Olanrewaju Ogundojutimi

IGLOBAL, Information Technology, Graduate Student

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I am a cybersecurity and fraud risk analyst with a strong academic and industry background in digital infrastructure protection, governance, and regulatory compliance. I hold a Master’s degree in Cybersecurity from Washington University of Science and Technology and actively engage in research and development at the intersection of artificial intelligence, digital risk modeling, and compliance automation.

My work focuses on applying predictive analytics, machine learning, and behavioral intelligence to cybersecurity governance—particularly within healthcare, financial, and critical infrastructure systems. I have authored and co-authored peer-reviewed papers on AI-enabled cyber risk prediction, SOC 2 audit automation, and fraud detection frameworks.

Currently serving as a Fraud Continuous Improvement Analyst at Digital Federal Credit Union (DCU), I lead strategic projects aimed at enhancing fraud mitigation and regulatory readiness through intelligent process documentation and threat modeling.

I am an active member of ISACA, IEEE, and IAFCI, and contribute as a peer reviewer and judge for global technology and cybersecurity innovation awards. I welcome academic collaboration, peer review opportunities, and thought leadership initiatives focused on building proactive, intelligent governance systems in cybersecurity.
Phone: 7747891126
Address: Washington University of Science and Technology (WUST), 2900 Eisenhower Ave, Alexandria, VA 22314

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Papers by Olanrewaju Ogundojutimi

AI-DRIVEN BUSINESS MODELING AND DECISION SUPPORT INTEGRATION FOR CORPORATE GOVERNANCE

AI-Driven Business Modeling and Decision Support Integration for Corporate Governance, 2025

In the evolving landscape of enterprise oversight, traditional corporate governance systems are i... more In the evolving landscape of enterprise oversight, traditional corporate governance systems are increasingly challenged by the complexity and dynamism of modern regulatory, ethical, and operational environments. This paper presents a novel, integrated framework for optimizing corporate governance decisions using machine learning, business process modeling, and explainable artificial intelligence. Data collected from three multinational organizations in Ghana and Nigeria were used to train and evaluate three supervised ML models, Decision Tree, Random Forest, and Neural Network, on governance decision classification tasks. Preprocessing involved imputation, normalization, and stratified data splitting (80/20), followed by model training and performance evaluation using precision, recall, F1-score, and AUC metrics. Experimental results showed that the Random Forest model consistently outperformed others, achieving an AUC of 0.94, precision of 0.91, recall of 0.89, and

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PREDICTING CYBERSECURITY RISK IN HEALTHCARE PHARMACY INFRASTRUCTURES

IAEME Publications (International Association for Engineering and Management Education, 2025

In an era of increasing cyber threats against healthcare institutions, medical pharmacies are eme... more In an era of increasing cyber threats against healthcare institutions, medical pharmacies are emerging as critical yet vulnerable components of the digital health ecosystem. This study presents a comprehensive machine learning-based framework for predicting cybersecurity risk in pharmacy environments using operational, threat, and control-related features. We evaluated the predictive performance of three regression models Linear Regression, Support Vector Regressor (SVR), and Random Forest Regressor, using metrics such as R² score, RMSE, and MAE. Random Forest outperformed all models with an R² of 0.91, RMSE of 0.42, and MAE of 0.28, confirming its superiority in capturing non-linear relationships within pharmacy operations. For binary risk classification, the Random Forest Classifier achieved an AUC of 1.00, with a confusion matrix showing high precision (91.4%) and recall (87.6%). Feature importance analysis revealed that control effectiveness, threat probability, and asset value were the most influential factors affecting cybersecurity risk scores. These

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Capital Budgeting and Financial Investment Appraisal: A Review of Archival Literature

by Moyosoluwa Awodire, Kabiru Agboola, and Olanrewaju Ogundojutimi

American Journal of Multidisciplinary Research in Africa, 2023

Universally, fund raising issues and capital utilitization have been the heartbeat of all financi... more Universally, fund raising issues and capital utilitization have been the heartbeat of all financial managers both in the private and public sector. However, profitable utilization of scarce financial resources by various sectors has become more important as cost of fund raised daily has become increasingly astronomical. Hence, the financial manager must do all that is possible to generate returns that will not only be sufficient to meet the cost of funds but also enough to satisfy the wealth maximization objective of the firm. Raising finance for corporate bodies thus becomes important and of highest import. Thus, the aim of this study is to carry out a detailed review of archival literature on the roles of financial management in capital investment appraisal. In this study, the authors intend to review archival literatures to find out the contributions of financial managers in making good financial decisions and to reveal the impact of capital budgeting techniques on financial projects. This simply will provide the reader full detail information about how the necessary data/materials used in the study was collected. The data used for the research were collected by means of vital and relevant data from secondary sources through the review of some related and available documents such as textbooks, term papers, and business publications like magazines, journals, newspaper, periodicals, published and unpublished works and statistical records. This study found that in other to achieve the desired goals of financial managers in effective decision making senior management of any organization must bear in mind that observation of the doctrine of capital investment appraisal must be obeyed and properly executed in the organization.

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Anatomy of Retail In-Store Analytics in Enhancing Customer Experience: Review of Existing Studies

American Journal of IT and Applied Sciences Research

Today, technology helps retailers integrate various In-Store techniques to understand their custo... more Today, technology helps retailers integrate various In-Store techniques to understand their customers better. In-Store Analytics has been in use in the retail industry for a long time, where retailers administer their Customer Analytics manually. The purpose of this study is to carry out a detailed review of existing literature on concepts of customer experience (CX) and In-Store Analytics. The authors used secondary sources of collecting data obtained from existing researches, and articles published on In-Store Analytics. This study reported that the implementation of In-Store Analytics should meet organization needs such as insight creation, market optimization, CX improvement, category and pricing optimization, performance management, and sales improvement.

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Capital Budgeting and Financial Investment Appraisal: A Review of Archival Literature

by Olanrewaju Ogundojutimi and Kabiru Agboola

American Journal of Multidisciplinary Research in Africa

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