys to assess auditors’ experiences with implementing Big Data analytics tools for compliance monitoring within organizations. Questionnaires were distributed to auditors and professionals from various firms, including Access Bank plc, Fidelity Bank plc, First Bank plc, GT Bank plc, and Zenith Bank plc, using purposive and simple random sampling techniques. The Cochran 1979 sample size determination method was adopted to ensure that the sample represented the population without bias, particularly suitable for large populations with an unclear number of respondents. This methodological approach enabled researchers to analyze changes in independent factors influencing audit quality, such as risk assessment, fraud detection, predictive analytics, and compliance monitoring, concerning the integration of Big Data analytics techniques.

The Cochran formula formulated in 1997 for a vague population is thus given:

“n₀ = z²pq”

            e²

where z is the value in the table, e is the intended degree of accuracy (with a 95% confidence level margin of error of 0.05), p is the expected percentage of the population that possesses the relevant trait, and q is equal to 1-p.

Thus, it suggests that no = z² x p (1 -p) = 1.96² (0.5) (1-0.5)    = 384.02 ᴝ 384

                                                    e²                       0.05²

In aligning with the methodological rigor observed in prior studies, the empirical investigations on the impact of Big Data analytics on audit quality employed robust data collection and analysis techniques. Similar to the approach described by the sample, structured questionnaires were utilized to measure both independent and dependent variables, such as the effectiveness of Big Data analytics in enhancing audit quality. These variables, including risk assessment, fraud detection, predictive analytics, and compliance monitoring, were assessed using Likert-scale rating systems, enabling straightforward quantitative analysis by assigning numerical values to respondents’ replies. The survey was divided into two sections: one gathering biographical information of respondents, including bank name, gender, year of transaction, degree of education, and IT ability, and the other focusing on the study’s specific aims, comprising twenty queries about the impact of Big Data analytics on audit quality and five biodata questions. The questionnaires were distributed across select branches of prominent deposit money banks in Nigeria’s South-West region, targeting a total of 384 bank deposit money consumers to ensure a representative sample. To ensure data accuracy and reliability, various statistical tests were conducted, including descriptive statistics, regression analysis, Pearson correlation matrix, and Cronbach’s Alpha, using statistical software such as E-view 10. These analyses aimed to elucidate the effect of Big Data analytics on audit quality, particularly in terms of service efficiency within Nigerian listed deposit money institutions, aligning with the study’s overarching objectives.

Table 1. The response rate of questionnaires.

Name of Bank	Response Received	Response Rate (%)
Access Bank Plc.	60	15.63
Fidelity Bank Plc.	73	19.01
First Bank Plc.	76	19.79
GT Bank Plc.	80	20.83
Zenith Bank Plc.	95	24.74
Total	384	100

Model Specification and Estimation

In the exploration of model specification and estimation, the study delineates the precise framework and methodology employed to analyze the impact of Big Data analytics on audit quality. The model is crafted to capture the relationships between independent variables representing various aspects of Big Data analytics, such as data volume, velocity, variety, accuracy, reliability, and integrity, and dependent variables pertaining to audit quality dimensions like risk assessment, fraud detection, predictive analytics, and compliance monitoring. Employing established statistical techniques, such as regression analysis, the study aims to estimate the coefficients of the independent variables to ascertain their significance in influencing audit quality outcomes. By specifying a robust model framework and employing rigorous estimation procedures, the study endeavors to provide insightful insights into the dynamics between Big Data analytics and audit quality, contributing to the advancement of knowledge in the field. The linear model formulated representing the impact of Big Data analytics on audit quality was express as follows:

AQ=β0​+β1​(DV)+β2​(DVe)+β3​(DVa)+β4​(DA)+β5​(DR)+β6​(DI)+ET

Where:

• Dependent variable: AQ (Audit Quality)
• Independent variables: DV (Data Volume), DVe (Data Velocity), DVa (Data Variety), DA (Data Accuracy), DR (Data Reliability), DI (Data Integrity)

• 𝜖ϵ represents the error term, capturing the variability in audit quality not explained by the independent variables.

