Biodun P. Oluwole, How Data and Automation Transformed Small Business Lending Amid COVID-19 (2025)
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An article by Biodun P. Oluwole, CEO and President of BMOD Network Solutions, titled How Data and Automation Transformed Small Business Lending Amid COVID-19, published April 20, 2025 in the European Journal of Computer Science and Information Technology, Vol. 13(7), pages 1–18 (DOI 10.37745/ejcsit.2013/vol13n7118). Using a mixed-method survey of 500 key stakeholders in Nigeria—including small business owners, financial service providers, fintech innovators and regulatory officials—the study examines how commercial banks and fintech startups adopted automated loan origination, eligibility assessment, fraud detection and compliance reporting during COVID-19. It notes that manual lending processes and in-person verifications were overwhelmed by relief applications for micro, small and medium enterprises, which contribute over 50% of Nigeria's GDP.
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European Journal of Computer Science and Information Technology,13(7),1-18, 2025
Print ISSN: 2054-0957 (Print)
Online ISSN: 2054-0965 (Online)
Website: https://www.eajournals.org/
Publication of the European Centre for Research Training and Development -UK
How Data and Automation Transformed Small
Business Lending Amid COVID-19
Biodun P. Oluwole
CEO & President
BMOD Network Solutions
doi: https://doi.org/10.37745/ejcsit.2013/vol13n7118 Published April 20, 2025
Citation: Oluwole B.P. (2025) How Data and Automation Transformed Small Business Lending Amid COVID-19,
European Journal of Computer Science and Information, Technology,13(7),1-18
Abstract: The global COVID-19 pandemic triggered unprecedented economic disruptions,
severely impacting micro, small, and medium enterprises (MSMEs) across developing economies.
In Nigeria, small businesses, already grappling with limited access to credit, encountered
additional constraints as traditional loan disbursement systems became overwhelmed by the
volume and urgency of pandemic-related relief applications. Manual lending processes,
characterized by bureaucratic delays and in-person verifications, proved ill-equipped to handle
the crisis, prompting an accelerated shift toward data-driven digital solutions. In response,
financial institutions—ranging from commercial banks to fintech startups—deployed automation
technologies to streamline loan origination, eligibility assessments, fraud detection, compliance
reporting, and customer engagement. These technologies not only enhanced the speed and
accuracy of credit delivery but also contributed to greater transparency and accountability in the
disbursement of public funds.This paper investigates the transformative role of automation in
Nigeria’s small business lending landscape during COVID-19. Using a mixed-method research
design, we surveyed 500 key stakeholders, including small business owners, financial service
providers, fintech innovators, and regulatory officials. The findings reveal that automation
significantly improved loan approval timelines, increased user satisfaction, and enhanced fraud
prevention capabilities. Furthermore, the study underscores automation’s long-term potential in
deepening financial inclusion, improving regulatory oversight, and driving operational efficiency
within Nigeria’s financial sector. By offering empirical insights, this research contributes to the
evolving discourse on digital transformation in emerging markets and provides a framework for
future innovation in crisis-resilient financial systems.
Keywords: automation, small business lending, COVID-19, Nigeria, financial services, digital
transformation
INTRODUCTION
The outbreak of the COVID-19 pandemic sent shockwaves across global economies, disrupting
business operations, destabilising supply chains, and intensifying financial uncertainty. In Nigeria,
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the impact was particularly profound on micro, small, and medium enterprises (MSMEs), which
collectively contribute over 50% to the nation’s GDP and form the backbone of employment
(Chondough, 2021; Akpan et al., 2022). Nigerian small businesses are already faced with limited
access to affordable credit and severe liquidity shortages during the pandemic. Government relief
programs were swiftly rolled out to support MSMEs, but traditional lending infrastructures,
marked by slow manual processes and centralized physical verifications, were incapable of
responding effectively to the surge in loan demand (Arinzeh, 2022).
Therefore, the pandemic exposed deep-rooted inefficiencies in Nigeria’s financial systems and
catalyzed a rapid pivot toward digital transformation (Agarwal et al., 2024). Financial institutions,
regulatory agencies, and fintech providers turned to automation and data-driven technologies as a
way to streamline loan processing, improve risk assessments, and maintain service continuity
under lockdown conditions (Akpan et al., 2022). According to Subramanyam (2024), Technologies
such as artificial intelligence (AI), predictive analytics, robotic process automation (RPA), and
digital identity verification became essential components of the lending ecosystem. These
innovations enabled faster loan approvals, improved fraud detection, and allowed institutions to
comply with evolving regulatory requirements—all while minimizing face-to-face interactions
(Theodos et al., 2022).
