Anthony Obulor Olisa

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.

Interconnected networks such as supply chains, energy systems, transportation, telecommunications, digital platforms, and healthcare increasingly face disruptions from supply shortages, cyber threats, and sudden demand surges. This study proposes an integrated AI-driven resilience framework designed to help such networks anticipate, withstand, and recover from disruptions. Using publicly available operational time-series data, logistics records, and global disruption datasets, we develop a predictive pipeline that combines impact forecasting, ethical governance assessment, and proactive mitigation strategies. A Long Short-Term Memory (LSTM) model was used to forecast disruption patterns and showed strong predictive accuracy, outperforming traditional ARIMA approaches on normalized time-series data (RMSE = 0.0916; MAPE = 4.2%). To ensure responsible deployment, governance mechanisms were evaluated with AI Fairness 360, revealing notable bias risks that require further refinement. Model transparency was improved using SHAP explanations, which aligned well with operational expectations and supported interpretable decision-making. To test real-world impact, agent-based simulations with 1,000 agents evaluated how predictive insights improve network recovery. Results show significant resilience gains, including faster recovery, higher recovery rates, and about 23% cost savings compared with baseline scenarios. Overall, the framework demonstrates that scalable, desk-based AI methods can strengthen resilience across complex networks while supporting transparent and responsible decision-making for future infrastructure planning.

This research develops and validates autonomous artificial intelligence agents for fault detection and self-healing in smart manufacturing systems within the context of Industry 4.0, addressing critical challenges in operational efficiency and reliability. Motivated by the need to reduce unplanned downtime and enhance production resilience, the study pursues three objectives: designing a robust AI architecture for fault detection, implementing effective self-healing mechanisms, and evaluating performance through rigorous testing. The literature review highlights gaps in temporal-spatial data integration and explainable self-healing, underscoring the potential of spiking neural networks (SNNs) and symbolic rule engines. Adopting a design science research paradigm, the proposed hybrid AI framework integrates SNNs, symbolic reasoning, and Isolation Forest algorithms, supported by industrial IoT sensor networks capturing vibration, thermal, and acoustic data. Validation was conducted through hardware-in-loop simulations, fault injection testing, and field deployments in automotive and electronics manufacturing environments. Results demonstrate a 97.3% fault detection accuracy and 89.4% self-healing recovery rate, reducing mean-time-to-repair by 31.7% and improving overall equipment effectiveness by 6.7 points, offering high annual savings for mid-sized manufacturers. Limitations include a 2.7% error margin in detection accuracy and an 83-second material regeneration latency, which exceed some high-speed production takt times. The research concludes by recommending adoption of the framework for improved manufacturing resilience and suggests future work in transfer learning and energy-efficient algorithms to enhance cross-domain scalability and sustainability.

This study investigates the vulnerabilities of blockchain cryptographic protocols to quantum computing threats and evaluates post-quantum cryptographic models for securing financial data governance. Using data from the NIST Cryptographic Algorithm Vulnerability Dataset, World Bank Financial Cybersecurity Adoption Dataset, and IMF Financial Regulatory Policy Database, the study integrates cryptographic benchmarking, regression modeling, and regulatory compliance scoring. Results indicate that RSA-2048 decryption time decreases from 3.57 minutes at 500 qubits to 2.41 minutes at 10,000 qubits, illustrating the accelerating risk posed by quantum advancements. Financial institutions with cybersecurity budgets exceeding $7 billion report an 80% adoption rate of post-quantum cryptography, whereas fintech firms lag at 45%, underscoring critical disparities in readiness. However, while Kyber-1024 and SPHINCS+-SHA256 enhance security, their computational overhead and scalability challenges pose significant concerns for widespread adoption. Furthermore, the study reveals inconsistent global regulatory compliance, with countries like the U.S., China, and the EU progressing more rapidly than developing economies such as Brazil and India. These disparities hinder a coordinated approach to quantum-resistant blockchain integration. The findings emphasize the need for financial institutions to adopt hybrid cryptographic models that balance computational efficiency and quantum resistance, while governments must enforce standardized compliance frameworks to accelerate global readiness. Investments in scalable, efficient post-quantum solutions are crucial to future-proof financial systems against emerging quantum threats. It is recommended that financial institutions implement hybrid cryptographic models, governments enforce standardized compliance frameworks, and investments in scalable post-quantum solutions be prioritized.

This study explores the enhancement of incident response strategies in the U.S. healthcare sector, leveraging data from the Verizon Data Breach Investigations Report, the HHS Breach Portal, and the MITRE ATT&CK Framework. A quantitative methodology was employed, incorporating descriptive statistics, linear regression, and clustering analysis to assess the state of incident response, the impact of recent cyberattacks, and the effectiveness of advanced technologies. Findings revealed consistent improvements in detection and containment times (5.26% and 5.68% annually, respectively) but showed that healthcare still lags behind cross-industry benchmarks. A strong correlation (R² = 0.946) was observed between breach size and financial losses, with larger breaches incurring severe operational and financial impacts. AI-driven systems outperformed traditional methods, achieving a 93.5% F1 score compared to 81.5% for conventional approaches. Recommendations include investing in AI systems, comprehensive training programs, upgrading legacy systems, and implementing continuous improvement mechanisms to strengthen healthcare cybersecurity resilience.

