Impact of Artificial Intelligence on Effective Document Management in the Banking Sector in Nigeria

Impact of Artificial Intelligence on Effective Document Management in the Banking Sector in Nigeria

Osita Amaugo

Tau, Portharcourt-Old Gra, Rivers, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2025.905000336

Received: 28 April 2025; Revised: 13 May 2025; Accepted: 16 May 2025; Published: 14 June 2025

ABSTRACT

This study investigates the influence of artificial intelligence on the effectiveness of document management within Nigerian banking institutions. In a context where conventional document management has transitioned to digital advancements, the incorporation of AI technologies, encompassing machine learning and natural language processing, promises enhanced accuracy, diminished turnaround times, and heightened overall efficiency.  Employing a quantitative study design, data were gathered using questionnaires from a randomly selected sample of branches of Fidelity, GTB, Polaris, Stanbic, and Eco Bank, thereby assuring a robust representation of the 281 respondents.  The research examined three hypotheses concerning the influence of AI on document classification, retrieval efficiency, and overall operational performance, with empirical evidence indicating substantial enhancements in these areas. Employing multiple regression analysis alongside statistical and computational methods, the findings indicate that AI integration achieves an average classification accuracy of 92.3%, markedly decreases document retrieval time, and exhibits a robust positive correlation with enhanced efficiency. The ramifications of these findings extend beyond mere enhancement of operational performance, offering significant insights for the banking sector and potentially other businesses facing analogous difficulties.  This study advocates for the adoption of AI-driven document management systems, acknowledging the necessity for meticulous deployment to alleviate related risks, including data protection concerns. This study significantly contributes to the scientific discourse on AI integration in financial institutions and inspires future innovations in the field.

Keywords: Document, Management, Banking, Nigeria, Artificial, Intelligence, Effective

INTRODUCTION

In contemporary business operations, effective document management has become an essential element for organizational efficiency and data security. Document management encompasses a broad range of activities, including the systematic capture, storage, retrieval, distribution, and disposal of documents (Azhar & Iqbal, 2018; Ayaz & Yanartaş, 2020). These activities ensure that critical information is not only accessible but also maintained in compliance with regulatory requirements (Jakšič & Marinč, 2019). In the banking sector, where transactions, financial reports, compliance documents, and customer records are constantly in flux, effective document management is crucial for administrative efficiency (Maskin, 2024; Abidin et al., 2020). Additionally, document management helps in maintaining the integrity and reliability of financial data, fostering trust and transparency among stakeholders (Nyrkov et al., 2016). Given the highly regulated environment of banking, any failure in document management could lead to significant legal and reputational consequences, making the importance of efficient systems even more pronounced (Benmakhlouf & Chouaou, 2024).

Historically, banks have struggled with the limitations of paper-based records. The cumbersome nature of physical documents, which were prone to damage, loss, and misplacement, has made document management a complex task (Thach et al., 2021). The transition to electronic records management was a ground-breaking shift, marking the beginning of a more organized and efficient system (Sircar et al., 2021; Heines, Rüttimann & Jung, 2021). Electronic document management systems (EDMS) offered the ability to store, retrieve, and manage documents quickly and securely, solving many issues associated with traditional paper-based methods (Manser Payne, Peltier & Barger, 2021). Despite this, challenges remain, such as data redundancy, corruption, and inefficiencies in retrieval processes, which hinder the effectiveness of these systems (Ismael & Okumus, 2017; Gangi et al., 2019). The need for more reliable and efficient document management systems in the banking industry is more pressing than ever, as organizations strive to streamline their operations while ensuring compliance with legal and regulatory standards (Rosa et al., 2019).

Artificial intelligence (AI) offers a revolutionary approach to solving these ongoing challenges. AI technologies, such as machine learning (ML) and natural language processing (NLP), have the potential to transform document management by automating many of the tasks that traditionally required human intervention (Kaur et al., 2020; Malali & Gopalakrishnan, 2020). Machine learning algorithms can significantly improve document classification and retrieval, while NLP techniques enhance the accuracy of data extraction, providing faster and more accurate results than human operators could achieve (Gangi et al., 2019; Kaur et al., 2020). By integrating AI into document management systems, banks can reduce human error, improve operational efficiency, and ensure that critical data is always at hand when needed (Pandey et al., 2023; Di Marzo Serugendo et al., 2024). This can lead to a more streamlined and efficient workflow, which is highly desirable in the fast-paced banking environment (Qasaimeh & Jaradeh, 2022).