The adoption of automation in this context was not merely a matter of convenience; it became a
necessity for resilience. Predictive analytics, in particular, played a crucial role by analyzing vast
volumes of historical and real-time data to inform credit decisions, anticipate borrower defaults,
and identify patterns of fraud (Subramanyam, 2024). These advancements allowed financial
institutions to disburse loans more efficiently and accurately, ensuring that support reached the
businesses most in need.
Despite the clear shift in lending practices, there remains a notable gap in empirical research that
explores how automation and data analytics transformed small business financing in Nigeria
during this unique period (Odio et al., 2021). Existing studies often focus on the broader rise of
fintech or the challenges of financial inclusion but rarely interrogate the direct relationship between
crisis response, technological adoption, and institutional efficiency within the Nigerian context
(Ediagbonya & Tioluwani 2023). Moreover, while global narratives emphasize the benefits of
digitization, they often overlook the localized infrastructural, regulatory, and socio-economic
constraints that shape implementation outcomes in emerging markets (Akpan et al., 2022).
This study draws on two key theoretical perspectives to frame the investigation. First is Rogers’
Diffusion of Innovation Theory (Rogers et al., 2014), which explains how new technologies spread
through populations in response to perceived benefits and external pressures. The urgency of the
COVID-19 crisis accelerated the diffusion of automation tools among financial institutions, driven
by the need to sustain operations and serve growing customer demands (Amankwah-Amoah et al.,
2021). Second is the Structural Contingency Theory, which posits that organizational effectiveness
improves when operational structures align with environmental demands. In this case, automation
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served as a structural adjustment to the pandemic's disruptions, enabling banks and fintechs to
adapt to the dynamic conditions and continue delivering credit services efficiently (Pennings,
2013).Guided by these theoretical foundations, this study addresses the following primary research
question:
How did data and automation impact small business lending in Nigeria during the COVID-19
pandemic? In seeking to answer this question, the research asked several sub-questions
(Chondough, 2021; Akpan et al., 2022): What role did automation play in streamlining loan
approvals and disbursements? How effective were automated compliance and fraud detection
systems in reducing financial risks for both lenders and borrowers? What challenges did small
businesses face in accessing loans during the pandemic, and how were these addressed through
automation? Finally, what is the long-term impact of automation on small business financing in
Nigeria in the post-pandemic era?
To achieve these aims, the study sets out the following objectives:
It seeks to examine the role of data and automation in enhancing access to small business loans
during the COVID-19 crisis in Nigeria. It aims to evaluate the effectiveness of automated credit
scoring models and AI-based underwriting in speeding up loan approvals. It also analyzes how
automation supports regulatory compliance and fraud prevention across institutions (Theodos et
al., 2022; Odio et al., 2021). Furthermore, the research assesses the key challenges encountered by
MSMEs in the lending process and how automation helped to mitigate those issues. Lastly, it
explores the enduring implications of digital automation for the future of small business financing,
particularly in fostering financial inclusion and operational sustainability.
To provide a robust and well-rounded analysis, the study adopts a mixed-method research design.
Quantitatively, a structured questionnaire was distributed to 500 stakeholders, including small
business owners, bank employees, FinTech operators, and regulatory officials. The survey assessed
experiences with automated lending platforms, loan access, fraud mitigation, and customer service
improvements. Qualitatively, a systematic literature review was conducted, covering peer-
reviewed articles, institutional reports, and regulatory guidelines published between 2020 and
2025. This dual approach allows for both empirical validation and contextual understanding of
automation’s role during the pandemic.
Through this research, the paper aims to make a valuable contribution to the growing discourse on
digital transformation in financial services, particularly within the African context. It offers
evidence-based insights that can guide policymakers, financial institutions, and technology
developers in building more inclusive, resilient, and technology-driven credit systems for small
businesses in Nigeria and similar emerging economies.
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LITERATURE REVIEW AND THEORETICAL UNDERPINNING
The COVID-19 pandemic marked a turning point for financial service delivery across the globe,
especially in emerging economies like Nigeria, where digital infrastructure and financial inclusion
were still developing. As traditional systems faltered under the weight of urgent credit demand,
automation and data-driven technologies became critical to maintaining lending operations. This
section reviews relevant literature on digital transformation in the financial sector, particularly in
the context of small business lending, and outlines the theoretical framework guiding the current
study.