This study investigates the integration of Artificial Intelligence (AI) and Open Source Intelligence (OSINT) to enhance predictive threat modeling in cybersecurity, addressing the growing complexity and frequency of cyber threats. Integrating AI and OSINT offers transformative potential by enabling organizations to transition from reactive to proactive security measures, a critical need in the evolving digital landscape. Leveraging data from the Twitter Academic API, Common Crawl Dataset, and MITRE ATT&CK Framework, the analysis employed descriptive statistical analysis, logistic regression, and multivariate regression methodologies. Results indicate high data completeness (90.41%) and relevance (81.44%) in OSINT datasets, supporting their suitability for AI model training. Logistic regression demonstrated strong predictive capabilities, achieving 94.98% accuracy, 88.69% precision, and an AUC score of 0.91. However, risks such as data bias (-0.36 coefficient) and adversarial manipulation (-0.33 coefficient) significantly impact predictive performance. The ethical implications of this integration, including concerns about privacy, data fairness, and the potential for misuse, are highlighted as critical considerations for broader adoption. Recommendations include robust preprocessing protocols, advanced adversarial defenses, ethical guidelines, and continuous AI innovation to address these challenges. These findings underscore the potential of AI-OSINT integration while emphasizing the need for ethical and technical safeguards to enhance cybersecurity effectiveness.

This study investigates the transformative role of artificial intelligence (AI) in state-sponsored cyber espionage, focusing on its dual use in offensive and defensive operations. Using data from the MITRE ATT&CK Framework, FireEye APT Groups Database, UNSW-NB15 Intrusion Detection Dataset, and the Cyber Conflict Tracker by CFR, this research applied network graph analysis, multi-criteria decision analysis (MCDA), ensemble classification models, and Difference-in-Differences (DiD) analysis. Results revealed that AI-driven offensive techniques, phishing (degree centrality 0.85), and adaptive malware (betweenness centrality 0.81) significantly enhance operational precision and scalability. Defensively, ensemble classification models achieved up to 95.8% accuracy, highlighting AI's efficacy in intrusion detection. AI regulatory frameworks reduced misattribution rates by 20% and escalation incidents by 10%, demonstrating their critical role in mitigating geopolitical risks. The findings impress AI's transformative potential in advancing cyber operations and shaping international policy and governance. By addressing challenges such as attribution, escalation risks, and ethical dilemmas, this study highlights the necessity for stronger global cooperation and regulatory frameworks to navigate the dual-use nature of AI, providing actionable insights for policymakers, cybersecurity professionals, and researchers, emphasizing the urgency of aligning technological advancements with strategies for enhancing global cybersecurity resilience.

This study investigates the impact of artificial intelligence (AI), robotics, and cloud computing on the future of work, focusing on adoption trends, productivity gains, and ethical challenges. Using a mixed-method approach, data were sourced from the OECD AI Policy Observatory, Pew Research Center, and European Union Open Data Portal. Quantitative analysis included K-Means clustering of industries based on technology adoption, with high adopters like North America and Europe leading in cloud computing (65.87%), and Africa showing stronger AI adoption (63.02%). The analysis also highlighted a 14.5% productivity increase in manufacturing, but a 19.3% risk of job displacement. Sentiment analysis revealed that 75% of public sentiment on algorithmic bias was negative, correlating strongly (0.74) with reported violations. The study affirms the need for improved AI governance and stronger GDPR compliance to address ethical challenges, while prioritizing human-centric strategies that ensure inclusive and sustainable outcomes for the workforce.

This study explores the impact of blockchain technology on aerospace supply chains, focusing on mitigating risks such as supply chain disruptions, counterfeit parts, and regulatory compliance challenges. A mixed-methods approach was employed, combining quantitative and qualitative analysis using data sourced from publicly available databases, including the Federal Aviation Administration (FAA), the Aerospace Industries Association (AIA), the International Air Transport Association (IATA), and reports from blockchain technology providers. Descriptive analysis identified supply chain disruptions (M = 3.74, SD = 1.17) as the most significant risk, followed by counterfeit parts (M = 3.34, SD = 1.20). Structural Equation Modeling (SEM) demonstrated that part authentication and provenance tracking had the strongest impact on improving traceability (β = 0.40, p < 0.001) and transparency (β = 0.38, p < 0.001). A Cost-Benefit Analysis (CBA) revealed a positive return on investment, with cost-benefit ratios up to 2.31. Blockchain adoption was also found to reduce CO₂ emissions by up to 25%, while improving operational efficiency through shorter procurement lead times and lower administrative costs. However, regulatory misalignment and interoperability challenges remain barriers to full adoption. The study concludes that blockchain is a transformative tool for enhancing traceability, security, and sustainability in aerospace supply chains.

This study investigates the vulnerabilities of unmanned aerial vehicles (UAVs) to GPS spoofing and jamming, addressing three key research questions: (1) What are the common techniques used to spoof or jam GPS signals for UAVs? (2) How do these techniques impact UAV performance and safety? (3) What mitigation strategies are most effective in preventing interference? A mixed-methods approach was used, combining a qualitative review of peer-reviewed literature and a quantitative analysis of GPS signal data. Spoofing increased positioning errors to 20.45 meters, while jamming reduced mission completion rates by 40%. Detection models, including Random Forest, SVM, and Neural Networks, were evaluated, with SVM showing a recall of 56.4% for spoofed signals despite lower overall accuracy. Inertial Navigation Systems (INS) and Visual Odometry were most effective in reducing navigation errors by over 90% and showed the highest mission success rates, recovering from interference within 0.81 to 1.28 seconds. These findings highlight the importance of integrating advanced detection methods and resilient systems in GPS-reliant UAV operations.