I would like to thank the authors for their engaging work entitled “Integrating Machine Learning for Sustaining Cybersecurity in Digital Banks.” This paper addresses a critical and timely topic, and its exploration of machine learning (ML) applications in enhancing digital banking cybersecurity is commendable. However, I have several questions and comments regarding the study. Please note that the following remarks are intended solely to foster constructive discussion and, hopefully, contribute to advancements in the field:
The manuscript provides a broad overview of ML’s potential in addressing cybersecurity challenges within digital banking. However, there are several concerns regarding the robustness and real-world applicability of the findings. While the study highlights the relevance of ML in detecting threats such as phishing, ransomware, and insider threats, it lacks empirical validation or specific case studies to substantiate its claims. The absence of real-world data or practical implementations significantly limits the reliability and transferability of the proposed integration model.
Furthermore, the SWOT analysis, while informative, appears generic and could apply to any industry implementing ML in cybersecurity. The discussion fails to address domain-specific nuances, such as the regulatory and compliance challenges unique to digital banking. Incorporating these aspects would enhance the relevance of the analysis and provide actionable insights for stakeholders.
Citation accuracy and the representation of referenced works also raise concerns. For instance, the manuscript references studies on ML algorithms like support vector machines and random forests for fraud detection but provides no context or critical evaluation of the cited works. This oversight may mislead readers regarding the effectiveness and limitations of these methods in practical settings.
The proposed integration model is intriguing but conceptual in nature, with no supporting empirical data to validate its feasibility. Claims regarding its effectiveness remain speculative without real-world application or simulation results. Additionally, while the authors emphasize the importance of ethical considerations and privacy protection, these discussions remain superficial. Providing concrete strategies or frameworks for addressing these critical issues would significantly strengthen the manuscript.
Lastly, while the paper attempts to consolidate insights from existing literature, several sections are verbose and repetitive, which detracts from the overall clarity and impact of the study. A more focused and concise presentation would improve the readability and effectiveness of the paper.
Looking forward to your thoughts on these points.