Digital Transformation in Financial Services
Before the pandemic, financial institutions globally had begun incorporating automation into
various aspects of service delivery (Theodos et al., 2022). However, this digital shift was gradual
and often constrained by regulatory hesitation, budget limitations, and customer adoption rates.
COVID-19 accelerated this process dramatically. According to Cong et al. (2021), the pandemic
created an imperative for rapid digitization as institutions sought to maintain operational continuity
while addressing surging demand for relief credit. In Nigeria, where many small businesses are
informal or underbanked, this digital push created new opportunities and challenges (Singh et al.,
2024).
Fintech companies in Nigeria played a crucial role in this transition. Research by Ediagbonya &
Tioluwani (2023) shows that digital lenders outpaced traditional banks in deploying mobile-first
lending solutions, automated credit scoring, and real-time decision-making systems. These fintech
solutions often relied on non-traditional data, such as mobile phone usage, e-commerce activity,
and social behavior, to assess borrower risk in the absence of formal credit history.
Automation and Credit Risk Management
Automation tools such as AI, predictive analytics, and machine learning have significantly altered
the approach to credit risk assessment. Studies by Theodos et al. (2022) and Cong et al. (2021)
emphasize that automated underwriting systems can process loan applications more quickly,
reduce administrative costs, and detect risk patterns with greater precision than manual methods.
During the pandemic, these capabilities became essential as financial institutions faced both
increased application volume and elevated default risks.
In Nigeria, banks and fintechs adopted AI-powered platforms to assess loan eligibility and deploy
capital at scale. According to Ediagbonya & Tioluwani (2023), automated systems also enable
lenders to personalize loan terms and repayment structures based on real-time income data and
behavioral patterns, thereby reducing default rates and improving customer satisfaction.
Compliance and Fraud Detection
Okegbola & Akiotu (2024) talked about the key area where automation proved transformative is
in regulatory compliance and fraud mitigation. Compliance with anti-money laundering (AML)
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and know-your-customer (KYC) regulations typically requires labour-intensive verification
procedures. However, the use of intelligent automation and digital identity verification streamlined
these processes during the COVID-19 emergency, allowing institutions to remain compliant
despite remote operations (Chondough, 2021).
Fraud prevention also improved significantly with the use of pattern recognition algorithms and
anomaly detection tools (Okegbola & Akiotu, 2024). Financial institutions could flag suspicious
applications or behaviors in real time, thus protecting both public funds and institutional capital.
In the Nigerian context, where fraud and documentation forgery are recurring concerns, these
systems added a vital layer of security to emergency lending operations.
Barriers to Automation: The Role of Digital Literacy and Infrastructure
Despite the clear benefits, several studies highlight barriers to the successful deployment of
automation in emerging markets. Digital exclusion, poor internet connectivity, low smartphone
penetration, and limited digital literacy among small business owners hindered widespread
adoption (Bello & Ajao 2024). These issues created disparities in access, with rural or informal
businesses often left behind in the digital lending transformation.
Ethical concerns related to algorithmic bias, data privacy, and user consent have surfaced in global
discourse. As noted by Tsamados et al. (2021), over-reliance on opaque AI systems can perpetuate
inequalities if the underlying data reflects historical discrimination or structural disparities.
Theoretical Framework
To contextualize these dynamics, this study draws on two interrelated theoretical models:
Diffusion of Innovation Theory and Structural Contingency Theory.Rogers’ Diffusion of
Innovation Theory (Rogers et al., 2014) provides a framework for understanding how new
technologies, such as automation in lending, are adopted within organizations and among user
groups. According to Rogers et al. (2014), the theory, adoption is influenced by perceived
advantage, compatibility with existing systems, complexity, trialability, and observability. In the
case of Nigeria, the pandemic acted as an external pressure that significantly accelerated the
adoption of automation across the financial sector. Financial institutions that might otherwise have
hesitated to digitize were compelled to innovate to survive.
Structural Contingency Theory posits that organizations must align their internal structures with
external environmental conditions to remain effective (Pennings, 2013). During the pandemic,
traditional banking structures proved incompatible with the urgent, high-volume demands for
small business loans. The deployment of automation represented a structural adjustment that
enabled institutions to respond more effectively to a rapidly changing environment (Amankwah-
Amoah et al., 2021). By realigning systems with external pressures, Nigerian banks and fintechs
increased their operational efficiency and improved their service delivery outcomes.
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Literature Gap and Contribution.
