2026· International Journal of Latest Technology in Engineering, Management & Applied Science· Vol 15, pp. 1047-1076· 0 citations
TL;DR
The findings showed that AI could strengthen fraud detection, risk-based supervision, early-warning systems, regulatory-reporting analysis and consumer monitoring, however, poor data quality, algorithmic bias, limited explainability, privacy and cybersecurity risks, skills shortages and vendor dependence constrained responsible adoption.
Abstract
Artificial intelligence (AI) is increasingly used in financial services and supervision, yet financial institutions and FinTech firms are adopting it faster than many African regulators can effectively oversee it. This study examined the applications, opportunities, risks and regulatory conditions associated with AI-enabled financial-sector supervision in Africa. It combined a systematic literature review with comparative regulatory analysis covering publications issued between January 2020 and July 2026. Evidence was retrieved from Scopus, Web of Science, SSRN, the IMF, World Bank, Bank for International Settlements, African Development Bank and relevant African regulatory authorities. Following PRISMA procedures, 565 records were identified, 141 duplicates were removed, and 424 records were screened. After full-text and quality assessments, 56 publications were included in the final thematic synthesis. Regulatory arrangements in Nigeria, Ghana, Kenya and South Africa were compared across AI policy, SupTech initiatives, data protection, cybersecurity, consumer protection, regulatory sandboxes, institutional capacity and accountability. The findings showed that AI could strengthen fraud detection, risk-based supervision, early-warning systems, regulatory-reporting analysis and consumer monitoring. However, poor data quality, algorithmic bias, limited explainability, privacy and cybersecurity risks, skills shortages and vendor dependence constrained responsible adoption. South Africa demonstrated the strongest disclosed financial-sector AI readiness, while Kenya had the clearest national AI strategy. Nigeria and Ghana had important digital-finance and cybersecurity foundations but limited publicly documented AI-enabled supervisory applications. The study developed an African Responsible AI–Financial Supervision Framework comprising six connected pillars and a phased adoption cycle. It recommends proportionate implementation, common model-validation standards, stronger inter-agency cooperation, shared regional infrastructure and independent oversight.
This study investigates the impact of artificial intelligence (AI) adoption on financial risk management performance across 142 banking and financial institutions in five North African countries—Morocco, Egypt, Tunisia, Algeria, and Libya—over 2015–2024. Despite the global surge in AI-driven financial research, the MENA region remains virtually absent from the empirical literature, and existing studies rarely examine the institutional conditions that mediate AI's performance effects in structurally constrained emerging markets. Drawing on an integrated theoretical framework combining the Diffusion of Innovation theory [41], the Technology Acceptance Model [19], and Agency Theory [30], the research adopts a hypothetico-deductive approach implemented through a rigorous seven-step multivariate Ordinary Least Squares (OLS) regression protocol. Five research hypotheses are tested examining the joint effects of AI adoption, data quality, risk governance maturity, regulatory compliance, and institution size on a composite risk management performance score. All classical OLS assumptions are formally verified—anchored in data quality, governance maturity, and regulatory compliance—as the primary determinants of AI-driven risk performance in emerging markets, offering actionable insights for regulators, policymakers, and financial institutions across the MENA region. Results confirm a dominant and statistically significant positive impact of AI adoption on financial risk management performance, mediated by data quality and governance maturity, and amplified by the regulatory framework. Institution size yields no significant direct effect. The model achieves substantial explanatory power, confirming the robustness of the specification. These findings introduce the AI Performance Enabling Ecosystem (APEE) framework—anchored in data quality, governance maturity, and regulatory compliance—as the primary determinants of AI-driven risk performance in emerging markets, offering actionable insights for regulators, policymakers, and financial institutions across the MENA region.
Artificial intelligence (AI) is increasingly embedded in digital banking, yet evidence concerning its actual adoption, operational value, and governance implications in Nigeria remains fragmented. This study provides a PRISMA 2020-guided systematic literature review to examine the emerging opportunities and associated risks of AI-enabled digital banking in Nigeria. Scopus, Web of Science, IEEE Xplore, and Google Scholar were searched for English-language publications issued between 2018 and 2026, supplemented by selected policy and organisational documents. From 3,583 identified records, 1,258 were screened, 236 full texts were assessed, and 89 records were retained. Evidence was appraised using source-sensitive criteria derived from the Mixed Methods Appraisal Tool, AMSTAR 2, and AACODS, and was synthesised through thematic, temporal, technology-use-case, and adoption-maturity analyses. The evidence indicates that AI adoption in Nigerian banking is accelerating but remains uneven and predominantly function-specific. Fraud detection, cybersecurity, customer-service automation, and predictive analytics show the clearest operational uptake, whereas explainable credit scoring, generative AI, and enterprise-wide integration remain emergent. Benefits relating to efficiency, financial inclusion, personalisation, and risk detection are inseparable from data-protection, bias, cybersecurity, model-risk, skills, and infrastructure constraints. The review contributes a Nigeria-specific socio-technical synthesis that links AI capabilities, institutional readiness, adoption maturity, and regulatory safeguards. Sustainable deployment requires privacy-by-design, model validation, human oversight, interoperable digital infrastructure, and coordinated supervision by banking, data-protection, and technology regulators.
