International Journal of Advances in Engineering & Scientific Research
Print ISSN : 2349 –4824
Online ISSN : 2349 –3607
Frequency : Continuous
Current Issue : Volume 11 , Issue 2
2024
Chetna Kakde, Dr. Jayasri Murali Iyengar
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Chetna Kakde Department of Research and Business Analytics and Finance Lexicon Mile, Pune, Wagholi-44111 Co-author: Dr. Jayasri Murali Iyengar |
DOI : https://doi.org/10.53882/IJAESR.2024.1102004 Page No : 45-61
Published Online : 2024-12-10
Download Full Article : PDF Check for Updates
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This dissertation investigates the application of machine learning techniques to enhance the security of financial corporate data in response to the escalating threats posed by cyber attacks and data breaches. By employing a mixed-methods approach, the study analyzes quantitative data on current cyber attack trends and incidents related to financial data security, alongside qualitative insights derived from organizational perceptions and experiences with machine learning applications. The findings reveal that advanced machine learning algorithms, particularly anomaly detection and predictive analytics, significantly improve the identification and prevention of potential security breaches, outperforming traditional security measures. Furthermore, the research highlights a critical gap in the understanding of machine learning's transformative potential within financial institutions, suggesting that reluctance or lack of familiarity with these technologies hinders optimal implementation. This study's implications extend beyond financial corporate data security, offering valuable insights applicable to the healthcare sector, where data integrity and confidentiality are paramount. The integration of machine learning in healthcare data security can enhance patient safety, improve compliance with regulatory mandates, and ultimately foster greater trust in digital health systems. By addressing the intersection of machine learning and data security, this research not only contributes to theoretical discourse but also provides practical recommendations that can shape security protocols and strategies across multiple sectors, thereby reinforcing the importance of adaptive and proactive measures in the face of evolving cyber security challenges. |
