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Addressing Insider Threats: The Human Factor in Cybersecurity for Financial Institutions Hewa Majeed Zangana; Harman Salih Mohammed; Mamo Muhamad Husain
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2686

Financial institutions face persistent cybersecurity threats, with insider threats emerging as a particularly complex challenge due to their human-centric nature. This study aims to examine the human factor in cybersecurity within financial institutions, with a focus on insider threats and strategies to mitigate them. A hybrid research approach was used, combining a systematic literature review (SLR) and qualitative case study analysis to investigate cybersecurity risks, AI-driven solutions, and regulatory compliance. The findings reveal that AI-powered toolsâ€”such as behavioral biometrics, machine learning, and blockchain technologiesâ€”substantially enhance fraud detection and risk management. Real-world implementations in financial institutions demonstrated improved threat response, reduced regulatory penalties, and increased operational efficiency. The study concludes that integrating technological tools with a strong cybersecurity culture can significantly mitigate insider threats.

Banking Cybersecurity: Safeguarding Financial Information in the Digital Era Hewa Majeed Zangana; Harman Salih Mohammed; Mamo Muhamad Husain
Journal of Computers and Digital Business Vol. 4 No. 2 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i2.751

This study explores the escalating cybersecurity challenges in the banking sector and the potential of large language models (LLMs) to enhance digital defense mechanisms. Employing a qualitative methodology that includes a systematic literature review, expert interviews, and case study evaluations, the research investigates the integration of LLMs in cybersecurity operations such as threat detection, automated incident response, and user authentication. The findings reveal that LLMs offer significant advantages in real-time anomaly detection, predictive analytics, and natural language-based security training. However, their adoption is hindered by concerns over algorithmic transparency, data privacy, and the need for specialized technical expertise within financial institutions. A key contribution of this work is the development of an integrated cybersecurity framework that combines AI-driven technologies, blockchain-based transaction security, digital forensic tools, and human-centered security practices. The proposed framework aims to guide financial institutions in implementing adaptive, intelligent cybersecurity strategies aligned with evolving global regulatory standards. This research offers both theoretical insights and practical recommendations for enhancing cyber resilience in digital banking environments. It emphasizes the importance of a multidimensional approach that addresses technical innovation, organizational preparedness, and regulatory compliance. Future studies are encouraged to validate the proposed framework through empirical testing across diverse banking infrastructures.

The Role of Change Control Boards in Ensuring Cybersecurity Compliance for IT Infrastructure Hewa Majeed Zangana; Firas Mahmood Mustafa; Ayaz Khalid Mohammed; Marwan Omar
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 1 (2025)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar

In the dynamic landscape of information technology, maintaining cybersecurity compliance is a paramount concern for organizations. Change Control Boards (CCBs) play a crucial role in this context, serving as a governance mechanism to oversee and manage changes within IT infrastructure. This paper explores the significance of CCBs in ensuring cybersecurity compliance, focusing on their functions, processes, and impact on organizational security posture. Through a comprehensive review of existing literature and case studies, the research highlights how CCBs facilitate risk assessment, enforce policy adherence, and mitigate potential threats arising from changes in the IT environment. The findings underscore the importance of structured change management and suggest best practices for integrating cybersecurity considerations into the CCB workflow. By understanding the role of CCBs, organizations can enhance their ability to safeguard sensitive data and maintain regulatory compliance in an ever-evolving threat landscape.

The Role of Change Control Boards in Ensuring Cybersecurity Compliance for IT Infrastructure Hewa Majeed Zangana; Firas Mahmood Mustafa; Ayaz Khalid Mohammed; Marwan Omar
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 1 (2025)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar

In the dynamic landscape of information technology, maintaining cybersecurity compliance is a paramount concern for organizations. Change Control Boards (CCBs) play a crucial role in this context, serving as a governance mechanism to oversee and manage changes within IT infrastructure. This paper explores the significance of CCBs in ensuring cybersecurity compliance, focusing on their functions, processes, and impact on organizational security posture. Through a comprehensive review of existing literature and case studies, the research highlights how CCBs facilitate risk assessment, enforce policy adherence, and mitigate potential threats arising from changes in the IT environment. The findings underscore the importance of structured change management and suggest best practices for integrating cybersecurity considerations into the CCB workflow. By understanding the role of CCBs, organizations can enhance their ability to safeguard sensitive data and maintain regulatory compliance in an ever-evolving threat landscape.

AI-Driven Fraud Detection in Digital Banking: A Hybrid Approach using Deep Learning and Anomaly Detection Harman Salih Mohammed; Zina Bibo Sallow; Hewa Majeed Zangana
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5757

The rapid digital transformation in the banking sector has introduced new opportunities for efficiency and customer convenience but has also amplified the risks of financial fraud. Traditional fraud detection mechanisms, often reliant on static rule-based systems, struggle to keep pace with the dynamic, evolving nature of fraudulent activities. This paper proposes a novel hybrid framework that integrates deep learning models with anomaly detection techniques to enhance the accuracy, robustness, and adaptability of fraud detection in digital banking. The proposed approach leverages a deep neural network (DNN) architecture trained under supervised learning to capture complex transactional patterns and combines it with autoencoder-based unsupervised anomaly detection to uncover previously unseen fraud strategies. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications and its potential for multi-institutional deployment, enabling secure inter-bank fraud intelligence sharing without compromising data privacy. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications. This work contributes to the growing field of AI-driven financial security by addressing both detection performance and adaptability to emerging fraud behaviors.

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