# The intelligent prediction and assessment of financial information risk in the cloud computing model

arXiv: https://arxiv.org/abs/2404.09322 · HTML: https://mlsanity.com/paper/2404.09322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation](https://mlsanity.com/paper/2401.06167/) (2024)
- [Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management](https://mlsanity.com/paper/2402.17216/) (2024)
- [ViTCN: Vision Transformer Contrastive Network For Reasoning](https://mlsanity.com/paper/2403.09962/) (2024)
- [Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization](https://mlsanity.com/paper/2403.07905/) (2024)
- [Comprehensive Implementation of TextCNN for Enhanced Collaboration between Natural Language Processing and System Recommendation](https://mlsanity.com/paper/2403.09718/) (2024)
- [Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning](https://mlsanity.com/paper/2403.13619/) (2024)

## Abstract (arXiv)

Cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user. This report explores the intersection of cloud computing and financial information processing, identifying risks and challenges faced by financial institutions in adopting cloud technology. It discusses the need for intelligent solutions to enhance data processing efficiency and accuracy while addressing security and privacy concerns. Drawing on regulatory frameworks, the report proposes policy recommendations to mitigate concentration risks associated with cloud computing in the financial industry. By combining intelligent forecasting and evaluation technologies with cloud computing models, the study aims to provide effective solutions for financial data processing and management, facilitating the industry's transition towards digital transformation.