The integration of AI technologies into document management systems also opens the door for advanced functionalities such as intelligent search capabilities (Kaur et al., 2020; Malali & Gopalakrishnan, 2020). These capabilities enable the rapid searching of vast document repositories, allowing users to find the relevant documents in seconds rather than minutes or hours (Thach et al., 2021). This is particularly useful for regulatory compliance and audit purposes, where banks must frequently access and review historical records to ensure that their operations align with legal requirements (Thach et al., 2021). Moreover, AI-powered systems are often equipped with robust security protocols, which help to mitigate the risk of data breaches and enhance the overall security of the document management process (Bostonovna, 2023). As data security remains a major concern in the banking sector, the implementation of AI can provide much-needed safeguards against unauthorized access and information theft (Königstorfer & Thalmann, 2020).

The application of AI in document management is especially relevant in the context of Nigerian banks, which face unique challenges related to data integrity and compliance with regulatory measures (Qasaimeh & Jaradeh, 2022). In Nigeria, the banking sector is highly regulated, with strict requirements regarding record-keeping and reporting (Indriasari, Gaol & Matsuo, 2019). Failures in document management can lead to significant financial penalties, reputational damage, and legal issues (Al Shobaki et al., 2017). Furthermore, as Nigerian banks increasingly adopt digital technologies, the pressure to ensure the security and accuracy of data intensifies. AI offers a potential solution to these challenges by automating many of the tasks involved in document processing, thereby reducing the risk of errors and improving compliance (Doumpos et al., 2023). For example, AI-based systems can automatically flag missing or incorrect data, ensuring that financial records are accurate and complete before they are submitted to regulatory bodies (Abacı & Medeni, 2022; Yulianto et al., 2024).

Despite its promise, the adoption of AI in document management systems is not without its challenges. One of the primary concerns is data privacy. Banks must ensure that AI systems comply with data protection laws and that sensitive customer information is handled securely (Jakšič & Marinč, 2019). The implementation of AI also requires significant investment in both hardware and software, which may pose financial challenges for some institutions (Ammirati et al., 2020; Usama, 2025). Additionally, the introduction of AI technologies into traditional banking environments may face resistance from employees who are wary of changes to established workflows (Rahman et al., 2023; Jakšič & Marinč, 2019). Therefore, it is essential for banks to develop a comprehensive implementation strategy that takes into account both the benefits and the potential drawbacks of AI adoption.

The aim of this study is to critically examine the impact of AI on document management efficiency within Nigerian banks. This examination focuses on its ability to improve classification accuracy, reduce document retrieval time, and enhance overall operational efficiency. While previous studies (Pandey et al., 2023; Di Marzo Serugendo et al., 2024; Yulianto et al., 2024) have explored the general benefits of AI in document management, there is a gap in the literature concerning its specific application in the Nigerian banking sector. This study seeks to address that gap by providing empirical evidence on the effectiveness of AI-driven document management systems in Nigerian banks and offering insights into how these systems can be leveraged to enhance operational performance.

Given the complexity and scale of banking operations, the integration of AI in document management systems has the potential to streamline workflows and reduce inefficiencies. For instance, AI systems can automate the process of document classification by analyzing the content of documents and categorizing them based on predefined criteria (Yulianto et al., 2024). This eliminates the need for manual sorting, which is not only time-consuming but also prone to errors. AI can also speed up document retrieval times, allowing employees to access necessary information faster and more efficiently (Sambetbayeva et al., 2022; Usama, 2025). The ability to rapidly retrieve documents is particularly valuable in the banking sector, where quick access to financial records is essential for making informed decisions and responding to customer inquiries (Aziz & Andriansyah, 2023).

Furthermore, AI integration can lead to improved data accuracy, which is crucial in a sector where even minor errors can have significant financial implications (Obukhov et al., 2020; Tharayil et al., 2024). Traditional document management systems are often prone to human error, such as misfiling or incorrectly labelling documents. AI-based systems, on the other hand, can classify documents with high accuracy, minimizing the risk of such errors and improving the reliability of the entire document management process (Sprague, 1995). This increased accuracy not only improves operational efficiency but also strengthens the integrity of financial records, which is vital for maintaining stakeholder trust (Singh & Gildhiyal, 2023; Abdukhalilova et al., 2023).