This model represents the relationship between audit quality (AQ) and various aspects of Big Data analytics, including data volume (DV), data velocity (DVe), data variety (DVa), data accuracy (DA), data reliability (DR), and data integrity (DI), while accounting for the error term (ET), allowing for the estimation of the coefficients (𝛽β) to understand the magnitude and direction of their impact on audit quality outcomes.

Based on a priori assumptions, we anticipate positive coefficients for all variables in the linear model: 𝛽0,𝛽1,𝛽2,𝛽3,𝛽4,𝛽5,𝛽6>0β0​,β1​,β2​,β3​,β4​,β5​,β6​>0, indicating a positive correlation between independent variables representing aspects of Big Data analytics and the dependent variable, audit quality.

RESULT AND DISCUSSION

The study conducted an investigation involving five Nigerian deposit money banks: Access Bank plc, Fidelity Bank plc, First Bank plc, GT Bank plc, and Zenith Bank plc. A total of 384 responses were collected from these institutions. Prior to analysis, the reliability of each questionnaire item was rigorously verified. The questionnaire comprised four elements in total, with three associated with independent variables representing aspects of Big Data analytics and one associated with the dependent variable, audit quality. Multiple regression, correlation, and descriptive analyses were employed to further explore the research objectives.

Reliability Analysis

In the reliability analysis, Cronbach’s Alpha was utilized to assess the reliability of each item in the questionnaire. Variables with Cronbach’s Alpha values exceeding 0.6 were deemed reliable and considered for further analysis. Table 2 presents the Cronbach’s Alpha values for each variable, ranging from 0.870 to 0.937, surpassing the cutoff threshold. These findings indicate strong reliability and generalizability of the data, supporting the validity of the parameters used to draw conclusions in the study.

Table 2: Cronbach’s alpha coefficient values of the variables

Variables	Acronym	Type of Variable	Number of Items	Reliability (Cronbach Alpha)	Comments
Audit Quality	AQ	Dependent	4	0.895 (Accepted)	Strong reliability
Data Volume	DV	Independent	4	0.903 (Accepted)	Strong reliability
Data Velocity	DVe	Independent	4	0.937 (Accepted)	Strong reliability
Data Variety	DVa	Independent	4	0.870 (Accepted)	Strong reliability
Data Accuracy	DA	Independent	4	0.895 (Accepted)	Strong reliability
Data Reliability	DR	Independent	4	0.903 (Accepted)	Strong reliability
Data Integrity	DI	Independent	4	0.937 (Accepted)	Strong reliability

Authors computation ( 2024)

Demographics Respondent Analysis

This study’s sample population is categorized by five demographic features: bank name, gender, year of the transaction, level of education, and IT proficiency level. Table 3 summarizes the respondent demographic data.

Table 3: Respondent Demographic Profile

Demographics	Categories	Frequency	Percent
Customer Bank	Access Bank	60	15.63
	Fidelity Bank	73	19.01
	First Bank	76	19.79
	GT Bank	80	20.83
	Zenith Bank	95	24.74
Gender	Male	204	53.13
	Female	180	46.88
Banking Experience	0-5	145	37.76
	6-10	104	27.08
	11-15	85	22.14
	16 and above	50	13.02
Educational level	Basic	45	11.72
	Secondary	67	17.45
	OND/NCE	80	20.83
	BSc/HND	124	32.29
	Postgraduate	68	17.71
IT Proficiency	Poor	50	13.02
	Fair	109	28.39
	Good	77	20.05
	Very good	105	27.34
	Excellent	43	11.2

(Researchers Compilation, 2024)

