Securing Pension Systems with AI-Driven Risk Analytics and Cloud-Native Machine Learning Architectures
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I2P106Keywords:
AI-Driven Risk Analysis, Pension System Security, Cloud-Based Architecture, Machine Learning in Finance, Financial Fraud Detection, Predictive Analytics, Anomaly Detection, Federated Learning, Financial Cybersecurity, Regulatory ComplianceAbstract
Pension systems are vital financial facilities that must be adequately protected against fraud, information threats, and other financial perils for the benefit of the interested parties. Traditional methods of risk assessment are limited, especially in the cybersecurity threats, compliance processes and fraud prevention in a real-time manner. Thus, implementing AI and ML in cloudbased architectures can be an effective solution for improving the security of the pension system, automating the risk analysis of all kinds of threats, and detecting various types of fraud. By availing supervised and unsupervised learning approaches, this paper examines the potential of AI to identify fraudulent activities and evaluate the risks in pension systems. It also discusses architectures that allow scalability. Moreover, it explores current architectures in real-time monitoring of the OS and data encryption enhancement on the cloud. The traditional method of utilizing AI in pension administration focuses on effectively identifying new challenges and employing predictive analytics to prevent or address them before they harm the pension fund adversely. In this paper, real cases and experiments proving the feasibility of using Autoencoders and LSTMs in the identification of suspicious transactions and irregular pension transfers have also been discussed. In addition, some of the issues highlighted in this paper include data privacy issues, interpretability of results, and AI prejudice in generating the decision. We suggest future work based on the following directions: federated learning to train secure AI models and adopting ethical frameworks to improve the model's interpretability and fairness. The significance of the issue, the analyses made, the conclusions drawn, and the measures recommended all suggest that the application of AI in the pension fund and pension management presents both opportunities for enhanced pension security, fraud prevention, and legal compliance in terms of size and complexity in cloud environments
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