The ultimate goal of this study is to provide insights into the practical benefits of AI in document management, focusing on its impact on the Nigerian banking sector. By exploring the integration of AI technologies into document management systems, this research will contribute to the growing body of knowledge on AI applications in banking and offer valuable recommendations for banks looking to adopt AI-driven solutions to improve their operations.

The following null hypotheses were formulated and tested:

H₀1: Artificial intelligence does not significantly improve the accuracy of document classification in Nigerian banks.

H₀2: The implementation of AI in document management does not result in a reduction of document retrieval time in banking institutions.

H₀3: There is no significant relationship between the integration of AI and overall operational efficiency in document management within Nigerian banks.

METHODOLOGY

Research Design

This study employed a descriptive-quantitative research design, which is particularly effective in capturing the quantitative impact of artificial intelligence (AI) improvements on document management practices in Nigerian banks. A quantitative approach facilitates the collection of numerical data from a large sample, which allows for the application of statistical tools to rigorously test the hypotheses proposed in this research (Qasaimeh & Jaradeh, 2022). The use of questionnaires as the primary data collection instrument ensures that responses are systematically gathered, quantifiable, and suitable for subsequent analysis (Doumpos et al., 2023). Moreover, this approach offers the opportunity to capture both the breadth and nuance of document management practices as experienced by bank employees, providing a comprehensive understanding of how AI technologies are influencing operational efficiency (Königstorfer & Thalmann, 2020).

The target population for this study consisted of employees from five prominent financial institutions in Nigeria: Fidelity, GTB, Polaris, Stanbic, and Ecobank. These banks were selected due to their significant presence in the Nigerian banking sector and their varying degrees of technological adoption in document management. A purposive sampling technique was employed to ensure the inclusion of banks that are most likely to have AI-driven document management systems in place. Within each of these banks, two branches were selected, resulting in a total of 281 participants. The employees chosen for this study hold positions that make them highly familiar with the intricacies of records management, making them ideal respondents for capturing detailed insights into the impact of AI on document management (Kaur et al., 2020).

To enhance the representativeness of the sample and ensure that each bank and branch was proportionally represented, a stratified random sampling technique was applied. Stratified sampling was chosen because it allows for the identification and inclusion of subgroups within the larger population (Malali & Gopalakrishnan, 2020). This is particularly useful when examining a diverse set of institutional contexts (Malali & Gopalakrishnan, 2020). This technique ensures that each bank’s records management practices are adequately represented in the study, thus enhancing the generalizability and reliability of the results (Manser Payne et al., 2021). By selecting participants from each stratum randomly, the study minimizes potential biases that could arise from non-random selection, further strengthening the validity of the findings (Manser Payne et al., 2021).

Stratified random sampling not only ensures proportional representation of each bank and branch but also supports robust statistical analysis. This method allows the researcher to capture the nuances of document management practices within different institutional settings, ensuring that the results are reflective of the broader banking sector rather than being skewed by one particular institution or branch (Sircar et al., 2021). Through this approach, the study aims to provide a detailed and accurate picture of how AI is transforming document management across various banks, accounting for any unique practices or challenges that may arise in different organizational contexts.

Validity and Reliability of the Instrument

To ensure the validity of the data collected, the validity and reliability of the questionnaire were verified. Content validity was established by reviewing the existing literature and consulting experts in the field of document management and human intelligence (Straus, 2017). Subsequently, a pilot study was conducted to ensure that the instrument accurately measured the desired outcomes, with adjustments made to the responses obtained (Mohajan, 2017). Reliability was verified by calculating Cronbach’s alpha coefficients for the different scales contained in the questionnaire. The coefficients were found to exceed the acceptable limits. This indicates that the instrument provides consistent results in repeated studies (Sürücü & Maslakci, 2020). This rigour in the process of designing and validating the instrument ensures that the results obtained from the study are reliable and reproducible (Taherdoost,, 2016).