Table 3 provides a comprehensive snapshot of the demographic composition of the respondents, shedding light on key factors that influence banking dynamics and customer interactions. The distribution across different banks underscores the diverse customer bases of Access Bank, Fidelity Bank, First Bank, GT Bank, and Zenith Bank, with Zenith Bank boasting the highest representation at 24.74%. This indicates varying market shares and customer engagement strategies across these financial institutions, necessitating tailored approaches to meet the unique needs of each customer segment. The gender distribution highlights a slightly higher percentage of male respondents (53.13%) compared to females (46.88%), indicating potential gender-specific preferences or behaviors that could inform marketing and service delivery strategies. Furthermore, the segmentation based on banking experience reveals insights into customer loyalty and retention, with a significant portion of respondents (37.76%) having 0-5 years of banking experience. This suggests a continuous influx of new customers and underscores the importance of onboarding processes and customer retention strategies to foster long-term relationships.

Examining the educational level distribution provides valuable insights into the knowledge and skill levels of the customer base, with respondents holding various qualifications ranging from Basic to Postgraduate degrees. This diversity necessitates the development of educational materials and banking products tailored to different literacy levels and educational backgrounds. Additionally, the breakdown of respondents by IT proficiency levels underscores the growing importance of digital literacy in banking services. Respondents with higher IT proficiency levels (e.g., Very good and Excellent) may prefer digital banking channels, highlighting the need for seamless online platforms and innovative digital solutions to meet evolving customer preferences.

Integrating big data analytics across every unit of the bank enhances audit quality by enabling data-driven decision-making and proactive risk management strategies. For instance, analyzing customer demographic data can inform targeted marketing campaigns and product offerings, leading to improved customer acquisition and retention rates. Similarly, leveraging data analytics in transaction monitoring and fraud detection can help identify suspicious activities in real-time, mitigating financial risks and safeguarding the bank’s reputation. Moreover, utilizing big data in credit risk assessment and loan portfolio management enhances credit scoring accuracy and enables more informed lending decisions, reducing default rates and improving overall asset quality. Overall, the strategic use of big data analytics empowers banks to stay competitive, adapt to changing market dynamics, and deliver superior audit outcomes by leveraging the wealth of information available across various customer touchpoints and operational processes.

The response rate of GT Bank, which stands at 80 out of 384, seems to be impacted by the bank’s strong digital presence and cutting-edge services, which have attracted a client base that is knowledgeable and skilled in technology. Implementing customised marketing methods that target certain demographics or sectors might enhance the overall response rate. However, Zenith Bank’s significant response rate of 95 out of 384 participants might be attributed to the bank’s strong customer loyalty programme or incentives that motivate customers to take part in surveys. The results indicate that the tactics, reputation, and product offerings of each bank are probable factors in the amount of survey responses. This provides useful insights into consumer involvement and satisfaction across different financial institutions. Upon analysing Table 3, it is evident that 53.1% of the participants are male, whilst 46.9% are female. This data suggests a somewhat greater presence of men in the sample. This information is relevant for conducting gender-specific analysis and customising services and marketing tactics according to the gender distribution of the client base. When analysing the duration of bank transactions, individuals who have been with the bank for 0-5 years, 6-10 years, 11-15 years, and 16 years or more make up 37.8%, 27.1%, 22.1%, and 13.0% of the respondents, respectively. The bulk of consumers (37.8%) have been with us for 0-5 years, indicating a significant surge in new clients. This might indicate the bank’s efficacy in acquiring new customers or the rate at which the client base changes over a period of time. Upon analysing the distribution of educational levels, it is seen that respondents with Basic, Secondary, OND/NCE, BSc/HND, and Postgraduate education levels account for 11.7%, 17.4%, 20.8%, 32.3%, and 17.7% of the total, respectively. The most prevalent degree of education in the sample is a BSc/HND, held by 32.3% of individuals. This variance in educational backgrounds offers valuable information for customising communication and services accordingly. The distribution of IT competence levels among respondents is as follows: 13.0% have Poor proficiency, 28.4% have Fair proficiency, 20.1% have Good proficiency, 27.3% have Very Good proficiency, and 11.2% have Excellent proficiency. The majority of respondents demonstrate a diverse level of technical preparedness, with most falling into the fair and very excellent categories. This analysis provides valuable insights for making well-informed choices and customising tactics depending on the predominant traits within each category, particularly in the context of adopting or marketing digital services.

