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AI-Based Security Models for Protecting Financial Data

Author(s) Mahaboobsubani Shaik
Country India
Abstract The ever-increasing dependency on digital financial systems calls for much better security management of data. This article reviews AI-based security models for the protection of financial data by anomaly detection and predictive prevention of threats. It leverages machine learning with deep analytics to detect patterns in such a way that would otherwise have indicated a potential breach in security and proactive strategies of mitigation. It proposes the overall assessment framework necessary to monitor the performance of such AI-driven systems against traditional security mechanisms. The article discusses real-world challenges of deployment, from scalability to regulatory compliance to integration complexity. Besides, it showcases the metrics of security impacts such as detection accuracy, response time, and false positive rate which will be deployed for measuring the effectiveness of those models. The results underscore AI's transformative power in making financial data more secure while highlighting its superiority against conventional approaches in terms of flexibility and predictive capabilities
Keywords AI-powered security models, security of financial data, anomaly detection, predictive threat prevention, machine learning, cyber security, evaluation framework, real-world deployment, security impact metrics, and traditional security systems
Field Computer > Automation / Robotics
Published In Volume 4, Issue 10, October 2023
Published On 2023-10-04
Cite This AI-Based Security Models for Protecting Financial Data - Mahaboobsubani Shaik - IJLRP Volume 4, Issue 10, October 2023. DOI 10.5281/zenodo.14471572
DOI https://doi.org/10.5281/zenodo.14471572
Short DOI https://doi.org/g8vk2q
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