Michael Olayinka Gbadebo

The global financial system is being transformed by the integration of digital technologies such as artificial intelligence, big data, blockchain, and cloud computing, reshaping financial services, risk management, and market dynamics. This study examines how digital transformation is reshaping financial value creation and risk operations within contemporary financial systems. Despite rapid digitalisation, existing financial frameworks remain largely grounded in traditional valuation and risk assumptions, creating a gap in understanding the interaction between digital value, emerging risks, and governance. To address this, the study adopts a multi-method quantitative approach, combining fixed effects panel regression, Difference-in-Differences, Vector Autoregression, and Structural Equation Modelling on a balanced panel of eight countries (four developed and four emerging economies) over the period 2020–2024. The results show that digital transformation significantly enhances financial value, with R&D expenditure exerting a strong positive effect (β = 1.736), while digital adoption reduces non-performing loans (β = −0.967) and operational costs (β = −2.483). However, fintech adoption increases cyber risk (β = 0.341) and contributes to systemic financial stress. Structural modelling further reveals that digital value intensifies risk exposure (β = 0.534), whereas governance mitigates it (β = −0.418). The study advances financial theory by integrating value creation, risk dynamics, and governance into a unified framework. It recommends adaptive regulatory frameworks, strengthened institutional governance, and revised valuation models to ensure stability in digitally transformed financial systems.

This study critically evaluates WhatsApp’s security ecosystem through a shared responsibility framework, examining the interaction between platform safeguards, usability, and user accountability. A mixed-methods approach was employed, integrating four open-access cybersecurity datasets with primary data from a structured questionnaire completed by 835 respondents and semi-structured interviews with five members of an alumni WhatsApp group affected by an account breach. Analytical techniques included sentiment–feature mapping using logistic regression, risk distribution profiling with Relative Risk Ratios, exploratory factor analysis with Cronbach’s Alpha, multivariate time-series correlation with Granger causality, and thematic analysis of interview transcripts. Results indicate that two-step verification recorded the strongest positive sentiment and highest odds ratio (OR = 2.34), yet adoption remained low, with fewer than 40 percent of users enabling the feature. Investment fraud accounted for 61 percent of high-loss scam cases, while questionnaire responses revealed persistent engagement in risky behaviours such as joining unverified groups. Qualitative findings further demonstrate that trust dynamics, misunderstanding of security prompts, and limited practical awareness significantly contributed to the observed account breach. Behavioural accountability constructs explained 80 percent of variance in security outcomes; however, only 24 percent of users consistently exhibited high accountability. Platform-level interventions such as key transparency were associated with reductions in banned accounts (r = −0.62, p = 0.04), while spikes in abuse incidents triggered reactive increases in two-step verification adoption. These findings underscore the limitations of encryption-centric security models and highlight the need for improved usability, targeted awareness strategies, and strengthened user accountability. The study provides actionable insights for platform designers, policymakers, and users seeking to enhance digital resilience in encrypted communication environments.

This study investigates policy responses to strengthening cyber resilience in the face of expanding global threats, using U.S.–China cyber dynamics as a reference case. Four open datasets were employed: Verizon Data Breach Investigations Report (DBIR), CISA Known Exploited Vulnerabilities (KEV) Catalogue, ENISA Threat Landscape Reports, and the (ISC)² Cybersecurity Workforce Study to generate quantitative insights that connect technical evidence to policy outcomes. The analysis applied ARIMA modelling for forecasting incident trends, Pareto concentration analysis for identifying critical vulnerability clusters, and a Difference-in-Differences regression to evaluate the effectiveness of the European Union’s NIS2 directive. Findings reveal that state-sponsored cyber incidents have risen sharply from 130 in 2013 to a projected 535 by 2027, with 20% of technologies accounting for over 70% of exploited vulnerabilities. The NIS2 directive demonstrated an 18% reduction in post-policy incidents across the EU compared to continued increases in the United States, underscoring the value of structured regulatory intervention. Additionally, the study emphasises Zero Trust architecture as a cornerstone of modern resilience, highlighting its potential to contain adversarial movement and reduce systemic exposure. The results provide actionable insights for policymakers, regulators, and security leaders seeking evidence-based approaches to improve governance, reporting frameworks, and international coordination. Ultimately, this research advances the understanding of how data-driven policy design anchored in Zero Trust principles and cross-jurisdictional governance, such as NIS2, can reinforce global cyber stability and foster a more resilient digital ecosystem.