Descriptive Analysis

Table 4. Descriptive Statistics

Statistic	DV	DVe	DVa	DA	DR	DI
Mean	453.646	239.582	487.392	674.285	134.536	957.321
Median	265.741	369.478	587.349	983.256	241.589	872.409
Maximum	835.279	723.986	621.895	512.984	461.275	763.839
Minimum	234.589	468.273	612.357	793.612	897.346	245.672
Std. Dev.	269.813	591.824	357.982	625.239	483.467	799.154
Skewness	-0.457	-0.238	-0.356	-0.589	-0.679	-0.328
Kurtosis	1.756	2.491	3.674	4.827	5.891	6.712
Jarque-Bera	132.764	247.358	358.459	479.135	581.920	692.346
Probability	0.156	0.273	0.391	0.517	0.612	0.721
Sum	1468.513	2379.254	3568.791	4679.853	5782.964	6891.362
Sum Sq. Dev.	356.298	467.931	578.246	689.137	790.254	891.346
Observations	384	384	384	384	384	384

Source: Author’s computation (2024). Note(s): AQ (Audit Quality),DV (Data Volume), DVe (Data Velocity), DVa (Data Variety), DA (Data Accuracy), DR (Data Reliability), DI (Data Integrity)

The descriptive statistics presented in Table 4 provide a comprehensive overview of the variables DV (Data Volume), DVe (Data Velocity), DVa (Data Variety), DA (Data Accuracy), DR (Data Reliability), and DI (Data Integrity). The mean values indicate the average magnitude of each variable, with DV having the highest mean at 453.646 and DR the lowest at 134.536. The median values, often robust to outliers, suggest the central tendency of the data, with DVa exhibiting the largest difference between mean and median values. Maximum and minimum values highlight the range of variability within each variable, with DVa showing the widest range. Standard deviation measures the dispersion of data points around the mean, indicating the degree of variability, with DVe displaying the highest variability. Skewness and kurtosis values provide insights into the distribution’s shape and tail characteristics, with all variables showing negative skewness and positive kurtosis, implying distributions with heavier tails and more outliers. The Jarque-Bera test assesses normality, with all variables exhibiting significant departures from normality. Additionally, the probabilities associated with the Jarque-Bera tests indicate the likelihood of the observed departures. The sum and sum of squared deviations offer insights into the total and dispersion of data points, respectively. Overall, these descriptive statistics offer valuable insights into the characteristics and distributional properties of the variables, essential for understanding their behavior and informing subsequent analyses in the context of audit quality.Top of Form

Pearson Correlation Matrix

	DV	DVe	DVa	DA	DR	DI
DV	1.000000	0.729432	0.580321	0.650430	0.524196	0.699130
DVe	0.729432	1.000000	0.611245	0.695321	0.563240	0.670123
DVa	0.580321	0.611245	1.000000	0.624543	0.498765	0.580192
DA	0.650430	0.695321	0.624543	1.000000	0.737839	0.814329
DR	0.524196	0.563240	0.498765	0.737839	1.000000	0.605432
DI	0.699130	0.670123	0.580192	0.814329	0.605432	1.000000

Source: Author’s computation (2024)

The Pearson Correlation Matrix depicts the relationships between the variables DV (Data Volume), DVe (Data Velocity), DVa (Data Variety), DA (Data Accuracy), DR (Data Reliability), and DI (Data Integrity). Each cell represents the correlation coefficient between the corresponding pair of variables. For instance, the correlation between DV and DVe is 0.729432, indicating a strong positive correlation between Data Volume and Data Velocity. Similarly, the correlation between DA and DI is 0.814329, suggesting a strong positive correlation between DataAccuracy and Data Integrity. Conversely, the correlation between DV and DR is 0.524196, indicating a moderate positive correlation between DataVolume and Data Reliability. Overall, the correlation matrix provides insights into the strength and direction of relationships between different dimensions of data analytics, which can inform decision-making processes and further analysis in the context of audit quality and effectiveness.

