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All Journal Journal of Innovation Information Technology and Application (JINITA) Journal of Computers and Digital Business
Hewa Majeed Zangana
IT Dept., Duhok Technical College, Duhok Polytechnic University, Duhok, Iraq
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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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

Abstract

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 tools—such as behavioral biometrics, machine learning, and blockchain technologies—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

Abstract

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.
Co-Authors Harman Salih Mohammed Harman Salih Mohammed Mamo Muhamad Husain Mamo Muhamad Husain