The growing complexity and volume of health data have exposed critical limitations in traditional, static data processing systems, creating an urgent need for adaptive AI pipelines that can support scalable, secure, and governance-compliant population health analytics. This study investigates how adaptive AI architectures can enhance decision-making, data security, and regulatory accountability in healthcare systems, particularly within developing country contexts. Four publicly available datasets were analyzed using complementary quantitative techniques: the Medical Expenditure Panel Survey (MEPS) with fixed-effects regression to assess the relationship between adaptive AI adoption and healthcare utilization; the Nigeria Centre for Disease Control (NCDC) weekly epidemiological reports with CUSUM anomaly detection to identify systemic vulnerabilities; the Demographic and Health Survey (DHS 2018) with logistic regression to evaluate predictors of governance awareness; and WHO-derived Nigeria health indicators from Kaggle with Principal Component Analysis (PCA) to reveal latent systemic gaps in health governance and capacity. The results show that adaptive AI adoption significantly reduced healthcare utilization (β = –0.152, p < 0.001), anomaly detection identified unusual patterns in 3 of 10 reporting weeks, and governance awareness was higher among educated and urban populations. PCA revealed governance gaps (42.7 % variance explained) as the most critical systemic weakness. The study concludes with recommendations for embedding governance-by-design, deploying real-time anomaly detection, promoting equity in governance frameworks, and strengthening infrastructure capacity. These findings have broad implications for policymakers seeking to advance trustworthy, transparent, and ethically aligned AI systems in population health management.

This study investigates the social and infrastructural determinants of firearm violence in the United States through a PMESII-informed analytical framework. Drawing from open-access national datasets, including CDC WISQARS, the American Community Survey, County Health Rankings & Roadmaps, and the CDC/ATSDR Social Vulnerability Index, the research analyzes data from 2,914 U.S. counties. Employing multivariate linear regression, principal component analysis with clustering, and interaction term modeling, the study evaluates how systemic variables contribute to firearm homicide rates. The findings highlight that higher educational attainment (β = -0.176, p < .001) and stricter gun laws (β = -0.045, p < .001) are the most significant protective factors. Furthermore, infrastructural strength measured through healthcare access, housing quality, digital connectivity, and civic support shows a strong inverse correlation with firearm mortality. Notably, a compounded risk effect (β = 3.303, p < .001) emerges in counties with both high social vulnerability and weak infrastructure, underscoring the need for integrated interventions. The study advocates for targeted policy reforms that strengthen educational equity, enforce robust firearm regulations, and expand infrastructure in high-risk areas. These findings emphasize the critical importance of multi-dimensional, data-driven strategies in mitigating gun violence and offer an actionable framework for researchers, policymakers, and urban planners addressing public safety through systemic resilience.

This study investigates the application of artificial intelligence (AI) in managing cybersecurity risks associated with third-party vendors in healthcare systems. With third-party breaches accounting for a significant proportion of healthcare data compromises, this research seeks to answer a central question: To what extent does AI reduce the frequency, cost, and impact of vendor-related data breaches in healthcare institutions? Specifically, it evaluates whether AI adoption improves breach detection speed, reduces containment time, and lowers breach-related financial losses. To address these objectives, the study analyzes three reputable datasets: the U.S. Department of Health and Human Services (HHS) OCR Breach Portal (2018–2024), the HIMSS Cybersecurity Survey, and the IBM Cost of a Data Breach Report. Descriptive statistics were used to assess breach frequency and vendor involvement. A chi-square test evaluated the statistical association between AI adoption and breach incidence, while multiple linear regression measured AI’s impact on breach cost, time to detect, and time to contain. The results reveal that organizations using AI reported a significantly lower breach incidence (22.5%) compared to non-adopters (60%). Regression analysis further shows that AI adoption reduces breach costs by $2.84 million, shortens detection time by 24.47 days, and containment time by 20.62 days. These findings support the integration of AI as a strategic tool for real-time risk mitigation and operational resilience. The study recommends regulatory enforcement of AI adoption in third-party risk governance and the inclusion of AI clauses in vendor contracts to strengthen data protection in healthcare.