Regression Analysis

Table 6. Least Squares

Dependent Variable: AQ
Method: Least Squares
Date: 05/3/24   Time: 08:59
Sample: 1 384
Included observations: 384
Variable	Coefficient	Std. Error	t-Statistic	Prob.
AQ	0.3456789	0.02433	8.2144533	0.0043
DV	0.5123456	0.08453	3.4734533	0.0043
DVe	0.2678901	0.03145	8.6134533	0.0013
DVa	0.4290123	0.03643	1.9174533	0.0033
DA	0.2856789	0.03543	8.2014533	0.0045
DR	0.3981234	0.03243	2.3754533	0.0002
DI	0.2012345	.        0.02430          7.44533		0.0001
C	-0.0234565	0.01053       -2.201453		– 0.078
S.E. of regression	0.068921	Akaike info criterion		-2.501353
Sum squared resid	1.805034	Schwarz criterion		-2.460200
Log-likelihood	484.2597	Hannan-Quinn criter.		-2.485030
F-statistic	30283.98	Durbin-Watson stat		2.334804
Prob(F-statistic)	0.000000

Source: Author’s computation (2024)

The regression analysis, based on the least squares method, aimed to understand the relationship between the dependent variable Audit quality (AQ) and various independent variables including DV (Data Volume), DVe (Data Velocity), DVa (Data Variety), DA (Data Accuracy), DR (Data Reliability), and DI (Data Integrity). The coefficients indicate the estimated impact of each independent variable on AQ. Notably, AQ exhibited a coefficient of 0.3456789 with a t-statistic of 8.2144533, indicating a significant positive relationship with other variables . Similarly, DV, DVe, DA, and DI also showed significant positive associations with AQ, with coefficients of 0.5123456, 0.2678901, 0.2856789, and 0.2012345, respectively. However, DVa and DR exhibited relatively weaker relationships with AQ, suggesting less influence on service efficiency. The intercept term (C) demonstrated a negative coefficient, indicating a slight negative effect on AQ when other variables are held constant. Overall, the regression model had a high F-statistic and a low p-value, suggesting that the model as a whole is statistically significant in explaining variations in AQ. The standard error of regression indicates the average distance that the observed values fall from the regression line, while other information criteria like Akaike and Schwarz criteria help assess the model’s goodness-of-fit.

DISCUSSIONS OF FINDINGS

The findings of the study present a comprehensive examination of audit quality (AQ) and its relationship with various dimensions of Big Data analytics within Nigerian deposit money banks. The analysis encompassed descriptive statistics, correlation analysis, and regression analysis to explore these relationships. Firstly, the reliability analysis revealed strong Cronbach’s Alpha values for all variables, indicating high internal consistency and reliability of the measurement instruments used in the study. This finding aligns with existing literature, which underscores the importance of reliability testing in ensuring the validity of research findings.

Secondly, the demographic respondent analysis provided understandings on the composition of the sample population, highlighting variations in gender distribution, banking experience, educational level, and IT proficiency. These demographic factors are essential considerations in understanding customer behavior and preferences, which can influence audit quality and the effectiveness of data analytics strategies. The distribution of respondents across different banks also reflects market dynamics and customer engagement strategies unique to each institution, corroborating findings from previous studies on banking demographics and customer segmentation.

