# Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms

arXiv: https://arxiv.org/abs/2410.19394 · HTML: https://mlsanity.com/paper/2410.19394/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 25 Oct 2024.

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

- [CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset](https://mlsanity.com/paper/2406.13113/) (2024)
- [Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function](https://mlsanity.com/paper/2408.11839/) (2024)
- [Twitter Sentiment Analysis of Covid Vacciness](https://mlsanity.com/paper/2311.04479/) (2023)
- [TFWT: Tabular Feature Weighting with Transformer](https://mlsanity.com/paper/2405.08403/) (2024)
- [Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment](https://mlsanity.com/paper/2404.12634/) (2024)
- [Research on Heterogeneous Computation Resource Allocation based on Data-driven Method](https://mlsanity.com/paper/2408.05671/) (2024)

## Built on by (most cited first, 2 of 2)

- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning](https://mlsanity.com/paper/2412.10567/) (2024)

## Abstract (arXiv)

As the complexity and dynamism of financial markets continue to grow, traditional financial risk prediction methods increasingly struggle to handle large datasets and intricate behavior patterns. This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction. First, the application and advantages of deep learning and big data algorithms in the financial field are analyzed. Then, a deep learning-based big data risk prediction framework is designed and experimentally validated on actual financial datasets. The experimental results show that this method significantly improves the accuracy of financial risk behavior prediction and provides valuable support for risk management in financial institutions. Challenges in the application of deep learning are also discussed, along with potential directions for future research.