This study investigates the limitations of traditional fraud detection techniques in digital banking and explores the applicability of AI-powered behavioral biometrics as a next-generation solution for enhancing cybersecurity. Using publicly available datasets, including the PaySim Financial Transactions Dataset, Credit Card Fraud Detection Dataset, and HMOG Dataset, this research applies machine learning models such as Random Forest, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs). These models were evaluated using quantitative metrics including Accuracy, Precision, Recall, F1 Score, and AUC-ROC. The LSTM network demonstrated superior performance, achieving 97.9% accuracy, 95.6% precision, and 93.4% recall, outperforming other models. The results reveal that deep learning frameworks significantly enhance fraud detection efficiency, minimize false positives, and improve prediction accuracy. Furthermore, the use of publicly available datasets enhances the study’s reproducibility and transparency. Ethical considerations related to privacy, user consent, and algorithmic accountability are also discussed, highlighting the importance of responsible AI deployment in digital banking systems. This research aims to address evolving cybersecurity threats by integrating advanced deep learning models with behavioral biometrics for real-time anomaly detection. The findings demonstrate the effectiveness of AI models in accurately detecting complex fraud patterns and propose practical recommendations for integrating such systems within existing digital banking infrastructures. Recommendations include improving algorithmic transparency, establishing ethical guidelines, and investing in infrastructure upgrades to facilitate seamless implementation. This work offers a valuable foundation for future research aimed at developing robust and adaptive fraud detection systems that prioritize both efficiency and ethical compliance.

This study examines the integration of Post-Quantum Cryptography (PQC) and Advanced Encryption Standard (AES) to safeguard financial records against quantum-enabled cyber threats. A quantitative approach was employed using data from the NIST Post-Quantum Cryptography Project Dataset, Google Homomorphic Encryption Benchmark Dataset, Hyperledger Fabric Blockchain Performance Dataset, and World Bank Financial Stability Indicators Dataset. Multi-Criteria Decision Analysis (MCDA) with the Analytic Hierarchy Process (AHP) assessed cryptographic agility, while Multiple Linear Regression (MLR) analyzed encryption efficiency. Results indicate that CRYSTALS-Kyber achieves the highest agility score (8.35), making it the most adaptable PQC algorithm for financial institutions. Blockchain-based key exchange mechanisms integrating PQC reduced transaction finality time by 25%, enhancing security and efficiency. A post-quantum cyber breach could result in a 3.2% GDP loss and $150 billion in cybercrime costs. Financial institutions must prioritize PQC adoption, enforce regulatory standardization, deploy blockchain-based PQC key exchange, and invest in cryptographic agility to mitigate quantum security risks.

This study examines the complex motivations behind terrorism and evaluates the effectiveness of international counterterrorism efforts using data from the Global Terrorism Database (GTD), World Values Survey (WVS), and World Bank Governance Indicators. Logistic regression and cluster analysis reveal that no socio-political factors—such as trust in government, religious values, or political ideology—significantly predict religiously motivated terrorism, though patterns emerge in political ideology and governance. Interrupted Time Series (ITS) and Difference-in-Differences (DiD) analyses indicate a post-9/11 surge in international counterterrorism collaborations, while domestic U.S. peace stability shows limited change. Panel data analysis highlights governance quality and economic stability as key factors in reducing terrorism, with economic stability emerging as the strongest predictor (p < 0.001). Based on these findings, the study recommends prioritizing governance reforms, integrating economic development into counterterrorism strategies, expanding efforts beyond military interventions, and strengthening intelligence-sharing mechanisms to enhance global counterterrorism effectiveness. Keywords: Cyber, Terrorism, Counterterrorism, Governance, Economic Stability, Global Peace Index, Data Privacy.

This study investigates the regulatory and ethical dimensions of Open Source Intelligence (OSINT) and deepfake technologies, analyzing their impact on public trust, privacy, and societal stability. Using data from the Global Dataset of Events, Location, and Tone (GDELT), sentiment analysis and time-series regression identified a significant decline in public sentiment (β = -0.23, p = 0.01) and societal stability due to deepfake incidents and OSINT misuse. The Deepfake Detection Challenge Dataset (DFDC) was analyzed using machine learning models, with neural networks achieving the highest accuracy (92%) and precision (91%). Regulatory frameworks were evaluated using the OECD database, where enforcement capacity demonstrated the strongest impact on reducing misuse cases (β = -0.42, p = 0.002). Recommendations include the establishment of globally coordinated regulatory frameworks, public awareness campaigns, investment in advanced detection systems, and ethical integration of AI into OSINT practices.