Enhancing Flight Operations and Predictive Maintenance using Machine Learning and Generative AI (Published)
This technical article examines how machine learning and generative AI technologies can transform flight operations and maintenance in the airline industry. It explores the implementation of predictive analytics for flight delay forecasting and component failure detection, demonstrating how these technologies enable airlines to shift from reactive to proactive operational models. The article analyzes specific algorithms like XGBoost, LSTM networks, Random Forest, and gradient boosting techniques that have proven effective in aviation applications. It addresses implementation challenges related to data quality, legacy system integration, and organizational change management while providing insights into the return on investment and future technological developments. By leveraging AI-driven predictive strategies, airlines can enhance operational efficiency, improve maintenance practices, reduce unplanned downtime, and ultimately achieve significant cost savings while maintaining safety standards in an increasingly competitive industry.
Keywords: Digital Transformation, Predictive Maintenance, aviation efficiency, flight delay prediction, generative AI, machine learning algorithms
Demystifying Generative AI for Financial Services (Published)
Generative AI has emerged as a transformative force in financial services, revolutionizing operations from customer service to risk management. The technology’s ability to create, analyze, and optimize financial processes has led to significant improvements in operational efficiency, customer experience, and decision-making capabilities. Using architectural frameworks such as GANs, VAEs, and Transformer models, financial institutions are enhancing accuracy in fraud detection, portfolio management, and regulatory compliance. The implementation of these AI solutions, while presenting challenges in data privacy, bias mitigation, and operational risks, offers substantial opportunities for innovation in financial product development and service personalization. As the industry continues to evolve, the strategic adoption of generative AI becomes increasingly crucial for maintaining competitive advantage and meeting evolving customer needs.
Keywords: Financial Innovation, Risk Management, customer experience enhancement, generative AI, machine learning architecture
Synthetic Data for Payment Systems: AI-Powered Privacy-Preserving Testing (Published)
In modern banking, ensuring that new payment systems operate accurately and securely requires extensive testing. However, testing with real-world data introduces privacy risks, and synthetic data offers a promising alternative. This paper explores the potential of Generative AI for producing realistic, privacy‑compliant synthetic transaction data. The proposed approach addresses challenges such as data privacy, diverse dataset creation, and the ability to simulate rare or edge-case scenarios—thus enhancing the robustness of payment systems.
abstract
Keywords: Privacy, Synthetic data, generative AI, machine learning, payment systems
Enhancing Resilience Posture in Banking Security Through Generative AI: Predictive, Proactive, and Adaptive Strategies (Published)
This research explores the transformative potential of generative artificial intelligence in enhancing banking security resilience. Through a mixed-methods approach combining quantitative simulations and qualitative assessments, we demonstrate how generative AI models can significantly improve vulnerability detection, incident response times, and business continuity planning. Our findings indicate a 30% improvement in vulnerability detection and a 45% reduction in recovery times, suggesting that AI-driven approaches represent a paradigm shift in banking security frameworks. The study provides a comprehensive framework for implementing generative AI solutions while addressing practical challenges and ethical considerations.
Keywords: Resilience, adaptive strategies, banking security, generative AI, predictive analytics, vulnerability detection
Navigating the Ethical Dilemma of Generative AI in Higher Educational Institutions in Nigeria using the TOE Framework (Published)
Generative AI tools stand at the threshold of innovation and the erosion of the long-standing values of creativity, critical thinking, authorship, and research in higher education. This research crafted a novel framework from the technology, organization, and environment (TOE) framework to guide higher educational institutions in Nigeria to navigate the ethical dilemma of generative AI. A questionnaire was used to collect data from twelve higher institutions among lecturers, students, and researchers across the six (6) geopolitical zones of Nigeria. The structural equation modeling was used to analyze the data using the SPPS Amos version 23. The results revealed that factors such as perceived risks of generative AI, Curriculum support, institutional policy, and perceived generative AI trends positively impact the need for a generative AI ethical framework in higher educational institutions in Nigeria. Furthermore, the study contributes to the adoption of theory to navigate the ethical dilemma in the use of generative AI tools in higher educational institutions in Nigeria. It also provides some practical implications that suggest the importance of inculcating ethical discussions into the curriculum as part of institutional policy to create awareness and guidance on the use of generative AI.
Keywords: AI ethics, Higher Education, TOE framework., ethical framework, generative AI
AI vs. AI: The Digital Duel Reshaping Fraud Detection (Published)
In the evolving landscape of financial security, a new battlefront has emerged: synthetic identity fraud powered by Generative Artificial Intelligence (GAI). This paper examines the high-stakes digital duel between fraudsters wielding GAI and the adaptive defense mechanisms of financial institutions. The paper explores how GAI-created synthetic identities challenge traditional fraud detection paradigms with convincing backstories, digital footprints, and AI-generated images. These artificial personas’ unprecedented scale and sophistication threaten to overwhelm existing security infrastructures, potentially compromising the integrity of financial systems and identity verification frameworks. Our analysis reveals large-scale synthetic identity campaigns’ far-reaching economic implications and disruptive potential across multiple sectors. It also investigates cutting-edge countermeasures, including adversarial machine learning, real-time anomaly detection, and multi-modal data analysis techniques. As this technological arms race intensifies, the paper concludes by proposing future research directions and emphasizing the critical need for collaborative initiatives to stay ahead in this ever-evolving digital battlefield.
Keywords: Cybersecurity, Fraud Detection, generative AI, machine learning, synthetic identities