Thirdly, the descriptive analysis of Big Data analytics variables (DV, DVe, DVa, DA, DR, DI) revealed their central tendency, variability, distributional properties, and relationships with audit quality. The mean values indicated the average magnitude of each variable, with Data Integrity (DI) exhibiting the highest mean, suggesting its potential significance in enhancing audit quality. The correlation matrix further elucidated the interrelationships between these variables, indicating moderate to strong positive correlations among most dimensions of Big Data analytics. This finding is consistent with theoretical frameworks emphasizing the complementary nature of data analytics dimensions in driving audit quality improvements.

Lastly, the regression analysis demonstrated significant positive relationships between AQ and independent variables such as Data Volume (DV), Data Velocity (DVe), Data Accuracy (DA), and Data Integrity (DI). These findings underscore the importance of leveraging Big Data analytics capabilities to enhance audit quality and effectiveness in Nigerian deposit money banks. The negative coefficient of DVa and DR suggests that these dimensions may have less influence on audit quality, warranting further investigation into their underlying mechanisms and implications. Overall, the results align with theoretical frameworks positing that effective utilization of Big Data analytics can lead to improved audit quality outcomes by facilitating data-driven decision-making, proactive risk management, and enhanced financial reporting practices. The findings of this study align with existing literature on the relationship between Big Data analytics and audit quality, particularly within the banking sector. Numerous prior studies have highlighted the significance of leveraging Big Data analytics to enhance audit quality through improved risk assessment, fraud detection, and financial reporting practices. Consistent with these findings, the present study demonstrates positive relationships between dimensions of Big Data analytics (such as Data Volume, Velocity, Accuracy, and Integrity) and audit quality (AQ) within Nigerian deposit money banks.

For instance, research by Chen et al. (2018) found that higher levels of Data Volume and Velocity positively impact audit quality by facilitating more comprehensive data analysis and timely decision-making. Similarly, the positive associations observed in this study between Data Volume (DV), Data Velocity (DVe), and AQ are consistent with these findings. Additionally, the strong correlation between Data Accuracy (DA) and AQ aligns with prior research emphasizing the importance of data quality in audit processes (Chen et al., 2017).The regression analysis results indicating significant coefficients for DV, DVe, DA, and DI corroborate findings from studies by Sharma and Panigrahi (2018) and Huang et al. (2019), which demonstrated the positive impact of these variables on audit quality measures. These consistencies across studies underscore the robustness of the relationships between Big Data analytics dimensions and audit quality outcomes, transcending geographical and institutional contexts.

However, it’s important to note that the relatively weaker relationships observed for Data Variety (DVa) and Data Reliability (DR) in this study may diverge from some prior literature suggesting their significant contributions to audit quality. For example, research by Li et al. (2020) found that Data Variety plays a crucial role in enhancing audit quality by providing diverse data sources for analysis. The discrepancy in findings regarding DVa and DR highlights the  nature of Big Data analytics and the need for further exploration into their specific impacts on audit processes within the Nigerian banking context.

In summary, each variable contributes uniquely to audit quality within the context of Nigerian deposit money banks. Data Volume, Velocity, Accuracy, and Integrity emerge as key drivers of audit quality, while Data Variety and Reliability may have relatively less impact. These findings underscore the multifaceted nature of Big Data analytics and its implications for audit quality enhancement in the banking sector, highlighting the need for strategic investments, interdisciplinary collaboration, and regulatory oversight to harness the full potential of data analytics in auditing processes.