Model Specification

The appropriate model for the current study is the Multiple Regression Analysis model. This model allows for the examination of the relationship between the independent variable, specifically the integration of artificial intelligence in record management, and the dependent variables, which include record classification accuracy, record quality, and overall performance (Bujang & Adnan, 2016; Kyriazos, 2018). The regression model may be thus specified as:

Y= β0+β1X1+β2X2+β3X3+ϵ

Where:

Y: represents the overall operational efficiency in document management;

X1: denotes the level of AI integration in document classification;

X2: represents the reduction in document retrieval time;

X3: denotes other operational variables such as cost-effectiveness and compliance rate;

β0 is the intercept;

β1,β2,β3: are the coefficients of the independent variables; and

ϵ: is the error term.

This model is most appropriate as it accounts for multiple facets of document management and enables the researcher to parse out the distinct contributions of AI interventions vis-à-vis traditional methods (Chow et al., 2017).

Method of Analysis

The method of analysis consists of a series of statistical procedures designed to test the proposed hypotheses and reveal the relationships between the variables. Descriptive statistics, including means, standard deviations, and frequencies, were first calculated to provide an overview of the data (Mead, 217). Subsequently, inferential statistical techniques, such as t -tests and analysis of variance (ANOVA), were used to test the significance of differences between groups (Kruschke & Liddell, 2018).

The main analytical method is the use of multiple regression analysis, which allows simultaneous assessment of the influence of independent variables on the dependent variable (Smeeton, Spencer & Sprent, 2025). In this model, the variable representing AI integration is regressed on the document management performance measure, thereby providing an estimate of the effect size and its statistical significance (Mertler, 2021). Regression analysis was further supplemented by correlation analysis between variables (Ali & Bhaskar, 2016).

The independent variables used in this paper include:

Level of AI Integration (X₁): Measured through a series of items addressing the adoption of AI-driven tools in document classification and indexing (Bethea, 2018).

Document Retrieval Time (X₂): Quantified by the average time taken to retrieve documents, as reported by respondents (Hayes, 2020).

Other Operational Variables (X₃): This composite variable includes cost-effectiveness, error rates, and compliance with regulatory standards (Bethea, 2018).

Each variable is implemented in a way that allows for accurate measurement, ensuring that the statistical tests produce relevant and actionable results (Hayes, 2020). The data collected was evaluated using statistical software programs, and the results were presented in a well-labeled manner that helped clarify the relationship between AI integration and improved document management in Nigerian banks (Hayes, 2020).

FINDINGS AND DISCUSSION

This section presents the empirical findings of the study in tables that address the three hypotheses. Each table summarises the key statistical indices and measures, thereby shedding light on the effect of artificial intelligence on the various facets of document management in Nigerian banks.

Table 1. Hypothesis 1: Impact of AI on the Accuracy of Document Classification

Discussion

The results from Table 1 demonstrate that AI significantly improves document classification accuracy, with a mean accuracy of 92.3%. The low variability in responses further supports the robustness of AI systems in reducing human error. These results align with prior studies, such as those by Martiri et al. (2018), which confirm that AI can outperform human accuracy in classification tasks.

Table 2. Hypothesis 2: Effect of AI on Document Retrieval Time

Discussion

As indicated in Table 2, AI reduces document retrieval time to an average of 3.8 seconds, significantly faster than traditional methods. The t-statistic of -5.12 and p-value of 0.000 confirm that the reduction is statistically significant, highlighting AI’s effectiveness in enhancing operational speed. This finding is consistent with similar research, such as Haryanti (2020), which also found that AI optimizes retrieval processes.

Table 3. Hypothesis 3: Relationship Between AI Integration and Overall Operational Efficiency

Discussion

Table 3 shows a strong positive relationship (β = 0.68) between AI integration and overall operational efficiency. The regression analysis indicates that AI contributes to 56% of the variability in efficiency improvements, with a statistically significant result (p = 0.002). These findings echo those of Heines, Rüttimann, & Jung (2021), who found that AI integration in banking enhances productivity and operational performance.

SUMMARY OF RESULTS

The findings presented in these tables provide compelling evidence that AI integration leads to significant improvements in the accuracy of document classification, reduction in document retrieval time, and overall operational efficiency. The high classification accuracy of 92.3%, coupled with a notable reduction in retrieval time, demonstrates that AI can enhance banking operations by automating and optimizing traditionally manual processes. Furthermore, the regression analysis reinforces the positive impact of AI on operational efficiency, showing that AI-powered systems contribute significantly to improved productivity in Nigerian banks.