While global studies have documented the role of automation in financial resilience, there remains
limited empirical research focused on the Nigerian context, especially as it relates to MSME
lending during COVID-19 (Otokiti et al., 2022). Much of the available literature addresses either
fintech adoption in general or financial inclusion strategies without adequately capturing the
systemic changes that occurred in the lending process during the pandemic (Petighin, 2023).
Moreover, few studies integrate both qualitative stakeholder insights and quantitative performance
metrics to assess the true impact of automation.
This study addresses that gap by combining a stakeholder-based survey with a systematic review
of relevant literature to provide a comprehensive analysis. It offers empirical evidence on how data
and automation shaped the efficiency, inclusiveness, and sustainability of small business lending
in Nigeria during a time of crisis. By doing so, it contributes to both the theoretical discourse on
innovation diffusion and the practical debate on building more resilient financial ecosystems in the
developing world.
METHODOLOGY
This study adopts a mixed-method research design that combines both quantitative and qualitative
approaches to explore how data and automation transformed small business lending in Nigeria
during the COVID-19 pandemic (Nalisa, 2022). The research relies on a convergent parallel
design, allowing quantitative and qualitative data to be collected simultaneously and later
integrated during analysis to provide a comprehensive understanding of the topic.
The quantitative aspect of the research was carried out using a structured questionnaire
administered to a total of 500 respondents (Clampitt 2009). These participants included small
business owners (60%), bank employees (25%), fintech representatives (10%), and regulatory
officials (5%), selected through stratified random sampling to ensure balanced representation
across the stakeholder groups. The questionnaire was designed to capture information on access to
credit, awareness and use of automation tools, fraud prevention, compliance mechanisms, user
satisfaction, and challenges experienced during the pandemic. The questionnaire was distributed
both online and physically in various regions of Nigeria, including Lagos, Abuja, Kano, and
Enugu, between February and March 2025.
In addition to the quantitative data, the study also conducted a systematic literature review to gain
contextual insights and theoretical support for the analysis (Arevalo 2024). This review followed
the PRISMA guidelines and involved sourcing peer-reviewed articles, institutional reports, policy
briefs, and scholarly publications from academic databases such as JSTOR, ScienceDirect, Google
Scholar, and Scopus (Arevalo 2024). The review focused on publications from 2020 to 2025 that
addressed themes such as digital lending, automation, fintech in crisis response, and financial
inclusion in Nigeria and other emerging markets.
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Descriptive statistical analysis was applied to the quantitative data, including the use of
percentages, frequencies, and mean scores to identify trends and stakeholder perceptions (George
& Mallery 2018). Visual representations such as charts and tables were generated to present the
findings. For the qualitative component, thematic analysis was used to identify recurring patterns
and insights, both from open-ended responses in the questionnaire and from the reviewed
literature. Themes such as automation efficiency, compliance adaptation, digital inequality, and
fraud mitigation were extracted and mapped to the theoretical framework guiding the study
(Castleberry & Nolen 2018).
Ethical considerations were rigorously observed throughout the research process. Informed
consent was obtained from all participants, and data was collected anonymously to ensure
confidentiality (Kang & Hwang, 2021). The study adhered to the ethical guidelines concerning
privacy, voluntary participation, and the right to withdraw at any time.
By integrating quantitative data from stakeholder experiences and qualitative insights from
academic literature, this methodology offers a robust foundation for understanding the practical
and strategic impact of automation in Nigeria’s small business lending sector during a time of
unprecedented disruption (Kang & Hwang, 2021; Arevalo, 2024).
RESULTS AND FINDINGS
This study uncovers how data and automation have transformed Nigeria’s small business lending
sector amid the COVID-19 pandemic. It draws from responses of 500 stakeholders—including
small business owners, bank personnel, fintech representatives, and regulators, combining
quantitative findings from structured questionnaires aimed at evaluating how automation and data
analytics have reshaped small business lending during the pandemic with qualitative insights from
a thorough literature review. This integrated approach provides a comprehensive understanding of
automation’s diverse effects on lending operations, efficiency, compliance, and user experience.
The analyzed data includes responses from 500 participants: small business owners (60%), bank
employees (25%), fintech experts (10%), and regulatory officials (5%). The findings are examined
across four key aspects: loan approval processes, fraud detection and compliance, accessibility of
automated platforms, and stakeholder perceptions of the benefits and risks of automation.