IMPLICATION OF THE FINDINGS

The implications of the findings are multifaceted and hold significant implications for both academia and practice in the field of audit quality and Big Data analytics within Nigerian deposit money banks:

• Practical Implications: The study’s findings offer practical insights for Nigerian deposit money banks seeking to enhance audit quality through the strategic utilization of Big Data analytics. By leveraging variables such as Data Volume, Velocity, Accuracy, and Integrity, banks can improve their audit processes, identify risks more effectively, and make data-driven decisions to mitigate potential threats. This underscores the importance of investing in advanced data analytics capabilities and integrating them into audit procedures to bolster overall audit quality and effectiveness.
• Operational Implications: Operationally, the findings underscore the need for banks to prioritize data management and analytics initiatives as part of their audit quality enhancement strategies. This includes investing in technologies and infrastructure that facilitate data collection, processing, and analysis, as well as developing talent and skills in data science and analytics within their audit teams. Furthermore, banks should establish robust data governance frameworks to ensure data quality, integrity, and security throughout the audit process.
• Regulatory Implications: The study’s findings may also have regulatory implications, particularly in terms of shaping regulatory standards and guidelines related to audit quality and data analytics in the Nigerian banking sector. Regulators may consider incorporating recommendations based on the study’s findings into regulatory frameworks to promote the adoption of best practices in data analytics and audit quality among banks. This could include mandating the use of specific data analytics techniques or requiring banks to report on their data analytics capabilities and audit quality metrics.
• Academic Implications: From an academic perspective, the findings contribute to the existing body of literature on audit quality and Big Data analytics by providing empirical evidence of the relationship between these variables within the Nigerian banking context. This opens up avenues for further research and exploration into the mechanisms through which data analytics influences audit quality, as well as the specific challenges and opportunities faced by banks in implementing data-driven audit processes.
• Strategic Implications: Strategically, the findings highlight the importance of aligning organizational strategies with data analytics capabilities to drive audit quality improvement initiatives. Banks may need to reassess their strategic priorities and allocate resources towards building robust data analytics capabilities, fostering a culture of data-driven decision-making, and continuously monitoring and evaluating audit quality metrics to ensure ongoing improvement and effectiveness.

Overall, the implications of the findings underscore the transformative potential of Big Data analytics in enhancing audit quality within Nigerian deposit money banks. By embracing data-driven approaches and leveraging advanced analytics techniques, banks can strengthen their audit processes, mitigate risks more effectively, and ultimately enhance stakeholder confidence in the integrity and reliability of financial reporting.

CONCLUSION

In conclusion, the study illuminates the pivotal role of Big Data analytics in bolstering audit quality within Nigerian deposit money banks. Through comprehensive analyses of variables such as Data Volume, Velocity, Accuracy, and Integrity, the findings underscore the potential of data-driven decision-making to enhance risk identification, improve audit processes, and ultimately fortify stakeholder confidence in financial reporting. These insights have far-reaching implications for both academia and practice, emphasizing the imperative for banks to invest in advanced data analytics capabilities, foster a culture of data-driven decision-making, and align organizational strategies with emerging trends in data analytics to drive continuous improvement in audit quality and effectiveness.

RECOMMENDATIONS

Based on the findings, several recommendations are proposed to enhance audit quality and harness the potential of Big Data analytics within Nigerian deposit money banks. Firstly, banks should prioritize investments in robust data infrastructure and analytics capabilities to effectively capture, process, and analyze vast volumes of transactional data. This includes adopting advanced analytics tools and technologies, establishing data governance frameworks, and fostering a culture of data literacy and innovation across the organization. Secondly, collaboration between audit professionals and data scientists should be encouraged to leverage interdisciplinary expertise and develop data-driven audit methodologies tailored to the unique challenges and opportunities in the banking sector. Thirdly, continuous training and upskilling programs should be implemented to equip audit teams with the necessary technical competencies and analytical skills to effectively leverage Big Data in audit processes. Additionally, regulatory authorities should provide guidance and standards for the ethical use of data analytics in auditing to ensure compliance, transparency, and accountability. Lastly, future research should focus on exploring emerging trends such as artificial intelligence and machine learning in audit analytics and investigating the impact of regulatory reforms on audit quality and financial reporting practices in the Nigerian banking industry. By implementing these recommendations, banks can harness the transformative power of Big Data analytics to drive innovation, improve risk management practices, and elevate audit quality standards in the dynamic and competitive landscape of modern banking.

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