Further Implications

These results not only align with previous studies but also contribute to the growing body of literature on AI in banking. The evidence here supports the argument that AI can substantially reduce operational inefficiencies, enhance data accuracy, and improve overall service delivery (Abidin et al., 2020; Heines, Rüttimann & Jung 2021; Benmakhlouf & Chouaou, 2024). The positive correlation between AI integration and operational efficiency highlights the potential for AI to offer a competitive edge to banks that adopt these technologies (Kasim & Khalid, 2016; Nurmeksela, 2017). However, while the study highlights the significant benefits of AI, it also suggests that careful implementation and ongoing evaluation are necessary to mitigate challenges related to data privacy and system integration (Rahman et al., 2023; Jakšič & Marinč, 2019).

CONCLUSION

This study critically examined the impact of artificial intelligence (AI) on document management efficiency in Nigerian banks. The findings provide substantial evidence that AI integration leads to significant improvements in document classification accuracy, retrieval time, and overall operational efficiency. Specifically, the study found that AI systems achieved a high average classification accuracy of 92.3%, drastically reduced document retrieval time to an average of 3.8 seconds, and contributed to a 56% increase in operational efficiency. These results underscore the transformative potential of AI in optimizing document management processes, particularly in highly regulated environments like banking, where speed and accuracy are paramount.

The statistical analyses conducted (multiple regression and hypothesis testing) confirmed that AI integration not only improves the efficiency of document management but also enhances the accuracy and reliability of document handling. By reducing human error and automating manual tasks, AI offers banks a substantial opportunity to streamline operations, reduce costs, and improve service delivery to customers. Moreover, the AI systems examined in this study also provided robust security features, mitigating risks associated with data breaches and unauthorized access, which are critical concerns in the banking sector.

While these findings are promising, the study also highlights the challenges associated with implementing AI systems in traditional banking environments, particularly in terms of data privacy concerns and employee resistance to change. Therefore, the implementation of AI in document management systems must be carried out thoughtfully, with appropriate safeguards in place to protect sensitive data and with ongoing training for employees to adapt to these new technologies.

Areas for Future Research:

While this study focused on the impact of AI in Nigerian banks, future research could expand its scope to examine the application of AI in other sectors facing similar document management challenges, such as healthcare, law enforcement, and government agencies. Additionally, future studies could explore the long-term effects of AI adoption on organizational culture, employee satisfaction, and customer trust, as these factors are crucial in determining the overall success of AI integration.

Furthermore, more research is needed to investigate the ethical implications of AI in document management, particularly in relation to data privacy and the transparency of AI-driven decision-making processes. Future studies could also explore the development of hybrid systems that combine AI with human oversight to address any concerns related to the complete automation of critical tasks. Lastly, examining the financial and operational costs associated with implementing AI systems in document management will provide valuable insights for organizations considering this technology.

Thus, the adoption of AI in document management systems represents a significant step toward improving operational efficiency in the banking sector. This study contributes to the growing body of literature on AI applications in banking, providing both empirical evidence and practical recommendations for future implementation. Given the promising results, it is clear that AI has the potential to revolutionize document management practices, making them faster, more accurate, and more secure, thereby positioning banks for greater success in the increasingly digital financial landscape.

ACKNOWLEDGEMENT

We express our sincere and profound gratitude to the institutions and people without whose generous support this research would not have been possible. Our sincere thanks to the management and staff of Bank Fidelity, GTB, Polaris Bank, Steinbeck IBTC Bank and Ecobank, whose cooperation and transparency in providing information and insights have strengthened the study beyond measure.

Conflict of interest

There was no conflict of interest as the data was collected from the staff of Fidelity, GTB, Polaris, Stanbic, and Ecobank hence no financial and time commitment was experienced as this was done during the weekend.

Ethical Approval

The study adhered to ethical guidelines, ensuring that participation was voluntary, and respondents’ confidentiality was maintained. All participants were informed of the study’s purpose and assured that their responses would only be used for academic purposes. No personal identifiers were collected to ensure anonymity.

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