The Role of Data Science in Predictive Cyber Threat Intelligence: A Case Study Analysis of Cybersecurity and Ethical Considerations in Nigerian Financial Institutions.

Abstract

As cyber threats evolve in scale and sophistication, financial institutions are increasingly leveraging data science techniques to enhance cyber threat intelligence through predictive analytics. This study explores the role of data science in predicting and mitigating cyberattacks within selected Nigerian financial institutions, with a specific focus on the accompanying cybersecurity strategies and ethical considerations. Using a case study methodology, the research draws on secondary data from institutional reports, regulatory publications, and relevant policy documents issued by the Central Bank of Nigeria (CBN), Nigeria Inter-Bank Settlement System (NIBSS), and the Nigeria Data Protection Commission (NDPC). The findings reveal that machine learning models and behavioural analytics are actively employed to detect anomalies, assess risks, and prevent fraud in digital financial systems. However, the study also identifies critical ethical challenges, including the lack of transparency in algorithmic decisions, inadequate user consent mechanisms, and potential breaches of privacy in the deployment of AI-powered surveillance tools. Furthermore, compliance with the Nigeria Data Protection Act (NDPA 2023) and related data governance frameworks remains inconsistent across institutions. This research highlights the need for an integrated ethical and technical framework to guide the responsible use of data science in cybersecurity. It calls for improved regulatory oversight, institution-level ethical protocols, and the adoption of privacy-preserving technologies to ensure both security and trust in Nigeria’s digital financial ecosystem.