O. Aju· JOURNAL OF APPLIED INFORMATI...· 0 citations
The swift intertwining of smart technologies and digital financial services is transforming the design of modern financial systems and opening up new prospects in the inclusive and sustainable growth of economy. This is a conceptual research project looking into ways in which the Artificial Intelligence (AI) usage can be converted into wider finance involvement by offering technology-enabled financial solutions. The foundation of this study is a systematic review and synthesis of 20 high-quality scholarly publications related to the study published in 2022-2026, including two conceptual studies, two empirical studies, four systematic literature reviews, two bibliometric analysis, two cross-country studies, and two PLS-SEM-based studies. The review shows that using AI, such as predictive analytics, alternative credit assessment, intelligent automation, fraud detection, and personalized financial recommendations, can decrease informational and operational barriers and enhance the availability, affordability, accessibility, quality, and usability of financial services. The paper also theorizes the role of digital financial service adoption as an accessing engine whereby AI capabilities can be used to access underserved and financially marginalized individuals. Nonetheless, the results focus on the fact that technological development does not ensure meaningful participation. Digital and financial literacy, infrastructure, affordability, consumer trust, cybersecurity, data privacy, regulatory support, interoperability, and socio-economic conditions play a vital role in delivering inclusive outcomes. The research, therefore, suggests a combined Artificial Intelligence-FinTech-Financial Inclusion Sustainability Framework, placing AI as a technological agent, FinTech implementation as an various mechanisms, and financial inclusion as a channel to sustainable financial systems. The framework offers a conceptually based basis upon which future empirical validation can be made, as well as give implications to financial institution, technology providers, policymakers and regulators who wish to foster accessible, responsible, secure, and sustainable digital finance.
N. Dubey· International Journal of Man...· 0 citations
This study examines the impact of Artificial Intelligence (AI) on financial reporting quality in
Nigerian Deposit Money Banks (DMBs). The rapid advancement of digital technologies has
transformed accounting systems, auditing procedures, and financial reporting practices across
the banking sector. Despite increasing adoption of AI-driven accounting systems, empirical
evidence regarding their influence on financial reporting quality in Nigeria remains limited. This
study adopts a quantitative research design using secondary data obtained from annual reports
of selected Nigerian Deposit Money Banks between 2018 and 2025. The study employs
descriptive statistics, correlation analysis, and multiple regression analysis to investigate the
relationship between AI adoption indicators and financial reporting quality proxies. Findings
reveal that AI adoption significantly improves reporting accuracy, timeliness, transparency, and
audit quality among Nigerian banks. The study also identifies challenges such as cybersecurity
threats, high implementation costs, inadequate technical expertise, and regulatory limitations.
The study concludes that Artificial Intelligence has substantial positive effects on financial
reporting quality in Nigerian Deposit Money Banks. The study recommends increased investment
in
1.0 Introduction
AI infrastructure, regulatory reforms, digital accounting education, and enhanced
cybersecurity frameworks.
Mathias Avendei· INTERNATIONAL JOURNAL OF SOC...· 0 citations
This paper examines how risk governance architecture shapes interactions between artificial intelligence (AI) and environmental, social, and governance (ESG)-oriented sustainability policies in the banking industry. Most current research treats AI as a technological capability that directly affects ESG performance, yet little is known about the governance systems that produce these outcomes. Using PRISMA-guided SLR procedures, we selected 20 studies from 248 initial records identified in the Scopus and Web of Science databases that met the inclusion criteria and conducted a thematic synthesis. The results show that AI is primarily used in ESG disclosure and reporting, credit risk assessment, climate risk analytics, sustainable finance, and responsible AI governance. The literature remains dispersed across theoretical stances, including the resource-based view, stakeholder theory, institutional theory, legitimacy theory, and AI governance literature. Previous research has largely ignored the governance mechanisms that enable successful implementation, focusing instead on the direct implications of AI adoption for ESG-related outcomes. The study proposes an AI–ESG risk governance integrative framework to address this gap. This framework places risk governance architecture at the center of the relationship among AI capabilities, institutional pressures, stakeholder expectations, and ESG-oriented sustainability outcomes. The approach views AI as a strategic capacity integrated into enterprise-wide risk governance systems rather than merely a technical or compliance tool. The results indicate that strong governance arrangements, such as model governance, accountability frameworks, board supervision, and alignment with organizational risk appetite, are necessary for successfully deploying AI-enabled ESG. By offering an integrative theoretical framework and practical insights for banking organizations seeking to improve sustainability performance and long-term resilience through responsible AI use, this study contributes to the growing body of AI-ESG literature.
Rini Marlina, Rosa Christiana Esti Noor Sumaryanti, Poltak Maruli John Liberty Hutagaol· Asian Management and Busines...· 0 citations
Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Yun-Dong Wu, Wei-Jian Kong· Artificial Intelligence in E...· 0 citations
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