Table 1: Stakeholders
Stakeholders Percentage
small business owners (60%),
bank employees (25%),
fintech experts (10%)
regulatory officials (5%)
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Impact of Automation on Lending Efficiency
The COVID-19 pandemic exerted unprecedented pressure on financial systems to disburse relief
funds and loans promptly. A key objective of this research was to assess whether automation helped
financial institutions meet this urgent demand.According to the results in Table 2, automation
significantly improved the speed of loan approvals; 44.6% of respondents reported that automation
significantly reduced loan approval times, another 35.4% observed a moderate improvement, and
only 20% indicated no change in the speed of loan processing.
This indicates that nearly 80% of respondents acknowledged some level of positive impact in terms
of processing efficiency. This finding supports the hypothesis that automation tools—such as AI-
powered underwriting, credit-scoring models, and workflow management systems—helped banks
manage surging application volumes during the crisis.
Limitations in Fraud Detection and Loan Restructuring
While speed improved, the data also revealed important limitations. All 500 respondents
unanimously reported that automation did not improve Fraud detection capabilities, Transparency
in eligibility requirements, or Provisions for loan restructuring. This result is noteworthy. Despite
the availability of fraud prevention technologies (e.g., AI for identity verification and predictive
analytics), none of the stakeholders reported seeing an improvement in these areas during the
pandemic. It is possible that while the tools existed, they were not effectively implemented or
lacked the necessary data training to perform accurately at scale.
When asked about restructuring flexibility, only 36.3% of respondents said automation made it
easier to request modifications to their loan terms, while 63.7% disagreed. This reflects the rigidity
of most automated systems, which were not fully adapted to handle loan deferrals or adjustments
under emergency conditions.
Accessibility of Online Lending Platforms
As the pandemic restricted physical movement, automation enabled access to financial services
through digital platforms; 70.6% of respondents preferred online channels (website or mobile app)
for loan applications. Only 18.2% still used in-person applications, and 11.2% relied on third-party
agents or intermediaries.
This apparent preference for digital platforms highlights how the pandemic accelerated digital
transformation in Nigeria’s financial ecosystem. It also confirms that automated, self-service
systems—such as mobile loan apps, chatbots, and digital dashboards- were crucial in maintaining
continuity of service.
However, the ease of using these platforms varied; 20.2% rated the platforms as "very easy to use",
54.2% found them "somewhat easy", while 25.6% faced difficulties.
This suggests that while digital adoption was high, user experience design and platform stability
varied, possibly influenced by regional internet accessibility and digital literacy levels.
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Table 2: Automation Impact on Lending Efficiency and Access
Survey Item Response Frequency Percentage
(n) (%)
How did automation impact loan approvals? Reduced loan approval 223 44.6
time significantly
Reduced loan approval 177 35.4
time moderately
No impact 100 20.0
Did data-driven automation improve the Yes 0 0.0
ability to detect fraudulent loan
applications?
No 500 100.0
Did automation make loan eligibility Yes 0 0.0
requirements clearer?
No 500 100.0
Did automation play a role in loan Yes 0 0.0
restructuring during COVID-19?
No 500 100.0
Did automation make it easier to request Yes 182 36.3
loan restructuring?
No 318 63.7
How would you rate the ease of applying for Very easy 101 20.2
a loan online during COVID-19?
Somewhat easy 271 54.2
Difficult 128 25.6
What was/is the preferred method of loan Online (website or 353 70.6
application? mobile app)
In-person at a bank 91 18.2
Via a third-party agent 56 11.2
or intermediary
Challenges, Regulatory Concerns, and Perceived Benefits
The study also explored the major barriers and perceived outcomes of automation from both
lenders' and borrowers' perspectives. As seen in Table 3, the top challenges in implementing digital
lending systems included technical limitations and infrastructure (29.0%), Resistance from users
unfamiliar with digital systems (26.6%), Lack of reliable data for risk evaluation (24.0%), and
Fraud and identity verification issues (20.4%).Moreover, key user-side challenges during the
pandemic were delays in approval (36.2%) and technical difficulties (35.2%). These statistics
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highlight how the success of automation hinges not just on software but also on the availability of
supporting infrastructure—like electricity, internet coverage, and trained personnel.
From a regulatory perspective, the main concerns centered on Data privacy and security risks
(37.4%), Potential exclusion of underserved businesses (32.6%), and Increased risk of algorithmic
fraud (30.0%).Despite these concerns, participants saw automation’s greatest strengths in
Improved fraud detection (26.4%), Faster processing (25.8%), reduced paperwork (25.4%), and
Wider access to funding (22.4%).
