Enhancing cybersecurity risk management in fintech through advanced analytics and machine learning
1 Deloitte and Touche LLP, Dallas, TX, USA.
2 Reeks Corporate Services, Lagos, Nigeria.
3 University of Potomac, Virginia Campus, USA.
4 Independent Researcher, Lagos, Nigeria.
5 Rome Business School, Estonia, Italy.
Review
International Journal of Frontiers in Science and Technology Research, 2025, 08(01), 001-023.
Article DOI: 10.53294/ijfstr.2025.8.1.0023
Publication history:
Received on 10 December 2024; revised on 18 January 2025; accepted on 21 January 2025
Abstract:
The rapid growth of the fintech sector has amplified the need for robust cybersecurity risk management frameworks to safeguard sensitive financial data and ensure operational continuity. This abstract explores the transformative role of advanced analytics and machine learning (ML) in enhancing cybersecurity for fintech companies. By leveraging these technologies, organizations can build proactive defenses, improve threat detection accuracy, and reduce response times to cyber incidents. Advanced analytics enable fintech companies to process large volumes of real-time data, identifying anomalies and potential vulnerabilities with unparalleled precision. Techniques such as predictive modeling and behavior analysis allow for the early detection of sophisticated threats, including phishing, ransomware, and advanced persistent attacks. Machine learning algorithms enhance these capabilities by continuously learning from evolving cyber threats, adapting to new attack vectors, and optimizing detection mechanisms. Incorporating machine learning into cybersecurity risk management frameworks also facilitates automated responses to identified threats. AI-powered systems can assess the severity of attacks, prioritize remediation efforts, and deploy countermeasures with minimal human intervention, significantly reducing downtime and potential financial losses. Additionally, these systems can generate actionable insights, enabling fintech organizations to strengthen their cybersecurity posture and comply with regulatory requirements. Despite its benefits, implementing advanced analytics and ML in cybersecurity presents challenges, such as the risk of algorithmic biases, high resource demands, and the complexity of integrating these tools into existing systems. Addressing these barriers requires a strategic approach, including robust training datasets, investment in scalable technologies, and collaboration between fintech firms and cybersecurity experts. This investigation underscores the critical role of advanced analytics and machine learning in shaping the future of cybersecurity risk management within the fintech ecosystem. By adopting these technologies, fintech companies can enhance their resilience against cyber threats, protect customer trust, and drive sustainable growth in a rapidly digitizing financial landscape.
Keywords:
Cybersecurity; Fintech; Advanced Analytics; Machine Learning; Threat Detection; Risk Management; Predictive Modeling; Automated Response; Operational Resilience; Regulatory Compliance
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0