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All Journal Asian Journal of Science, Technology, Engineering, and Art ALSYSTECH Journal of Education Technology Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics International Journal of Education, Management, and Technology
Akinnagbe, Olayiwola Blessing
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Human-AI Collaboration: Enhancing Productivity and Decision-Making Akinnagbe, Olayiwola Blessing
International Journal of Education, Management, and Technology Vol 2 No 3 (2024): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v2i3.4209

Abstract

The integration of Artificial Intelligence (AI) into various sectors has catalyzed significant improvements in productivity and decision-making. This paper explores the collaborative potential between humans and AI, focusing on how this synergy can enhance both operational efficiency and decision-making accuracy. While AI excels in processing vast amounts of data and automating repetitive tasks, human capabilities in creativity, intuition, and emotional intelligence complement AI systems, enabling more nuanced and informed decisions. Through a comprehensive review of existing literature, case studies, and real-world applications, the paper examines how AI tools, such as predictive analytics, machine learning, and cognitive computing, support human decision-makers in fields such as healthcare, finance, and business. Despite the clear benefits, challenges persist, including technical integration issues, resistance to AI adoption, and ethical concerns related to bias and transparency. This paper proposes a framework for optimizing human-AI collaboration, emphasizing complementary roles and the development of hybrid intelligence systems. It concludes by identifying future research directions and policy implications, aimed at fostering more effective and ethical human-AI partnerships in the workplace.
The Impact of Machine Learning on Fraud Detection in Digital Payment Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 2 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i2.4900

Abstract

The rapid adoption of digital payment systems has revolutionized financial transactions, but it has also introduced significant challenges in combating fraud. Traditional rule-based fraud detection methods are increasingly inadequate against sophisticated and evolving fraud schemes. This research explores the transformative impact of machine learning (ML) on fraud detection in digital payments. By leveraging advanced ML techniques such as supervised learning, unsupervised learning, and deep learning, financial institutions and payment platforms can analyze vast amounts of transaction data in real-time, identify complex patterns, and adapt to emerging threats. Case studies from industry leaders like PayPal, Stripe, and Mastercard demonstrate the effectiveness of ML in reducing false positives, improving detection accuracy, and enhancing scalability. However, challenges such as data quality, model interpretability, and adversarial attacks remain critical concerns. This study highlights the benefits, limitations, and future trends of ML in fraud detection, emphasizing its potential to create a more secure and resilient digital payment ecosystem. As fraudsters continue to innovate, the integration of machine learning with emerging technologies like explainable AI (XAI) and blockchain promises to further strengthen fraud prevention efforts, ensuring the safety and trust of digital payment systems worldwide.
The Future of Artificial Intelligence: Trends and Predictions Akinnagbe, Olayiwola Blessing
Mikailalsys Journal of Advanced Engineering International Vol 1 No 3 (2024): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v1i3.4125

Abstract

Artificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.
A Comparative Study of AI-Powered Virtual Assistants in Banking: Features, Benefits, and Challenges Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi
ALSYSTECH Journal of Education Technology Vol 3 No 2 (2025): ALSYSTECH Journal of Education Technology
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/alsystech.v3i2.5191

Abstract

This study examines the adoption of AI-powered virtual assistants in Nigerian banking, focusing on their features, benefits, and challenges. Through a comparative analysis of selected banks, including GTBank, Zenith Bank, and Access Bank, the research highlights key features such as 24/7 customer support, multilingual capabilities, and transaction processing. Benefits include cost reduction, improved customer service, and operational efficiency for banks, as well as convenience and personalized services for customers. However, challenges such as technical issues, low digital literacy, and regulatory compliance hinder widespread adoption. The study concludes with recommendations for stakeholders to enhance the effectiveness of AI-powered virtual assistants, fostering financial inclusion and digital transformation in Nigeria.
The Impact of Artificial Intelligence on Risk Management in Banking and Finance Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi; Adanna, Arinze Betsy
Mikailalsys Journal of Advanced Engineering International Vol 2 No 2 (2025): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v2i2.5195

Abstract

This research explores the transformative role of Artificial Intelligence (AI) in risk management within the banking and finance sector. It examines how AI technologies such as machine learning, natural language processing, and predictive analytics are enhancing risk assessment, fraud detection, and regulatory compliance. The study also highlights challenges such as data privacy, algorithmic bias, and the need for skilled professionals. The findings suggest that AI is revolutionizing risk management but requires careful implementation to mitigate associated risks.
Developing an AI-Driven Predictive Model for Stock Market Forecasting in the Banking Sector Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi; Adanna, Arinze Betsy
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 2 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i2.5197

Abstract

This study develops an AI-driven predictive model for stock market forecasting in the banking sector, using LSTM, Random Forest, and Linear Regression. Historical stock prices, macroeconomic indicators, and banking sector metrics were analyzed, with data preprocessing techniques applied to enhance accuracy. Model performance was evaluated using MAE, RMSE, and R², with LSTM achieving the best results (R² = 0.92). Findings suggest AI models can improve investment decisions, trading strategies, and risk management. Future research should explore real-time data integration, sentiment analysis, and hybrid AI models for enhanced forecasting accuracy.
Co-Authors Adanna, Arinze Betsy Akintayo, Taiwo Abdulahi