Finally, in terms of future outlook, 51.2% believe automation will improve efficiency and
accessibility, and 48.8% see its potential to reduce human bias in lending decisions.
Table 3: Summary of Challenges, Regulatory Concerns, and Perceived Benefits of Automation
Survey Item Response Frequency (n) Percentage (%)
What was the Technical 145 29.0
biggest challenge in limitations/infrastructure
digitizing the challenges
lending process?
Resistance from 133 26.6
customers unfamiliar
with digital systems
Lack of reliable data for 120 24.0
credit scoring
Fraud & identity 102 20.4
verification issues
What is the biggest Delays in approval 181 36.2
challenge you faced
when applying for
or processing loans
during COVID-19?
Technical difficulties in 176 35.2
using digital platforms
Strict eligibility 143 28.6
requirements
What is the biggest Data privacy & security 187 37.4
regulatory concern risks
regarding
automated lending?
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Potential exclusion of 163 32.6
underserved businesses
Increased susceptibility 150 30.0
to fraud
What do you think is Improved fraud 132 26.4
the biggest detection
advantage of using
automation in
lending?
Faster processing time 129 25.8
Reduced paperwork 127 25.4
Better access to 112 22.4
financing for small
businesses
How do you see the It will improve efficiency 256 51.2
future role of and accessibility
automation in small
business lending?
It will reduce human bias 244 48.8
in lending decisions
DISCUSSION
The findings from this study provide compelling evidence that data and automation significantly
reshaped small business lending practices in Nigeria during the COVID-19 pandemic (Bai et al.,
2021). As traditional banking systems struggled to meet the surge in demand for relief loans and
emergency financing, the deployment of automated tools introduced much-needed efficiency and
responsiveness into the financial ecosystem (Kamal et al., 2025). The data reveals that a combined
80% of respondents observed either significant or moderate improvements in the speed of loan
approvals following the introduction of automation (Adewale et al., 2025). This suggests that
technology such as AI-driven credit scoring, automated underwriting, and digital loan origination
systems played a pivotal role in enhancing the timeliness of lending decisions.
However, the study also uncovered critical limitations in the application of automation within the
lending environment (Adewale et al., 2025). Despite the promise of artificial intelligence and big
data for fraud detection, none of the respondents reported noticeable improvements in identifying
fraudulent loan applications. This may be attributed to gaps in data integration, inadequate fraud
prevention models, or the underutilization of real-time verification technologies (Demirgüç-Kunt
et al., 2022; Bai et al., 2021). Automation did not appear to enhance the transparency of loan
eligibility requirements or the flexibility of restructuring loan agreements. This finding points to a
potential rigidity in existing digital lending frameworks, which, while efficient in processing
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standard loan applications, were not adaptable enough to accommodate the nuanced and urgent
restructuring needs that arose during the pandemic.
The accessibility of digital platforms emerged as a defining element of financial resilience. Over
70% of respondents expressed a preference for applying through online channels, including
websites and mobile applications, reflecting a broader shift toward digital finance (Ayadi &
Shaban, 2020). This shift was partially driven by lockdown measures, but it also indicates an
increasing user comfort with remote financial services. However, 25.6% of users still faced
difficulties using these platforms, signaling the persistence of a digital divide rooted in factors such
as poor internet connectivity, inadequate digital literacy, or lack of inclusive platform design
(Kamal et al., 2025). These challenges have important implications for financial inclusion, as
automation alone does not guarantee equal access unless supported by targeted efforts to bridge
infrastructure and knowledge gaps.
Another major insight from the study relates to stakeholder concerns about regulatory and ethical
implications. While automation was praised for improving processing speed and reducing
paperwork, it also raised alarms about data privacy, security, and the exclusion of underserved
groups. Financial institutions and regulators must recognize that algorithmic systems can
perpetuate or even exacerbate biases if not carefully monitored Bahangulu & Owusu-Berko, 2025).
The absence of clear frameworks for ethical AI deployment in Nigeria’s financial sector presents
a risk that needs urgent attention.
Interestingly, the findings also suggest a strong belief in the future potential of automation. Over
half of the respondents felt that automation would lead to greater efficiency and accessibility, and
nearly an equal number expected it to reduce human bias in lending decisions. This indicates a
level of trust in digital transformation, albeit tempered by the recognition of its current limitations.
