A Secure Cloud-Based Financial Analysis System for Enhancing Monte Carlo Simulations and Deep Belief Network Models Using Bulk Synchronous Parallel Processing

Abstract

Background information: Monte Carlo simulations, Deep Belief Networks (DBNs), and Bulk Synchronous Parallel (BSP) processing are used in the suggested secure cloud-based financial analysis system to increase the effectiveness of risk prediction and financial modeling. The system uses cloud infrastructure for precise financial forecasts to guarantee scalability, security, and high-performance data processing. Computational time is greatly decreased by parallel processing, and data security is preserved by encryption, facilitating sound decision-making in intricate financial contexts.
Methods: The system combines Monte Carlo simulations for forecasting risks, DBNs for identifying patterns, and BSP processing to enhance computational efficiency within a cloud setting. Data that is encrypted is processed over multiple cloud nodes, improving security and scalability. This integration enables simultaneous processing of multiple simulations, thus enhancing the speed and precision of financial analysis.
Objectives: By combining Monte Carlo simulations, DBNs, and BSP processing, this work seeks to improve the efficiency of financial models and provide a safe, cloud-based financial analysis system. The solution is designed to safely manage huge datasets in a cloud environment that is scalable. The system aims to shorten calculation times by utilizing parallel processing, guaranteeing precise financial forecasts, risk assessment, and trustworthy decision-making.
Results: The suggested system performs financial analysis jobs more accurately and efficiently. Strong security and scalability are offered by encrypted data management and parallelized simulations. Performance measures show notable gains in recall, accuracy, and precision over conventional techniques, with BSP processing improving scalability. For crucial financial decision-making, this method facilitates quick and safe data analysis. Conclusion: The suggested system performs financial analysis jobs more accurately and efficiently. Strong security and scalability are offered by encrypted data management and parallelized simulations. Performance measures show notable gains in recall, accuracy, and precision over conventional techniques, with BSP processing improving scalability. For crucial financial decision-making, this method facilitates quick and safe data analysis.