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

TL;DR

This paper investigates the use of deep learning and big data algorithms to improve financial risk behavior prediction accuracy, demonstrating significant enhancements over traditional methods through experimental validation on real datasets.

Contribution

It introduces a deep learning-based big data risk prediction framework tailored for financial markets, showing its effectiveness and discussing application challenges.

Findings

Significant improvement in prediction accuracy.

Effective framework validated on actual financial data.

Discussion of challenges and future research directions.

Abstract

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.