In reflecting on the role of data and automation during the COVID-19 crisis, it is evident that these
tools offered critical advantages in managing scale, speed, and continuity. Yet, the promise of
automation extends beyond emergency response. Its long-term impact on the Nigerian lending
ecosystem will depend on how effectively stakeholders address its current shortcomings,
particularly in fraud detection, system adaptability, and equitable access (Ayodeji, 2024). The
integration of qualitative responses further supports this view, as many participants shared
sentiments that automation brought convenience and timeliness but lacked personalization and
fairness, especially for marginalized entrepreneurs unfamiliar with digital systems (Arevalo 2024).
Ethical considerations also shaped this study’s approach. Informed consent was obtained from all
participants, and anonymity was maintained to protect respondent identities (Kang & Hwang,
2021). Data was handled in compliance with digital research standards, ensuring confidentiality
and respect for participant perspectives. While the study made significant progress in mapping the
contours of automation’s impact, its limitations include a reliance on self-reported data, which may
be subject to respondent bias, and the exclusion of non-digitally literate populations who may not
have been able to complete the survey.
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The discussion underscores that automation transformed small business lending during the
COVID-19 pandemic in Nigeria by increasing operational efficiency, expanding access through
digital platforms, and stimulating interest in predictive technologies (Akpan et al., 2022).
However, its full potential remains contingent on the resolution of infrastructural, regulatory, and
ethical challenges that continue to shape the digital lending landscape.
Implications to Research and Practice
The findings of this study carry important implications for both academic research and practical
applications in the financial services industry. On the research front, this study contributes
significantly to the growing body of empirical work surrounding digital transformation,
particularly within the context of emerging economies (Michael & Obahiagbon, 2025). By
applying the Diffusion of Innovation Theory and Structural Contingency Theory, the research
offers a conceptual foundation for understanding how financial institutions in Nigeria adopted
automation to meet the demands of a crisis (Rogers et al. 2014). It enriches theoretical discourse
by demonstrating that while automation can be rapidly deployed in response to external shocks,
its efficacy is contingent upon the maturity of the institutional ecosystem and the alignment
between technology, user capabilities, and regulatory frameworks.
Moreover, the integration of qualitative user experiences with quantitative survey data provides a
methodological template that future scholars can replicate or expand (Clampitt 2009; Arevalo
2024). This mixed-method approach ensures that innovation in financial services is examined from
both systemic and user-centric perspectives. The study also opens up new lines of inquiry,
encouraging longitudinal research that explores how automated lending evolves and how it
interacts with macroeconomic factors and institutional behavior. Comparative studies across
different regions within sub-Saharan Africa would offer further insights into the scalability and
sustainability of automated financial systems.
From a practical standpoint, the study highlights several operational and strategic implications for
financial institutions, fintech startups, regulatory bodies, and policymakers. The research
demonstrates that financial institutions which implemented automation during the COVID-19
pandemic were able to improve turnaround times for loan approvals and offer enhanced customer
engagement (Chondough 2021) (Table 2 & 3). These gains in operational efficiency suggest that
such institutions are better equipped for business continuity during crises (Fischbacher-Smith,
2017). There is a clear need for financial organizations to consolidate these innovations post-
pandemic by enhancing scalability, improving integration across departments, and investing in
real-time data processing systems.
Despite these gains, the findings also reveal persistent barriers to access among small business
owners, particularly due to digital illiteracy, poor infrastructure, and the opacity of eligibility
criteria. This underlines the importance of designing lending platforms that are not only
technologically advanced but also inclusive, multilingual, and adaptable to diverse user needs and
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technological environments (Akpan et al., 2022). The success of automation is inextricably linked
to the ease with which users can navigate and engage with the system.
The study further reveals that while automation tools were in place, their effectiveness in fraud
detection and compliance reporting was limited. This indicates a pressing need for financial
institutions to explore advanced fraud detection mechanisms such as behavioral analytics and
adaptive algorithms that provide real-time monitoring (Ediagbonya & Tioluwani, 2023).
Automated compliance systems must also be expanded and refined to reduce regulatory burdens
and ensure reporting accuracy, particularly for micro and small enterprise loan portfolios.
Ethical considerations also emerge prominently from the findings (Gabriel 2023). Data privacy
concerns and the risk of algorithmic discrimination were reported by a significant proportion of
respondents. These insights underscore the necessity for robust data governance frameworks and
regulatory oversight to ensure that algorithmic decision-making is transparent, explainable, and
fair. Ethical AI frameworks, developed in collaboration between regulators and financial
institutions, can ensure that automation promotes equity rather than exacerbates existing
inequalities.
The findings suggest that successful implementation of automation requires capacity building at
multiple levels (Abikoye et al., 2024). Financial institutions must invest in the training of staff to
manage, interpret, and monitor automated systems effectively. At the same time, small business
owners and users of digital lending services need digital literacy programs that equip them with
the skills required to navigate automated platforms confidently and securely. Government agencies
and development partners can play a role in funding and facilitating such programs, especially in
underserved regions.
The study provides insight that can guide policy development. For automation to realize its full
potential in transforming small business lending, it must be supported by a robust national digital
infrastructure (Nguyen 2024). Policies that promote broadband access, foster data sharing through
open banking frameworks, and offer incentives for ethical digital innovation are essential.
Regulatory bodies such as the Central Bank of Nigeria and the Nigeria Data Protection
Commission should work closely with industry stakeholders to ensure that these systems are
accountable, transparent, and inclusive.
In summary, the integration of data and automation into Nigeria’s small business lending landscape
has proven to be a powerful yet incomplete transformation. While the benefits are evident in terms
of speed, reach, and resilience, challenges related to inclusion, ethics, infrastructure, and user
engagement persist. By leveraging the insights from this study, both researchers and practitioners
can better shape the future of digital finance in ways that are not only innovative but also
sustainable and just.
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CONCLUSION AND RECOMMENDATIONS
Conclusion
This study has examined the transformative impact of data and automation on small business
lending in Nigeria during the COVID-19 pandemic. Drawing on both quantitative and qualitative
evidence, including the analysis of 500 questionnaire responses and relevant literature, the research
revealed that automated financial systems significantly enhanced loan processing efficiency,
reduced manual workload, and enabled quicker disbursement of funds at a time when rapid
intervention was critical.
A majority of respondents noted substantial improvements in loan turnaround time due to
automation, and several automation tools, such as AI-driven underwriting, predictive analytics,
and digital self-service dashboards, were identified as contributors to improved customer
satisfaction and operational performance. However, these benefits were not evenly distributed
across all aspects of the lending process. Respondents also pointed out persistent challenges,
including approval delays, lack of clarity in eligibility requirements, and inefficiencies in loan
restructuring. Furthermore, automated systems were perceived as less effective in areas such as
fraud detection and compliance automation.
These mixed outcomes reinforce the study’s theoretical underpinnings, specifically the Diffusion
of Innovation Theory and Structural Contingency Theory. While the rapid adoption of automation
reflected a natural response to environmental pressure during the pandemic, its uneven integration
suggests that the systems were not fully adapted to organizational and infrastructural realities in
Nigeria’s financial sector. The findings confirm that the potential of automation to transform
lending practices is significant, but its success depends on system alignment, ethical
implementation, and capacity building across both institutions and end users.
In essence, the research concludes that automation played a pivotal role in enabling the financial
services sector to withstand and adapt to the challenges of the COVID-19 crisis. However, the
sustainability and inclusiveness of these solutions will depend on the continuous refinement of
infrastructure, governance, and user engagement strategies.
Recommendations
Based on the insights gained, several key recommendations can be made. There is a critical need
for investment in robust digital infrastructure, especially in underserved and rural areas, to ensure
that automation can be deployed effectively and inclusively across the country. Financial
institutions and digital service providers must focus on the development of automation tools that
are not only technically advanced but also tailored to the needs and capabilities of small business
owners, including those with low levels of digital literacy.
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Additionally, more advanced and responsive fraud detection systems should be integrated into
lending platforms, utilizing real-time data and adaptive algorithms. Compliance mechanisms
should be streamlined through automated reporting tools that align with evolving regulatory
requirements. The importance of ethical AI practices cannot be overstated; transparency in
algorithmic decision-making and fairness in credit scoring must be actively enforced by regulatory
bodies to prevent digital discrimination and build trust.
Another vital area for intervention is capacity building. Financial institutions should prioritize staff
training to ensure that employees can interpret and manage the output of automated systems, while
borrowers should be supported through financial literacy programs and digital onboarding
assistance. Furthermore, open banking and safe data-sharing frameworks should be encouraged to
expand access to reliable credit history and improve the accuracy of credit assessments.
Finally, the future deployment of automation should extend beyond loan approval and include
functionalities for post-disbursement services, such as repayment monitoring and digital loan
restructuring, especially during emergencies. Ongoing monitoring and longitudinal impact
evaluations will also be necessary to measure the sustained effectiveness of automation in the
evolving financial landscape of Nigeria.
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